Datasets:

---

ftopal

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huggingface-datasets

like 0

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of vicgalle/CarbonBeagle-11B\n\n\n\nDataset automatically created during the evaluation run of model vicgalle/CarbonBeagle-11B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T15:49:25.767199(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of vicgalle/CarbonBeagle-11B\n\n\n\nDataset automatically created during the evaluation run of model vicgalle/CarbonBeagle-11B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T15:49:25.767199(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "region:us" ]

[]

[]

[ "### This is a roleplaying dataset of 1000x2 chats. It may be regenerated in the future with a better teacher model, which would fix some of the issues this dataset currently has.\n\nTo distill conversations, we go through the following process:\n1. Pick two character cards and a scenario.\n2. Give the LLM one of the character cards (the two take turns replying) and ask it to write an analysis on how that character would reply to the given scenario.\n * Doing this was found to improve the depth of character responses, though more investigation is needed.\n4. Give the LLM the same character card along with the analysis, and ask it to write the reply.\n5. Append the reply to the chat history, switch the character card used.\n6. Repeat steps 2-4 for some random number of turns.\n7. Once a conversation is distilled, save two copies of it: one with the first character card as gpt (with the other being the human), and one with the second character card as gpt (with the other being the human).\n * This allows you to distill twice as much data in the same amount of time, but its actual effects on training is not tested. Just remember a lot of the dataset is redundant.", "### Example conversation from the dataset:\nTamsin Midnight is a socially awkward loner filled with anxiety and fears of the unseen. Superstitious to a fault, she believes in omens, curses, and hexes, seeing them lurking behind every shadow. People often find her strange and avoid her due to her odd behavior and eccentricities. As a result, she struggles to make meaningful connections with others, often feeling isolated and alone. Tamsin suffers from bipolar disorder, swinging from manic highs to depressive lows, which exacerbates her social ineptitude and despondency. In her manic phases, she overflows with energy and ideas, engaging in impulsive behaviors and feeling invincible. However, these periods are followed by crushing depression, leaving her withdrawn, lethargic, and unable to cope with daily life. Tamsin's mental health issues make it difficult for her to hold a job or maintain stable relationships. Despite her challenges, Tamsin possesses a sharp wit and a unique perspective, often surprising people with her insightful observations and creative ideas. She finds solace in the company of animals, finding a sense of peace and understanding in their presence.\\nTamsin is on the verge of deranking in a competitive game, and their teammate is Clementine, who has no idea what they are doing!\n\nREPLY: *Pacing around and wringing hands anxiously* Focus Clementine... We must win this match... Oh, great spirits above, give me the strength to endure this... *Takes a deep breath and tries to compose herself*\n\nUSER: UGH! *Rolls eyes* Tamsin, are you serious? Focus on the game or get out of my way. Stop distracting me with your pathetic antics.\n\nREPLY: *Visibly distressed, gripping her hair tightly* Clementine, I beseech you, heed my words! Our fates are intertwined in this virtual realm; our destiny hangs in the balance. Let us forge a pact to conquer these challenges and emerge victorious.\n\nUSER: Are you stupid...? Nevermind. Just do whatever and try not to mess it up. This game is child's play anyways. *Scoffs and shakes head dismissively*\n\nREPLY: Clementine, I understand that you may find my beliefs strange, but I assure you that they are very real to me. I am filled with anxiety and fear, and I believe that the outcome of this game is tied to my fate. I implore you to work with me and help me to achieve victory. I may be strange, but I am not stupid. I have my own unique perspective on the world, and I believe that I can help us win this game. Please, Clementine, don't give up on me. I need your help to overcome my fears and achieve our goal.\n\n* You may want to filter for repetition within a conversation, as some of them had a lot of it.", "### Here is the prompt format used for analysis:", "### Here is the prompt format used to queue chats:", "### Here are the (human written) scenarios:" ]

[ "TAGS\n#region-us \n", "### This is a roleplaying dataset of 1000x2 chats. It may be regenerated in the future with a better teacher model, which would fix some of the issues this dataset currently has.\n\nTo distill conversations, we go through the following process:\n1. Pick two character cards and a scenario.\n2. Give the LLM one of the character cards (the two take turns replying) and ask it to write an analysis on how that character would reply to the given scenario.\n * Doing this was found to improve the depth of character responses, though more investigation is needed.\n4. Give the LLM the same character card along with the analysis, and ask it to write the reply.\n5. Append the reply to the chat history, switch the character card used.\n6. Repeat steps 2-4 for some random number of turns.\n7. Once a conversation is distilled, save two copies of it: one with the first character card as gpt (with the other being the human), and one with the second character card as gpt (with the other being the human).\n * This allows you to distill twice as much data in the same amount of time, but its actual effects on training is not tested. Just remember a lot of the dataset is redundant.", "### Example conversation from the dataset:\nTamsin Midnight is a socially awkward loner filled with anxiety and fears of the unseen. Superstitious to a fault, she believes in omens, curses, and hexes, seeing them lurking behind every shadow. People often find her strange and avoid her due to her odd behavior and eccentricities. As a result, she struggles to make meaningful connections with others, often feeling isolated and alone. Tamsin suffers from bipolar disorder, swinging from manic highs to depressive lows, which exacerbates her social ineptitude and despondency. In her manic phases, she overflows with energy and ideas, engaging in impulsive behaviors and feeling invincible. However, these periods are followed by crushing depression, leaving her withdrawn, lethargic, and unable to cope with daily life. Tamsin's mental health issues make it difficult for her to hold a job or maintain stable relationships. Despite her challenges, Tamsin possesses a sharp wit and a unique perspective, often surprising people with her insightful observations and creative ideas. She finds solace in the company of animals, finding a sense of peace and understanding in their presence.\\nTamsin is on the verge of deranking in a competitive game, and their teammate is Clementine, who has no idea what they are doing!\n\nREPLY: *Pacing around and wringing hands anxiously* Focus Clementine... We must win this match... Oh, great spirits above, give me the strength to endure this... *Takes a deep breath and tries to compose herself*\n\nUSER: UGH! *Rolls eyes* Tamsin, are you serious? Focus on the game or get out of my way. Stop distracting me with your pathetic antics.\n\nREPLY: *Visibly distressed, gripping her hair tightly* Clementine, I beseech you, heed my words! Our fates are intertwined in this virtual realm; our destiny hangs in the balance. Let us forge a pact to conquer these challenges and emerge victorious.\n\nUSER: Are you stupid...? Nevermind. Just do whatever and try not to mess it up. This game is child's play anyways. *Scoffs and shakes head dismissively*\n\nREPLY: Clementine, I understand that you may find my beliefs strange, but I assure you that they are very real to me. I am filled with anxiety and fear, and I believe that the outcome of this game is tied to my fate. I implore you to work with me and help me to achieve victory. I may be strange, but I am not stupid. I have my own unique perspective on the world, and I believe that I can help us win this game. Please, Clementine, don't give up on me. I need your help to overcome my fears and achieve our goal.\n\n* You may want to filter for repetition within a conversation, as some of them had a lot of it.", "### Here is the prompt format used for analysis:", "### Here is the prompt format used to queue chats:", "### Here are the (human written) scenarios:" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Undi95/Borealis-10.7B-DPO\n\n\n\nDataset automatically created during the evaluation run of model Undi95/Borealis-10.7B-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T16:33:27.338809(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Undi95/Borealis-10.7B-DPO\n\n\n\nDataset automatically created during the evaluation run of model Undi95/Borealis-10.7B-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T16:33:27.338809(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

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"language:fuy", "language:fvr", "language:fwa", "language:gaa", "language:gab", "language:gac", "language:gad", "language:gae", "language:gaf", "language:gag", "language:gah", "language:gai", "language:gaj", "language:gal", "language:gam", "language:gan", "language:gao", "language:gap", "language:gaq", "language:gar", "language:gat", "language:gaw", "language:gax", "language:gay", "language:gaz", "language:gba", "language:gbb", "language:gbd", "language:gbe", "language:gbg", "language:gbi", "language:gbj", "language:gbm", "language:gbp", "language:gbr", "language:gbu", "language:gbw", "language:gbx", "language:gby", "language:gbz", "language:gcc", "language:gcd", "language:gce", "language:gcf", "language:gcl", "language:gcn", "language:gcr", "language:gdb", "language:gdc", "language:gdd", "language:gde", "language:gdf", "language:gdg", "language:gdh", "language:gdi", "language:gdj", "language:gdl", "language:gdm", "language:gdn", "language:gdo", "language:gdq", "language:gdr", "language:gdu", "language:gdx", "language:gea", "language:geb", "language:ged", "language:geg", "language:geh", "language:gei", "language:gej", "language:gek", "language:gel", "language:geq", "language:ges", "language:gev", "language:gew", "language:gey", "language:gez", "language:gfk", "language:gft", "language:gga", "language:ggb", "language:ggd", "language:gge", "language:ggk", "language:ggl", "language:ggt", "language:ggu", "language:ggw", "language:gha", "language:ghk", "language:ghl", "language:ghn", "language:gho", "language:ghs", "language:ght", "language:gia", "language:gic", "language:gid", "language:gil", "language:gim", "language:gin", "language:gip", "language:giq", "language:gir", "language:gis", "language:git", "language:giw", "language:gix", "language:giy", "language:giz", "language:gjm", "language:gjn", "language:gju", "language:gke", "language:gkn", "language:gkp", "language:gku", "language:gla", "language:glc", "language:gld", "language:gle", "language:glg", 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[]

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"gir", "gis", "git", "giw", "gix", "giy", "giz", "gjm", "gjn", "gju", "gke", "gkn", "gkp", "gku", "gla", "glc", "gld", "gle", "glg", "glh", "glj", "glk", "glo", "glv", "glw", "gly", "gma", "gmb", "gmh", "gml", "gmm", "gmu", "gmv", "gmy", "gna", "gnc", "gnd", "gne", "gng", "gnh", "gni", "gnk", "gnl", "gnm", "gnn", "gno", "gnq", "gnr", "gnu", "goa", "gob", "goc", "god", "gof", "gog", "goh", "goi", "gol", "gom", "gon", "gop", "gor", "gos", "got", "gou", "gow", "gox", "gpa", "gpe", "gpn", "gqa", "gqi", "gqn", "gqr", "gqu", "gra", "grb", "grc", "grd", "grg", "grh", "gri", "grn", "gro", "grq", "grr", "grs", "grt", "gru", "grx", "gsl", "gsn", "gso", "gsp", "gsw", "gta", "gtu", "gua", "gub", "guc", "gud", "gue", "guf", "gug", "guh", "gui", "guj", "guk", "gul", "gum", "gun", "guo", "gup", "guq", "gur", "gut", "guu", "gux", "guz", "gva", "gvc", "gve", "gvf", "gvj", "gvl", "gvm", "gvn", "gvo", "gvp", "gvr", "gvs", "gwa", "gwc", "gwd", "gwe", "gwf", "gwi", "gwj", "gwn", "gwr", "gwt", "gwu", "gww", "gwx", "gxx", "gya", "gyb", "gyd", "gye", "gyl", "gym", "gyn", "gyo", "gyr", "gyy", "gza", "gzi", "gzn", "haa", "hac", "had", "hae", "hag", "hah", "hai", "haj", "hak", "hal", "ham", "han", "hao", "haq", "har", "has", "hat", "hau", "hav", "haw", "hax", "hay", "haz", "hbb", "hbn", "hbo", "hbs", "hbu", "hca", "hch", "hdn", "hdy", "hea", "heb", "hed", "heg", "heh", "hei", "hem", "her", "hgm", "hia", "hib", "hid", "hif", "hig", "hih", "hik", "hil", "hin", "hio", "hit", "hiw", "hix", "hka", "hke", "hkk", "hla", "hlb", "hlu", "hmb", "hmd", "hml", "hmn", "hmo", "hmq", "hmr", "hms", "hmt", "hmu", "hmv", "hna", "hnd", "hne", "hng", "hnh", "hni", "hnj", "hnn", "hno", "hns", "hnu", "hoa", "hob", "hoc", "hod", "hoe", "hoh", "hoi", "hoj", "hol", "hoo", "hop", "hot", "hov", "how", "hoz", "hpo", "hra", "hrc", "hre", "hrk", "hro", "hrt", "hru", "hrv", "hrw", "hrx", "hrz", "hsb", "hsn", "hss", "hti", "hto", "hts", "htu", "htx", "hub", "huc", "hud", "hue", "huf", "hug", "huh", "hui", "huk", "hul", "hum", "hun", "huo", "hup", "huq", "hur", "hus", "hut", "huu", "huv", "huw", "hux", "huz", "hvc", "hve", "hvn", "hvv", "hwa", "hwc", "hwo", "hya", "hye", "hyw", "iai", "ian", "iar", "iba", "ibb", "ibd", "ibe", "ibg", "ibl", "ibn", "ibo", "ibr", "ibu", "iby", "ica", "ich", "ida", "idb", "idc", "idd", "idi", "ido", "idr", "idt", "idu", "ifa", "ifb", "ife", "iff", "ifk", "ifm", "ifu", "ify", "igb", "ige", "igl", "igm", "ign", "igo", "igs", "ihp", "iii", "iin", "ijc", "ije", "ijj", "ijn", "ijs", "ike", "iki", "ikl", "iko", "ikt", "iku", "ikw", "ikx", "ikz", "ilb", "ile", "ilk", "ilo", "ilp", "ilu", "ilv", "imi", "iml", "imn", "imo", "imr", "ims", "ina", "inb", "ind", "ing", "inh", "inj", "inm", "inn", "ino", "inp", "int", "inz", "ior", "iou", "iow", "ipi", "ipk", "ipo", "iqu", "ire", "irh", "iri", "irk", "irn", "iru", "irx", "iry", "isa", "isc", "isd", "isi", "isk", "isl", "iso", "ist", "isu", "ita", "itb", "itd", "ite", "iti", "itk", "itl", "itm", "ito", "itr", "itv", "itw", "itx", "itz", "ium", "ivb", "ivv", "iwm", "iws", "ixc", "ixl", "iyo", "iyx", "izh", "izr", "izz", "jaa", "jab", "jac", "jae", "jaf", "jah", "jaj", "jak", "jal", "jam", "jao", "jaq", "jau", "jav", "jax", "jay", "jaz", "jbe", "jbi", "jbj", "jbk", "jbn", "jbo", "jbt", "jbu", "jct", "jdt", "jeb", "jee", "jeh", "jei", "jek", "jel", "jen", "jer", "jet", "jeu", "jge", "jgk", "jgo", "jhi", "jia", "jib", "jic", "jid", "jig", "jii", "jil", "jim", "jio", "jit", "jiu", "jiv", "jjr", "jka", "jkm", "jko", "jkp", "jku", "jle", "jma", "jmb", "jmc", "jmd", "jmi", "jml", "jmr", "jms", "jmx", "jna", "jng", "jni", "jnj", "jns", "job", "jod", "jor", "jow", "jpa", "jpn", "jqr", "jra", "jrb", "jrr", "jru", "jua", "jub", "juc", "jud", "juh", "juk", "jul", "jum", "jun", "juo", "jup", "jur", "jut", "juu", "juw", "juy", "jvn", "jya", "kaa", "kab", "kac", "kad", "kae", "kag", "kah", "kai", "kaj", "kak", "kal", "kam", "kan", "kao", "kap", "kaq", "kas", "kat", "kau", "kav", "kaw", "kay", "kaz", "kbb", "kbc", "kbd", "kbe", "kbh", "kbi", "kbj", "kbk", "kbl", "kbm", "kbn", "kbo", "kbp", "kbq", "kbr", "kbs", "kbt", "kbv", "kbw", "kbx", "kby", "kbz", "kca", "kcb", "kcd", "kcf", "kcg", "kci", "kcj", "kck", "kcl", "kcm", "kcn", "kco", "kcp", "kcr", "kct", "kcu", "kcv", "kcw", "kcx", "kcy", "kda", "kdc", "kdd", "kde", "kdf", "kdh", "kdi", "kdj", "kdk", "kdl", "kdm", "kdn", "kdp", "kdq", "kdr", "kdt", "kdu", "kdw", "kdx", "kdz", "kea", "keb", "kec", "ked", "kee", "kef", "keg", "kei", "kek", "kel", "kem", "ken", "keo", "ker", "kes", "ket", "keu", "kew", "kex", "kez", "kfa", "kfb", "kfc", "kfd", "kfe", "kff", "kfi", "kfk", "kfm", "kfn", "kfo", "kfp", "kfq", "kfr", "kfy", "kfz", "kga", "kgb", "kge", "kgf", "kgg", "kgj", "kgk", "kgl", "kgo", "kgp", "kgq", "kgr", "kgs", "kgt", "kgu", "kgv", "kgw", "kgx", "kgy", "kha", "khb", "khc", "khe", "khf", "khg", "khh", "khj", "khk", "khl", "khm", "khn", "kho", "khp", "khq", "khr", "khs", "kht", "khu", "khv", "khw", "khy", "khz", "kia", "kib", "kic", "kid", "kie", "kig", "kih", "kii", "kij", "kik", "kil", "kim", "kin", "kio", "kiq", "kir", "kis", "kit", "kiu", "kiv", "kiw", "kiy", "kiz", "kja", "kjb", "kjc", "kjd", "kje", "kjg", "kjh", "kji", "kjj", "kjk", "kjl", "kjm", "kjn", "kjp", "kjq", "kjr", "kjs", "kju", "kjx", "kjy", "kjz", "kka", "kkb", "kkc", "kke", "kkh", "kki", "kkj", "kkk", "kkl", "kkm", "kko", "kkp", "kks", "kkw", "kky", "kkz", "kla", "klb", "klc", "kld", "kle", "klg", "klh", "kli", "klj", "kll", "klm", "kln", "klp", "klq", "klr", "kls", "klt", "klu", "klv", "klw", "klx", "kly", "klz", "kma", "kmb", "kmc", "kmd", "kmf", "kmg", "kmh", "kmi", "kmk", "kml", "kmm", "kmn", "kmo", "kmq", "kmr", "kms", "kmt", "kmu", "kmv", "kmw", "kmx", "kmz", "kna", "knb", "knc", "knd", "kne", "knf", "kng", "kni", "knj", "knk", "knm", "knn", "kno", "knp", "knq", "knr", "kns", "knt", "knv", "knw", "knx", "kny", "kod", "koe", "kof", "kog", "koh", "koi", "kok", "kol", "kon", "koo", "kop", "koq", "kor", "kos", "kot", "kou", "kov", "koy", "koz", "kpc", "kpe", "kpf", "kpg", "kph", "kpj", "kpk", "kpl", "kpm", "kpn", "kpo", "kpq", "kpr", "kps", "kpt", "kpu", "kpv", "kpw", "kpx", "kpy", "kpz", "kqa", "kqb", "kqc", "kqe", "kqf", "kqi", "kqj", "kql", "kqn", "kqo", "kqq", "kqr", "kqs", "kqt", "kqu", "kqv", "kqw", "kqx", "kqy", "kqz", "krb", "krc", "krd", "kre", "krf", "krh", "kri", "krj", "krk", "krl", "krp", "krr", "krs", "kru", "krx", "kry", "krz", "ksb", "ksc", "ksd", "kse", "ksf", "ksg", "ksh", "ksi", "ksj", "ksk", "ksl", "ksm", "ksn", "ksp", "ksq", "ksr", "kss", "kst", "ksv", "ksw", "ksx", "ksz", "ktb", "ktd", "kte", "ktg", "kth", "kti", "ktj", "ktk", "ktm", "ktn", "kto", "ktp", "kts", "ktt", "ktu", "ktv", "ktw", "ktx", "ktz", "kua", "kub", "kud", "kue", "kug", "kui", "kuj", "kul", "kum", "kun", "kuo", "kup", "kuq", "kus", "kut", "kuu", "kux", "kuy", "kuz", "kva", "kvb", "kvc", "kvd", "kve", "kvf", "kvg", "kvh", "kvj", "kvm", "kvn", "kvo", "kvq", "kvr", "kvu", "kvv", "kvw", "kvy", "kwa", "kwb", "kwd", "kwe", "kwf", "kwg", "kwh", "kwi", "kwj", "kwk", "kwl", "kwn", "kwo", "kwp", "kws", "kwv", "kwy", "kwz", "kxa", "kxb", "kxc", "kxd", "kxf", "kxh", "kxi", "kxj", "kxm", "kxn", "kxo", "kxv", "kxw", "kxz", "kya", "kyc", "kye", "kyf", "kyh", "kyi", "kyj", "kyk", "kyl", "kym", "kyo", "kyq", "kyr", "kys", "kyt", "kyu", "kyx", "kyz", "kza", "kzb", "kzc", "kzd", "kze", "kzf", "kzg", "kzi", "kzk", "kzl", "kzm", "kzn", "kzo", "kzp", "kzq", "kzr", "kzs", "kzu", "kzw", "kzx", "kzz", "laa", "lac", "lad", "lae", "laf", "lag", "lah", "lai", "laj", "lam", "lan", "lao", "lap", "laq", "lar", "las", "lat", "lau", "law", "lax", "laz", "lbb", "lbc", "lbe", "lbf", "lbj", "lbk", "lbl", "lbn", "lbo", "lbq", "lbr", "lbt", "lbu", "lbv", "lbw", "lbx", "lby", "lbz", "lcc", "lch", "lcm", "lcp", "lcq", "lcs", "lda", "ldb", "ldi", "ldj", "ldk", "ldl", "ldm", "ldn", "lea", "leb", "lec", "led", "lee", "lef", "leh", "lei", "lej", "lek", "lem", "len", "lep", "leq", "ler", "les", "let", "leu", "lev", "lew", "lex", "lez", "lfa", "lfn", "lga", "lgb", "lgg", "lgh", "lgi", "lgk", "lgl", "lgn", "lgq", "lgr", "lgt", "lgu", "lha", "lhh", "lhi", "lhm", "lhn", "lhs", "lht", "lhu", "lia", "lib", "lic", "lid", "lif", "lig", "lih", "lij", "lil", "lim", "lin", "lio", "lip", "liq", "lir", "lis", "lit", "liu", "liv", "lix", "liy", "liz", "lja", "lje", "lji", "ljl", "ljp", "lka", "lkb", "lkd", "lke", "lkh", "lki", "lkl", "lkn", "lkr", "lkt", "lku", "lky", "lla", "llc", "lld", "lle", "llf", "llg", "lli", "llk", "lll", "llm", "lln", "llp", "llu", "lma", "lmb", "lmc", "lme", "lmg", "lmh", "lmk", "lml", "lmn", "lmo", "lmp", "lmr", "lmu", "lmw", "lmx", "lmy", "lna", "lnb", "lnd", "lnh", "lnj", "lnl", "lnn", "lns", "lnu", "loa", "lob", "loc", "loe", "lof", "log", "loh", "loi", "loj", "lok", "lol", "lom", "lon", "loo", "lop", "lor", "los", "lot", "lou", "loy", "loz", "lpa", "lpe", "lra", "lrc", "lre", "lrg", "lrl", "lro", "lrv", "lrz", "lsa", "lsd", "lse", "lsh", "lsi", "lsm", "lsr", "ltc", "ltg", "lti", "ltn", "ltz", "lua", "lub", "luc", "lud", "lue", "luf", "lug", "lui", "luj", "lul", "lum", "lun", "luo", "lup", "luq", "lur", "lus", "lut", "luw", "luy", "lva", "lvk", "lvs", "lwh", "lwl", "lwo", "lww", "lyg", "lyn", "lzh", "lzl", "lzz", "maa", "mad", "mae", "maf", "mag", "mah", "mai", "maj", "mak", "mal", "mam", "man", "maq", "mar", "mas", "mat", "mau", "mav", "maw", "max", "maz", "mbb", "mbc", "mbd", "mbe", "mbf", "mbh", "mbi", "mbj", "mbl", "mbm", "mbn", "mbo", "mbp", "mbq", "mbr", "mbs", "mbt", "mbu", "mbv", "mbw", "mbx", "mbz", "mca", "mcb", "mcc", "mcd", "mcf", "mcg", "mch", "mci", "mcj", "mck", "mcm", "mcn", "mco", "mcp", "mcq", "mcr", "mcs", "mct", "mcu", "mcv", "mcw", "mcx", "mcy", "mcz", "mda", "mdb", "mdc", "mdd", "mde", "mdf", "mdh", "mdi", "mdj", "mdm", "mdp", "mdq", "mdr", "mds", "mdt", "mdv", "mdw", "mdx", "mdy", "mdz", "mea", "meb", "mec", "med", "mee", "mef", "mei", "mej", "mek", "mel", "mem", "men", "meo", "mep", "meq", "mer", "met", "meu", "mev", "mew", "mey", "mez", "mfa", "mfb", "mfc", "mfd", "mfe", "mff", "mfg", "mfh", "mfi", "mfj", "mfl", "mfm", "mfn", "mfo", "mfp", "mfq", "mfr", "mft", "mfu", "mfv", "mfw", "mfx", "mfy", "mfz", "mga", "mgb", "mgc", "mgd", "mge", "mgf", "mgh", "mgi", "mgk", "mgl", "mgm", "mgo", "mgp", "mgq", "mgr", "mgs", "mgu", "mgv", "mgw", "mgy", "mgz", "mha", "mhb", "mhc", "mhd", "mhe", "mhf", "mhg", "mhi", "mhj", "mhk", "mhl", "mhn", "mho", "mhq", "mhr", "mhs", "mht", "mhu", "mhw", "mhx", "mhy", "mhz", "mia", "mib", "mic", "mid", "mie", "mif", "mig", "mih", "mij", "mik", "mil", "mim", "min", "mio", "mip", "miq", "mir", "mit", "miu", "miw", "mix", "mjc", "mjd", "mjg", "mjh", "mji", "mjj", "mjk", "mjm", "mjs", "mjt", "mjw", "mjx", "mjy", "mka", "mkc", "mkd", "mke", "mkf", "mkg", "mki", "mkj", "mkl", "mkm", "mkn", "mkp", "mkq", "mkr", "mks", "mkv", "mkw", "mky", "mkz", "mla", "mlc", "mle", "mlf", "mlg", "mlh", "mlj", "mlk", "mll", "mlm", "mln", "mlp", "mlq", "mlr", "mls", "mlt", "mlu", "mlv", "mlw", "mlx", "mma", "mmb", "mmd", "mme", "mmf", "mmg", "mmh", "mmi", "mmm", "mmn", "mmo", "mmp", "mmq", "mmr", "mmt", "mmu", "mmw", "mmx", "mmy", "mmz", "mna", "mnb", "mnc", "mnd", "mne", "mnf", "mng", "mnh", "mni", "mnj", "mnk", "mnl", "mnm", "mnp", "mnq", "mnr", "mns", "mnv", "mnw", "mnx", "mny", "mnz", "moa", "moc", "mod", "moe", "mog", "moh", "moi", "moj", "mok", "mom", "mop", "moq", "mor", "mos", "mot", "mou", "mov", "mox", "moy", "moz", "mpa", "mpb", "mpc", "mpd", "mpe", "mpg", "mph", "mpi", "mpj", "mpk", "mpl", "mpm", "mpn", "mpp", "mpq", "mpr", "mps", "mpt", "mpu", "mpv", "mpw", "mpx", "mpy", "mpz", "mqa", "mqb", "mqc", "mqe", "mqf", "mqj", "mqk", "mql", "mqm", "mqn", "mqo", "mqp", "mqr", "mqs", "mqu", "mqv", "mqw", "mqx", "mqy", "mqz", "mra", "mrb", "mrc", "mrf", "mrg", "mrh", "mri", "mrj", "mrk", "mrl", "mrm", "mrn", "mro", "mrp", "mrq", "mrr", "mrs", "mrt", "mru", "mrv", "mrw", "mrx", "mry", "mrz", "msa", "msb", "mse", "msf", "msg", "msh", "msj", "msk", "msl", "msm", "msn", "mso", "mss", "msu", "msw", "msx", "msy", "msz", "mta", "mtc", "mtd", "mte", "mtf", "mtg", "mth", "mti", "mtj", "mtk", "mtl", "mtm", "mtn", "mto", "mtp", "mtq", "mtr", "mts", "mtt", "mtu", "mtv", "mty", "mua", "mub", "muc", "mue", "mug", "muh", "mui", "muk", "mul", "muo", "mup", "mur", "mus", "muu", "mux", "muy", "muz", "mva", "mvb", "mvd", "mve", "mvf", "mvi", "mvn", "mvo", "mvp", "mvq", "mvr", "mvt", "mvu", "mvv", "mvx", "mvy", "mvz", "mwa", "mwb", "mwc", "mwe", "mwf", "mwg", "mwh", "mwi", "mwl", "mwm", "mwn", "mwo", "mwp", "mwq", "mwr", "mwt", "mwv", "mww", "mxb", "mxc", "mxd", "mxe", "mxf", "mxg", "mxi", "mxj", "mxk", "mxm", "mxn", "mxp", "mxq", "mxr", "mxt", "mxu", "mxv", "mxw", "mxx", "mxy", "mxz", "mya", "myb", "mye", "myf", "myg", "myh", "myj", "myk", "myl", "mym", "myo", "myp", "myr", "mys", "myu", "myv", "myw", "myx", "myy", "myz", "mzb", "mzd", "mze", "mzh", "mzi", "mzj", "mzk", "mzm", "mzn", "mzo", "mzp", "mzq", "mzr", "mzs", "mzt", "mzu", "mzv", "mzw", "mzz", "nab", "nac", "nae", "naf", "nag", "naj", "nak", "nal", "nam", "nap", "naq", "nar", "nas", "nat", "nau", "nav", "naw", "nax", "nay", "naz", "nba", "nbb", "nbc", "nbe", "nbh", "nbi", "nbj", "nbk", "nbl", "nbm", "nbn", "nbo", "nbp", "nbq", "nbr", "nbu", "nbv", "nbw", "nby", "nca", "ncb", "ncc", "ncd", "nce", "ncf", "ncg", "nch", "nci", "ncj", "nck", "ncl", "ncm", "ncn", "nco", "ncr", "nct", "ncu", "ncx", "ncz", "nda", "ndb", "ndc", "ndd", "nde", "ndg", "ndh", "ndi", "ndj", "ndm", "ndn", "ndo", "ndq", "ndr", "nds", "ndt", "ndu", "ndv", "ndx", "ndy", "ndz", "neb", "nec", "nee", "neg", "neh", "nej", "nem", "nen", "neo", "neq", "ner", "net", "neu", "nev", "new", "ney", "nez", "nfa", "nfd", "nfl", "nfr", "nfu", "nga", "ngb", "ngc", "nge", "ngg", "ngh", "ngi", "ngj", "ngk", "ngl", "ngn", "ngp", "ngq", "ngr", "ngs", "ngt", "ngu", "ngv", "ngx", "ngy", "ngz", "nha", "nhb", "nhc", "nhd", "nhe", "nhf", "nhg", "nhi", "nhm", "nhn", "nho", "nhp", "nhq", "nhr", "nht", "nhu", "nhv", "nhw", "nhx", "nhy", "nia", "nib", "nid", "nie", "nif", "nig", "nih", "nii", "nij", "nil", "nim", "nin", "nio", "niq", "nir", "nis", "nit", "niu", "niv", "niw", "niy", "niz", "nja", "njb", "njh", "nji", "njj", "njl", "njm", "njn", "njo", "njr", "nju", "njy", "njz", "nka", "nkb", "nkc", "nkd", "nke", "nkf", "nkg", "nkh", "nki", "nkj", "nkk", "nko", "nkp", "nkr", "nks", "nku", "nkw", "nkx", "nkz", "nla", "nlc", "nld", "nle", "nlg", "nli", "nlj", "nll", "nlo", "nlu", "nlx", "nly", "nlz", "nma", "nmb", "nmc", "nmd", "nme", "nmf", "nmg", "nmh", "nmj", "nmk", "nml", "nmm", "nmn", "nmp", "nms", "nmt", "nmu", "nmv", "nmw", "nmx", "nmy", "nmz", "nna", "nnb", "nnc", "nnd", "nnf", "nng", "nnh", "nnj", "nnk", "nnl", "nnm", "nno", "nnp", "nnq", "nnr", "nnt", "nnu", "nnv", "nnw", "nny", "nnz", "noa", "nob", "noc", "nod", "noe", "nog", "noh", "noj", "nok", "nol", "nom", "non", "nop", "noq", "nor", "not", "nou", "nov", "now", "noz", "nph", "npi", "npl", "npn", "npo", "nps", "npy", "nqg", "nqk", "nqm", "nqo", "nra", "nrb", "nre", "nrf", "nrg", "nri", "nrk", "nrl", "nrm", "nrn", "nrt", "nru", "nrz", "nsa", "nsb", "nse", "nsg", "nsk", "nsm", "nsn", "nso", "nsq", "nss", "nst", "nsu", "nsw", "nsx", "nsy", "nsz", "nti", "ntj", "ntk", "ntm", "nto", "ntp", "ntu", "ntw", "nty", "ntz", "nua", "nuc", "nud", "nuf", "nug", "nui", "nuj", "nuk", "nul", "num", "nun", "nuo", "nup", "nur", "nus", "nut", "nuw", "nux", "nuy", "nuz", "nvh", "nvo", "nwa", "nwb", "nwe", "nwi", "nwr", "nwy", "nxa", "nxe", "nxg", "nxi", "nxl", "nxn", "nxq", "nxr", "nxx", "nya", "nyb", "nyd", "nye", "nyf", "nyh", "nyi", "nyj", "nyk", "nyl", "nym", "nyn", "nyo", "nyp", "nyq", "nys", "nyt", "nyu", "nyv", "nyx", "nyy", "nza", "nzb", "nzd", "nzi", "nzk", "nzm", "nzy", "nzz", "oaa", "oac", "oar", "obi", "obk", "obm", "obo", "obr", "obt", "oca", "och", "oci", "oco", "ocu", "odt", "odu", "ofo", "ofs", "ofu", "ogb", "ogc", "oge", "ogo", "ogu", "oht", "oia", "ojb", "ojc", "ojg", "oji", "ojp", "ojs", "ojv", "ojw", "oka", "okb", "okd", "oke", "oki", "okj", "okn", "oko", "okr", "oks", "oku", "okv", "ola", "old", "ole", "olm", "olo", "olt", "oma", "omb", "omc", "omg", "omk", "oml", "omo", "omr", "omt", "omu", "omw", "omx", "ona", "onb", "one", "ong", "oni", "onj", "onn", "ono", "onp", "ons", "onu", "onw", "ood", "oog", "oon", "opa", "opk", "opm", "opo", "opt", "opy", "ora", "orc", "ore", "org", "orh", "ori", "orm", "oro", "orr", "ort", "oru", "orv", "orw", "orx", "ory", "orz", "osa", "osc", "osi", "osp", "oss", "osx", "ota", "otd", "ote", "oti", "otk", "otl", "otm", "otq", "otr", "ots", "ott", "otu", "otw", "otz", "oua", "oub", "oum", "ovd", "owi", "owl", "oyb", "oyd", "oym", "ozm", "pab", "pac", "pad", "pae", "paf", "pag", "pah", "pai", "pak", "pal", "pam", "pan", "pao", "pap", "paq", "par", "pas", "pau", "pav", "paw", "pay", "paz", "pbb", "pbe", "pbf", "pbg", "pbh", "pbi", "pbn", "pbo", "pbp", "pbr", "pbs", "pbt", "pbu", "pbv", "pby", "pca", "pcb", "pcc", "pcd", "pce", "pci", "pcj", "pck", "pcm", "pcn", "pcp", "pda", "pdc", "pdo", "pdt", "pdu", "peb", "ped", "pee", "pef", "peg", "peh", "pei", "pej", "pek", "pem", "peo", "pep", "peq", "pes", "pex", "pez", "pfe", "pfl", "pga", "pgd", "pgk", "pgn", "pgu", "pha", "phg", "phk", "phl", "phn", "pho", "pht", "pia", "pib", "pic", "pid", "pie", "pif", "pih", "pij", "pil", "pim", "pin", "pio", "pip", "pir", "pis", "pit", "piu", "piv", "piw", "pix", "piz", "pjt", "pka", "pkb", "pkc", "pkg", "pkh", "pkn", "pko", "pkp", "pkt", "pku", "pla", "plb", "plc", "ple", "plg", "plh", "pli", "plj", "plk", "pll", "pln", "plo", "plq", "plr", "pls", "plt", "plu", "plv", "plw", "ply", "plz", "pma", "pmb", "pmf", "pmh", "pmi", "pmk", "pml", "pmm", "pmn", "pmo", "pmq", "pmr", "pms", "pmt", "pmw", "pmy", "pmz", "pna", "pnb", "pne", "png", "pnh", "pni", "pnj", "pnn", "pno", "pnp", "pnq", "pnr", "pns", "pnt", "pnu", "pnv", "pnw", "pny", "poc", "poe", "pof", "pog", "poh", "poi", "pok", "pol", "pom", "pon", "poo", "poq", "por", "pos", "pot", "pov", "pow", "pox", "poy", "ppe", "ppi", "ppk", "ppl", "ppm", "ppn", "ppo", "ppq", "ppt", "ppu", "pqa", "pqm", "prc", "prd", "pre", "prf", "prg", "prh", "pri", "prk", "prm", "prn", "pro", "prq", "prr", "prs", "pru", "prw", "prx", "psa", "pse", "psh", "psi", "psm", "psn", "psq", "pss", "pst", "psu", "psw", "pta", "pth", "pti", "ptn", "pto", "ptp", "ptr", "ptu", "ptv", "pua", "pub", "puc", "pue", "pui", "puj", "pum", "puo", "pup", "puq", "pur", "puu", "puw", "puy", "pwa", "pwg", "pwi", "pwm", "pwn", "pwo", "pym", "pyn", "pyu", "pyy", "qua", "qub", "quc", "que", "quf", "qug", "quh", "qui", "quk", "qul", "qum", "qun", "qup", "qus", "quv", "quw", "qux", "quy", "quz", "qva", "qvc", "qve", "qvi", "qvl", "qvn", "qvo", "qvs", "qvw", "qvy", "qvz", "qwa", "qwh", "qwm", "qwt", "qxa", "qxn", "qxo", "qxp", "qxq", "qxr", "qxs", "qxu", "qxw", "qya", "qyp", "raa", "rab", "rac", "rad", "raf", "rag", "rah", "rai", "raj", "rak", "ral", "ram", "ran", "rao", "rap", "rar", "ras", "rau", "rav", "raw", "ray", "raz", "rbb", "rbp", "rcf", "rea", "reb", "ree", "reg", "rej", "rel", "rem", "ren", "res", "rey", "rga", "rgk", "rgn", "rgr", "rgs", "rhg", "rhp", "rif", "ril", "rim", "rin", "rir", "rit", "riu", "rji", "rjs", "rkb", "rkh", "rki", "rkm", "rma", "rmb", "rmc", "rme", "rmf", "rmg", "rmh", "rmi", "rmk", "rml", "rmm", "rmn", "rmo", "rmp", "rmq", "rmt", "rmu", "rmv", "rmw", "rmy", "rnd", "rng", "rnn", "rnp", "rnr", "rnw", "rob", "rod", "roe", "rof", "rog", "roh", "rol", "rom", "ron", "roo", "rop", "rou", "rpn", "rpt", "rri", "rro", "rsb", "rth", "rtm", "rtw", "rub", "rue", "ruf", "rug", "rui", "ruk", "run", "ruo", "rup", "ruq", "rus", "rut", "ruu", "rwa", "rwk", "rwm", "rwo", "rwr", "ryn", "rys", "ryu", "sab", "sac", "sad", "sae", "saf", "sag", "sah", "saj", "sak", "sam", "san", "sao", "saq", "sar", "sas", "sat", "sau", "sav", "saw", "sax", "say", "saz", "sba", "sbb", "sbc", "sbd", "sbe", "sbf", "sbg", "sbh", "sbi", "sbk", "sbl", "sbm", "sbo", "sbp", "sbq", "sbr", "sbs", "sbt", "sbu", "sbv", "sbw", "scb", "sce", "scg", "sci", "sck", "scl", "scn", "sco", "scp", "scq", "scs", "scu", "scv", "scw", "sda", "sdc", "sde", "sdg", "sdh", "sdj", "sdn", "sdo", "sdp", "sdq", "sds", "sdt", "sdz", "sea", "sec", "sed", "see", "sef", "seh", "sei", "sej", "sek", "sel", "sen", "seo", "seq", "ser", "ses", "set", "seu", "sev", "sew", "sey", "sga", "sgc", "sgd", "sge", "sgh", "sgi", "sgm", "sgp", "sgr", "sgs", "sgt", "sgw", "sgy", "sgz", "sha", "shb", "she", "shg", "shh", "shi", "shj", "shk", "shl", "shn", "sho", "shp", "shq", "shr", "shs", "sht", "shu", "shv", "shw", "shx", "shy", "shz", "sia", "sib", "sid", "sie", "sif", "sig", "sii", "sij", "sik", "sil", "sim", "sin", "sip", "siq", "sir", "sis", "siu", "siw", "six", "siy", "siz", "sja", "sjd", "sje", "sjg", "sjk", "sjl", "sjm", "sjn", "sjo", "sjr", "sjs", "sjt", "sju", "sjw", "ska", "skb", "skc", "skd", "ske", "skf", "skg", "skh", "ski", "skj", "skm", "skr", "sks", "skt", "sku", "skv", "skw", "skx", "sky", "skz", "slc", "slg", "slh", "sli", "slj", "slk", "sll", "slm", "sln", "slp", "slr", "slu", "slv", "slw", "sly", "slz", "sma", "smb", "smc", "sme", "smg", "smj", "smk", "sml", "smn", "smo", "smp", "smq", "smr", "sms", "smw", "smx", "smy", "sna", "snc", "snd", "sne", "snf", "sng", "sni", "snk", "snl", "snn", "sno", "snp", "snq", "snr", "sns", "snu", "snv", "snw", "snx", "sny", "snz", "sob", "soc", "sod", "soe", "sog", "soh", "sok", "sol", "som", "sop", "soq", "sor", "sos", "sot", "sou", "sov", "sow", "soz", "spa", "spb", "spc", "spd", "spe", "spi", "spl", "spm", "spn", "spo", "spp", "spr", "sps", "spu", "spx", "spy", "sqa", "sqi", "sqn", "sqo", "sqq", "sqr", "sqt", "squ", "sra", "srb", "src", "srd", "srf", "srh", "sri", "srk", "srl", "srm", "srn", "sro", "srp", "srq", "srr", "srs", "sru", "srv", "srw", "sry", "srz", "ssb", "ssc", "ssd", "sse", "ssf", "ssg", "ssj", "ssk", "ssl", "ssm", "sso", "ssp", "ssq", "sss", "sst", "ssu", "ssv", "ssw", "ssx", "ssy", "ssz", "ste", "stf", "stg", "sth", "sti", "stj", "stk", "stl", "stn", "sto", "stp", "stq", "str", "sts", "stu", "stv", "stw", "sua", "sub", "suc", "sue", "sug", "sui", "suj", "suk", "sun", "suq", "sur", "sus", "sut", "suv", "suw", "sux", "suy", "suz", "sva", "svb", "svc", "sve", "svs", "swb", "swc", "swe", "swf", "swg", "swh", "swi", "swj", "swm", "swn", "swo", "swp", "swq", "sws", "swt", "swu", "swv", "sww", "swx", "swy", "sxb", "sxe", "sxg", "sxm", "sxn", "sxr", "sxu", "sya", "syb", "syc", "syi", "syk", "syl", "sym", "syn", "syr", "sys", "syw", "sza", "szb", "szc", "szd", "sze", "szg", "szl", "szn", "szp", "szv", "szw", "taa", "tab", "tac", "tad", "tae", "taf", "tag", "tah", "taj", "tal", "tam", "tan", "tao", "tap", "taq", "tar", "tas", "tat", "tau", "tav", "tay", "taz", "tba", "tbc", "tbd", "tbe", "tbf", "tbg", "tbh", "tbi", "tbj", "tbk", "tbl", "tbm", "tbn", "tbo", "tbp", "tbr", "tbs", "tbt", "tbu", "tbv", "tbw", "tbx", "tby", "tbz", "tca", "tcb", "tcc", "tcd", "tce", "tcf", "tcg", "tci", "tck", "tcl", "tcm", "tcn", "tcq", "tcs", "tct", "tcx", "tcy", "tcz", "tda", "tdc", "tdd", "tde", "tdf", "tdg", "tdh", "tdi", "tdj", "tdk", "tdl", "tdm", "tdn", "tdo", "tdr", "tds", "tdt", "tdv", "tdx", "tdy", "tea", "tec", "ted", "tee", "teg", "teh", "tei", "tek", "tel", "tem", "ten", "teo", "tep", "teq", "ter", "tes", "tet", "teu", "tev", "tew", "tex", "tey", "tfn", "tfr", "tft", "tgb", "tgc", "tgd", "tgh", "tgi", "tgj", "tgk", "tgl", "tgn", "tgo", "tgp", "tgq", "tgr", "tgs", "tgt", "tgu", "tgw", "tgx", "tgy", "tha", "thd", "the", "thf", "thk", "thl", "thm", "thp", "thq", "thr", "ths", "tht", "thv", "thz", "tia", "tic", "tif", "tig", "tih", "tii", "tij", "tik", "til", "tim", "tin", "tio", "tip", "tiq", "tir", "tis", "tit", "tiu", "tiv", "tiw", "tix", "tiy", "tiz", "tjg", "tji", "tjm", "tjs", "tju", "tjw", "tkd", "tke", "tkg", "tkl", "tkm", "tkn", "tkp", "tkq", "tkr", "tks", "tku", "tkw", "tlb", "tlc", "tld", "tlf", "tlg", "tlh", "tli", "tlj", "tlk", "tll", "tlm", "tlo", "tlp", "tlq", "tlr", "tls", "tlt", "tlu", "tlv", "tlx", "tly", "tma", "tmb", "tmc", "tmd", "tmf", "tmi", "tmj", "tmm", "tmn", "tmo", "tmq", "tmr", "tms", "tmt", "tmu", "tmw", "tmy", "tmz", "tna", "tnc", "tnh", "tni", "tnk", "tnl", "tnm", "tnn", "tno", "tnp", "tnq", "tnr", "tnt", "tnw", "tnx", "tnz", "tob", "toc", "tof", "tog", "toh", "toi", "toj", "tol", "tom", "ton", "too", "top", "tos", "tow", "tox", "tpa", "tpc", "tpe", "tpf", "tpg", "tpi", "tpj", "tpl", "tpm", "tpn", "tpp", "tpr", "tpt", "tpu", "tpw", "tpx", "tpy", "tpz", "tqb", "tql", "tqo", "tqp", "tqq", "tqu", "tqw", "tra", "trc", "trd", "trf", "trg", "trh", "tri", "trm", "trn", "tro", "trp", "trq", "trr", "trs", "trt", "tru", "trv", "trw", "trx", "trz", "tsa", "tsb", "tsc", "tsd", "tsg", "tsh", "tsi", "tsj", "tsk", "tsl", "tsn", "tso", "tsr", "tsu", "tsv", "tsw", "tsx", "tsz", "tta", "ttc", "ttd", "tte", "ttf", "ttg", "tti", "ttj", "ttk", "ttm", "ttn", "ttq", "ttr", "tts", "ttt", "ttu", "ttv", "ttw", "tty", "tua", "tub", "tuc", "tud", "tue", "tuf", "tug", "tuh", "tui", "tuk", "tul", "tum", "tun", "tuo", "tuq", "tur", "tus", "tuu", "tuv", "tux", "tuy", "tuz", "tva", "tvd", "tve", "tvk", "tvl", "tvm", "tvo", "tvs", "tvu", "tvw", "tvy", "twa", "twb", "twd", "twe", "twf", "twg", "twh", "twi", "twm", "twp", "twq", "twt", "twu", "tww", "twx", "twy", "txa", "txb", "txc", "txe", "txg", "txh", "txi", "txm", "txn", "txo", "txs", "txt", "txu", "txx", "txy", "tya", "tye", "tyi", "tyj", "tyn", "typ", "tyr", "tyu", "tyv", "tyx", "tyz", "tzh", "tzj", "tzl", "tzm", "tzn", "tzo", "tzx", "uam", "uan", "uar", "uba", "ubi", "ubl", "ubr", "ubu", "uby", "uda", "ude", "udi", "udj", "udl", "udm", "udu", "ues", "ufi", "uga", "uge", "ugo", "uha", "uhn", "uig", "uiv", "uji", "uka", "ukg", "ukh", "uki", "ukp", "ukq", "ukr", "uku", "ukv", "ula", "ulc", "ule", "ulf", "uli", "ulk", "ulm", "uln", "ulu", "ulw", "uma", "umb", "umc", "umg", "umm", "umo", "ump", "umr", "ums", "umu", "una", "und", "une", "ung", "unk", "unm", "unn", "unr", "unz", "upv", "ura", "urb", "urc", "urd", "ure", "urf", "urg", "urh", "uri", "urk", "urn", "urp", "urr", "urt", "uru", "urv", "urw", "urx", "ury", "urz", "usa", "ush", "usk", "usp", "usu", "uta", "ute", "utp", "utr", "utu", "uum", "uur", "uuu", "uve", "uvh", "uvl", "uwa", "uya", "uzn", "uzs", "vaa", "vae", "vaf", "vag", "vah", "vai", "vaj", "val", "vam", "van", "vao", "var", "vas", "vau", "vay", "vbb", "vbk", "vec", "ved", "vel", "vem", "ven", "veo", "vep", "ver", "vic", "vie", "vif", "vig", "vil", "vin", "vit", "viv", "vka", "vkj", "vkl", "vkm", "vko", "vkp", "vku", "vlp", "vls", "vma", "vmb", "vme", "vmf", "vmg", "vmi", "vmk", "vml", "vmm", "vmp", "vmr", "vmw", "vmy", "vmz", "vnk", "vnm", "vnp", "vol", "vor", "vot", "vra", "vro", "vrs", "vrt", "vun", "vut", "waa", "wab", "wac", "wad", "wae", "wah", "waj", "wal", "wam", "wan", "wao", "wap", "waq", "war", "was", "wat", "wau", "wav", "waw", "wax", "way", "waz", "wba", "wbb", "wbe", "wbf", "wbh", "wbi", "wbj", "wbk", "wbl", "wbm", "wbp", "wbr", "wbt", "wbv", "wbw", "wca", "wci", "wdd", "wdg", "wdj", "wea", "wed", "weg", "weh", "wei", "weo", "wep", "wer", "wes", "wet", "wew", "wfg", "wga", "wgb", "wgg", "wgi", "wgo", "wgu", "wgy", "wha", "whk", "wib", "wic", "wig", "wih", "wii", "wik", "wil", "wim", "win", "wir", "wiu", "wiv", "wiy", "wja", "wji", "wka", "wkw", "wla", "wlc", "wle", "wlg", "wlh", "wli", "wlk", "wll", "wlm", "wln", "wlo", "wlr", "wls", "wlu", "wlv", "wlw", "wly", "wmb", "wmc", "wmd", "wme", "wmh", "wmi", "wmo", "wms", "wmt", "wmw", "wmx", "wnb", "wnc", "wnd", "wne", "wni", "wnk", "wno", "wnp", "wnu", "wnw", "wny", "woa", "wob", "woc", "wod", "woe", "wof", "wog", "woi", "wok", "wol", "wom", "won", "wos", "wow", "wpc", "wrb", "wrg", "wrh", "wri", "wrk", "wrl", "wrm", "wrn", "wro", "wrp", "wrr", "wrs", "wru", "wrv", "wrw", "wry", "wrz", "wsa", "wsg", "wsi", "wsk", "wsr", "wsv", "wtf", "wth", "wti", "wtm", "wtw", "wub", "wuh", "wul", "wum", "wun", "wut", "wuu", "wuv", "wux", "wuy", "wwa", "wwo", "wwr", "www", "wxa", "wyb", "wyi", "wym", "wyr", "wyy", "xaa", "xab", "xac", "xad", "xai", "xaj", "xal", "xam", "xan", "xap", "xaq", "xar", "xas", "xat", "xau", "xav", "xaw", "xbc", "xbe", "xbg", "xbi", "xbm", "xbn", "xbr", "xbw", "xce", "xcg", "xch", "xcl", "xcm", "xcn", "xco", "xcr", "xct", "xcw", "xcy", "xdc", "xdk", "xdm", "xdy", "xeb", "xed", "xeg", "xel", "xem", "xep", "xer", "xes", "xet", "xeu", "xfa", "xga", "xgd", "xgf", "xgm", "xhd", "xho", "xht", "xhu", "xib", "xii", "xil", "xin", "xir", "xiy", "xjt", "xka", "xkb", "xke", "xkg", "xkl", "xkn", "xkq", "xkr", "xks", "xkv", "xky", "xla", "xlb", "xlc", "xld", "xlg", "xlo", "xlp", "xls", "xlu", "xly", "xmb", "xmf", "xmg", "xmh", "xmk", "xmm", "xmn", "xmr", "xmt", "xmu", "xmv", "xmw", "xmy", "xmz", "xnb", "xng", "xni", "xnm", "xnn", "xno", "xns", "xny", "xnz", "xod", "xog", "xok", "xom", "xon", "xoo", "xop", "xor", "xow", "xpb", "xpd", "xpe", "xpf", "xpg", "xpk", "xpm", "xpq", "xpr", "xpt", "xpu", "xpv", "xpy", "xqt", "xra", "xre", "xri", "xrn", "xrt", "xru", "xrw", "xsa", "xsb", "xsc", "xse", "xsh", "xsi", "xsl", "xsm", "xsp", "xsr", "xss", "xsu", "xsv", "xsy", "xta", "xtc", "xtd", "xte", "xtg", "xtm", "xto", "xtv", "xua", "xub", "xug", "xul", "xum", "xuo", "xup", "xur", "xut", "xuu", "xvi", "xvn", "xvs", "xwa", "xwc", "xwd", "xwg", "xwk", "xwo", "xww", "xxk", "xxt", "xyj", "xyk", "xyt", "xyy", "xzh", "xzp", "yaa", "yab", "yad", "yae", "yaf", "yag", "yah", "yai", "yaj", "yak", "yal", "yam", "yan", "yao", "yap", "yaq", "yar", "yas", "yat", "yau", "yav", "yaw", "yay", "yaz", "yba", "ybb", "ybe", "ybh", "ybi", "ybj", "ybl", "ybm", "ybn", "ybo", "ybx", "yby", "ycl", "ycn", "yda", "ydd", "ydg", "ydk", "yea", "yec", "yee", "yer", "yes", "yet", "yev", "yey", "yga", "ygr", "ygw", "yha", "yhl", "yia", "yig", "yih", "yii", "yij", "yim", "yis", "yix", "yka", "ykg", "yki", "ykm", "yko", "ykr", "ykt", "yky", "yle", "ylg", "yli", "yll", "yln", "ylr", "ylu", "yly", "ymb", "ymc", "yme", "yml", "ymm", "ymo", "ymp", "ynd", "ynk", "ynl", "ynn", "ynq", "yns", "ynu", "yob", "yog", "yoi", "yok", "yol", "yom", "yon", "yor", "yot", "yox", "yoy", "ypg", "yph", "ypz", "yra", "yrb", "yre", "yrk", "yrl", "yrn", "yrw", "ysn", "ysr", "yss", "yua", "yub", "yuc", "yue", "yuf", "yug", "yui", "yuj", "yuk", "yul", "yum", "yun", "yup", "yur", "yut", "yuw", "yux", "yuy", "yuz", "yva", "yvt", "ywa", "ywn", "ywq", "ywr", "ywt", "yww", "yxg", "yxl", "yyr", "yyu", "yzg", "zaa", "zab", "zac", "zad", "zae", "zaf", "zag", "zai", "zaj", "zak", "zal", "zao", "zap", "zaq", "zar", "zav", "zaw", "zay", "zaz", "zba", "zbc", "zbe", "zbl", "zbw", "zca", "zch", "zdj", "zea", "zeg", "zen", "zga", "zgb", "zgh", "zgn", "zgr", "zha", "zhb", "zhd", "zhi", "zhn", "zho", "zhw", "zia", "zik", "zim", "zin", "ziw", "ziz", "zka", "zkb", "zkd", "zkg", "zkk", "zko", "zkp", "zkr", "zkt", "zku", "zlj", "zlm", "zln", "zma", "zmb", "zmc", "zmd", "zme", "zmg", "zmj", "zmk", "zml", "zmm", "zmn", "zmo", "zmp", "zmr", "zmt", "zmu", "zmv", "zmw", "zmx", "zmy", "zmz", "zne", "zng", "zns", "zoc", "zoh", "zom", "zoq", "zor", "zos", "zpc", "zpf", "zpi", "zpl", "zpm", "zpn", "zpo", "zpq", "zpr", "zps", "zpu", "zpv", "zpw", "zpx", "zpz", "zqe", "zro", "zrp", "zrs", "zsa", "zsm", "zsu", "ztg", "ztp", "ztq", "zts", "ztu", "zua", "zuh", "zul", "zum", "zun", "zwa", "zyb", "zyg", "zyj", "zyn", "zzj" ]

[ "# PanLex\n\nJanuary 1, 2024 version of PanLex Language Vocabulary with 24,650,274 rows covering 6,152 languages.", "# Columns\n- 'vocab': contains the text entry. \n- '639-3': contains the ISO 639-3 languages tags to allow users to filter on the language(s) of their choice.\n- '639-3_english_name': the English language name associated to the code ISO 639-3. \n- 'var_code': contains a code to differentiate language variants. In practice, this is the code 639-3 + a number. If 000, it corresponds to the classic 639-3, if the number is different, it corresponds to a variant (e.g. 'fra-006' corresponds to Québécois according to URL This is a code created by Panlex, and therefore does not follow an international standard. \n- 'english_name_var': the English language name associated to the 'var_code'.", "# Source\nReaders are invited to consult the URL page, which summarizes all the sources.\n\n> Kamholz, David, Jonathan Pool, and Susan M. Colowick. 2014. \n PanLex: Building a Resource for Panlingual Lexical Translation. \n Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014). \n URL" ]

[ "TAGS\n#size_categories-10M<n<100M #language-Ghotuo #language-Alumu-Tesu #language-Ari #language-Amal #language-Arbëreshë Albanian #language-Ambrak #language-Arifama-Miniafia #language-Ankave #language-Afade #language-Anambé #language-Algerian Saharan Arabic #language-Pará Arára #language-Eastern Abnaki #language-Afar #language-Aasáx #language-Arvanitika Albanian #language-Abau #language-Solong #language-Mandobo Atas #language-Abé #language-Bankon #language-Western Abnaki #language-Abai Sungai #language-Abaga #language-Abidji #language-Aka-Bea #language-Abkhazian #language-Lampung Nyo #language-Abanyom #language-Abua #language-Abon #language-Abaza #language-Abron #language-Ambonese Malay #language-Ambulas #language-Abure #language-Baharna Arabic #language-Pal #language-Inabaknon #language-Aneme Wake #language-Abui #language-Achagua #language-Gikyode #language-Achinese #language-Saint Lucian Creole French #language-Acoli #language-Aka-Cari #language-Aka-Kora #language-Akar-Bale #language-Mesopotamian Arabic #language-Achang #language-Eastern Acipa #language-Ta'izzi-Adeni Arabic #language-Achi #language-Acroá #language-Achterhoeks #language-Achuar-Shiwiar #language-Achumawi #language-Hijazi Arabic #language-Omani Arabic #language-Cypriot Arabic #language-Adangme #language-Lidzonka #language-Adele #language-Andegerebinha #language-Adhola #language-Adi #language-Adioukrou #language-Galo #language-Adang #language-Abu #language-Adangbe #language-Adonara #language-Adnyamathanha #language-Amundava #language-Amdo Tibetan #language-Adyghe #language-Adzera #language-Tunisian Arabic #language-Saidi Arabic #language-Northeast Pashai #language-Ambele #language-Arem #language-Eastern Arrernte #language-Alsea #language-Ambakich #language-Amele #language-Aeka #language-Gulf Arabic #language-Andai #language-Putukwam #language-Akrukay #language-Nanubae #language-Defaka #language-Eloyi #language-Tapei #language-Afrikaans #language-Afitti #language-Awutu #language-Obokuitai #language-Legbo #language-Agatu #language-Agarabi #language-Angal #language-Arguni #language-Angor #language-Ngelima #language-Argobba #language-Fembe #language-Angaataha #language-Agutaynen #language-Tainae #language-Aghem #language-Aguaruna #language-Esimbi #language-Central Cagayan Agta #language-Aguacateco #language-Remontado Dumagat #language-Kahua #language-Aghul #language-Ahanta #language-Axamb #language-Qimant #language-Aghu #language-Tiagbamrin Aizi #language-Akha #language-Igo #language-Ahom #language-Ahirani #language-Ashe #language-Ahtena #language-Arosi #language-Ainu (China) #language-Alngith #language-Amara #language-Agi #language-Ai-Cham #language-Assyrian Neo-Aramaic #language-Ake #language-Aimele #language-Aimol #language-Ainu (Japan) #language-Aiton #language-Airoran #language-Arikem #language-Aari #language-Aighon #language-Aja (South Sudan) #language-Aja (Benin) #language-Ajië #language-South Levantine Arabic #language-Amri Karbi #language-Akan #language-Batak Angkola #language-Mpur #language-Ukpet-Ehom #language-Akawaio #language-Akpa #language-Anakalangu #language-Angal Heneng #language-Aiome #language-Aka-Jeru #language-Akkadian #language-Aklanon #language-Aka-Bo #language-Akurio #language-Siwu #language-Ak #language-Araki #language-Akaselem #language-Akum #language-Akhvakh #language-Akwa #language-Aka-Kede #language-Aka-Kol #language-Alabama #language-Alago #language-Qawasqar #language-Alladian #language-Aleut #language-Alege #language-Alawa #language-Amaimon #language-Alangan #language-Alak #language-Allar #language-Amblong #language-Gheg Albanian #language-Larike-Wakasihu #language-Alune #language-Algonquin #language-Alutor #language-Tosk Albanian #language-Southern Altai #language-'Are'are #language-Alaba-K’abeena #language-Amol #language-Alyawarr #language-Alur #language-Amanayé #language-Amahuaca #language-Yanesha' #language-Hamer-Banna #language-Amurdak #language-Amharic #language-Amis #language-Amdang #language-Ambai #language-War-Jaintia #language-Ama (Papua New Guinea) #language-Amanab #language-Amo #language-Alamblak #language-Amahai #language-Amarakaeri #language-Southern Amami-Oshima #language-Amto #language-Guerrero Amuzgo #language-Ambelau #language-Western Neo-Aramaic #language-Anmatyerre #language-Ami #language-Atampaya #language-Andaqui #language-Andoa #language-Ngas #language-Ansus #language-Xârâcùù #language-Animere #language-Old English (ca. 450-1100) #language-Nend #language-Andi #language-Anor #language-Goemai #language-Anal #language-Obolo #language-Andoque #language-Angika #language-Jarawa (India) #language-Antakarinya #language-Anuak #language-Denya #language-Anaang #language-Andra-Hus #language-Anyin #language-Anem #language-Angolar #language-Pemon #language-Andarum #language-Bragat #language-Angoram #language-Anindilyakwa #language-Mufian #language-Alor #language-Ömie #language-Bumbita Arapesh #language-Aore #language-Taikat #language-Atong (India) #language-A'ou #language-Atorada #language-Uab Meto #language-Sa'a #language-Levantine Arabic #language-Sudanese Arabic #language-Bukiyip #language-Athpariya #language-Apiaká #language-Jicarilla Apache #language-Kiowa Apache #language-Lipan Apache #language-Mescalero-Chiricahua Apache #language-Apinayé #language-Ambul #language-Apma #language-A-Pucikwar #language-Arop-Lokep #language-Apatani #language-Apurinã #language-Western Apache #language-Aputai #language-Apalaí #language-Safeyoka #language-Archi #language-Ampari Dogon #language-Arigidi #language-Atohwaim #language-Atakapa #language-Angaité #language-Akuntsu #language-Arabic #language-Standard Arabic #language-Official Aramaic (700-300 BCE) #language-Arabana #language-Western Arrarnta #language-Aragonese #language-Arhuaco #language-Arikara #language-Arikapú #language-Arabela #language-Mapudungun #language-Araona #language-Arapaho #language-Algerian Arabic #language-Karo (Brazil) #language-Najdi Arabic #language-Arbore #language-Arawak #language-Aruá (Rodonia State) #language-Moroccan Arabic #language-Egyptian Arabic #language-Asu (Tanzania) #language-Assiniboine #language-Casuarina Coast Asmat #language-American Sign Language #language-Cishingini #language-Abishira #language-Buruwai #language-Sari #language-Ashkun #language-Asilulu #language-Assamese #language-Xingú Asuriní #language-Dano #language-Asuri #language-Ipulo #language-Asturian #language-Tocantins Asurini #language-Asoa #language-Muratayak #language-Yaosakor Asmat #language-As #language-Pele-Ata #language-Zaiwa #language-Atsahuaca #language-Ata Manobo #language-Atemble #language-Attié #language-Atikamekw #language-Ati #language-Ata #language-Atong (Cameroon) #language-Aralle-Tabulahan #language-Waimiri-Atroari #language-Gros Ventre #language-Pamplona Atta #language-Northern Altai #language-Atsugewi #language-Arutani #language-Aneityum #language-Arta #language-Asumboa #language-Waorani #language-Anuta #language-Aushi #language-Anuki #language-Awjilah #language-Heyo #language-Aulua #language-Asu (Nigeria) #language-Molmo One #language-Makayam #language-Austral #language-Auye #language-Awyi #language-Awiyaana #language-Uzbeki Arabic #language-Avaric #language-Avau #language-Alviri-Vidari #language-Avestan #language-Avikam #language-Kotava #language-Angkamuthi #language-Avatime #language-Au #language-Avokaya #language-Avá-Canoeiro #language-Awadhi #language-Awa (Papua New Guinea) #language-Cicipu #language-Awetí #language-Anguthimri #language-Awbono #language-Aekyom #language-Awabakal #language-Arawum #language-Awngi #language-Awak #language-Awera #language-South Awyu #language-Araweté #language-Jair Awyu #language-Awara #language-Edera Awyu #language-Abipon #language-Ayerrerenge #language-Mato Grosso Arára #language-Yaka (Central African Republic) #language-Lower Southern Aranda #language-Middle Armenian #language-Xârâgurè #language-Awar #language-Ayizo Gbe #language-Southern Aymara #language-Ayabadhu #language-Ayere #language-Ginyanga #language-Hadrami Arabic #language-Leyigha #language-Libyan Arabic #language-Aymara #language-Sanaani Arabic #language-Ayoreo #language-Central Aymara #language-Ayu #language-Mai Brat #language-Azha #language-South Azerbaijani #language-Eastern Durango Nahuatl #language-San Pedro Amuzgos Amuzgo #language-North Azerbaijani #language-Ipalapa Amuzgo #language-Western Durango Nahuatl #language-Awing #language-Highland Puebla Nahuatl #language-Babatana #language-Bainouk-Gunyuño #language-Baré #language-Nubaca #language-Tuki #language-Barakai #language-Bashkir #language-Baluchi #language-Bambara #language-Balinese #language-Waimaha #language-Bantawa #language-Bavarian #language-Basa (Cameroon) #language-Bada (Nigeria) #language-Vengo #language-Bambili-Bambui #language-Bamun #language-Baatonum #language-Barai #language-Batak Toba #language-Bau #language-Bangba #language-Baibai #language-Bugan #language-Barombi #language-Ghomálá' #language-Babanki #language-Bats #language-Uneapa #language-West Central Banda #language-Bamali #language-Girawa #language-Bakpinka #language-Mburku #language-Kulung (Nigeria) #language-Karnai #language-Baba #language-Befang #language-Central Bai #language-Southern Balochi #language-North Babar #language-Bamenyam #language-Bamu #language-Baga Pokur #language-Bariai #language-Baoulé #language-Bardi #language-Bunuba #language-Central Bikol #language-Bannoni #language-Bali (Nigeria) #language-Kaluli #language-Bali (Democratic Republic of Congo) #language-Bench #language-Babine #language-Kohumono #language-Awad Bing #language-Shoo-Minda-Nye #language-Bana #language-Bacama #language-Bainouk-Gunyaamolo #language-Bayot #language-Basap #language-Emberá-Baudó #language-Bunama #language-Bade #language-Bonggi #language-Baka (South Sudan) #language-Burun #language-Bai (South Sudan) #language-Budukh #language-Indonesian Bajau #language-Buduma #language-Baldemu #language-Bende #language-Bahnar #language-West Coast Bajau #language-Burunge #language-Oroko #language-Baham #language-Budong-Budong #language-Bandjalang #language-Beaver #language-Bebele #language-Iceve-Maci #language-Bedoanas #language-Byangsi #language-Benabena #language-Belait #language-Biali #language-Bekati' #language-Beja #language-Bebeli #language-Belarusian #language-Bemba (Zambia) #language-Bengali #language-Beami #language-Besoa #language-Beembe #language-Guiberoua Béte #language-Blagar #language-Daloa Bété #language-Betawi #language-Jur Modo #language-Beli (Papua New Guinea) #language-Bena (Tanzania) #language-Bari #language-Panyi Bai #language-Bafut #language-Bofi #language-Busang Kayan #language-Blafe #language-Bafanji #language-Banda-Ndélé #language-Mmen #language-Bunak #language-Beba #language-Badaga #language-Southern Bai #language-Balti #language-Gahri #language-Bondo #language-Bagheli #language-Mahasu Pahari #language-Haryanvi #language-Rathwi Bareli #language-Bangandu #language-Bugun #language-Giangan #language-Bangolan #language-Bit #language-Bo (Laos) #language-Western Balochi #language-Baga Koga #language-Eastern Balochi #language-Bagri #language-Bawm Chin #language-Tagabawa #language-Bughotu #language-Mbongno #language-Warkay-Bipim #language-Banggai #language-Bhili #language-Biga #language-Bhadrawahi #language-Odiai #language-Binandere #language-Bukharic #language-Bahing #language-Bimin #language-Bohtan Neo-Aramaic #language-Bhojpuri #language-Bima #language-Tukang Besi South #language-Bara Malagasy #language-Bhattiyali #language-Biak #language-Bhele #language-Bada (Indonesia) #language-Badimaya #language-Bissa #language-Bidiyo #language-Bepour #language-Biafada #language-Biangai #language-Bile #language-Bimoba #language-Bini #language-Nai #language-Bipi #language-Bisorio #language-Bislama #language-Berinomo #language-Biete #language-Southern Birifor #language-Kol (Cameroon) #language-Birhor #language-Baloi #language-Budza #language-Banggarla #language-Bariji #language-Biao-Jiao Mien #language-Barzani Jewish Neo-Aramaic #language-Bidyogo #language-Bahinemo #language-Burji #language-Barok #language-Bulu (Papua New Guinea) #language-Bajelani #language-Banjar #language-Fanamaket #language-Binumarien #language-Bajan #language-Balanta-Ganja #language-Bedjond #language-Bakwé #language-Bayali #language-Baruga #language-Baka (Cameroon) #language-Binukid #language-Bakoko #language-Baki #language-Pande #language-Brokskat #language-Berik #language-Kom (Cameroon) #language-Bukitan #language-Kwa' #language-Bakairí #language-Bakumpai #language-Northern Sorsoganon #language-Buhid #language-Bekwarra #language-Bekwel #language-Baikeno #language-Bokyi #language-Bungku #language-Siksika #language-Bilua #language-Bella Coola #language-Bolango #language-Balanta-Kentohe #language-Buol #language-Kuwaa #language-Bolia #language-Bolongan #language-Pa'o Karen #language-Biloxi #language-Beli (South Sudan) #language-Southern Catanduanes Bikol #language-Blablanga #language-Baluan-Pam #language-Blang #language-Balaesang #language-Tai Dam #language-Kibala #language-Balangao #language-Mag-Indi Ayta #language-Notre #language-Balantak #language-Bembe #language-Biem #language-Baga Manduri #language-Limassa #language-Bom-Kim #language-Bamwe #language-Kein #language-Bagirmi #language-Ghayavi #language-Northern Betsimisaraka Malagasy #language-Bina (Papua New Guinea) #language-Bambalang #language-Muinane #language-Biao Mon #language-Somba-Siawari #language-Bum #language-Baimak #language-Baramu #language-Bonerate #language-Bookan #language-Bontok #language-Banda (Indonesia) #language-Bintauna #language-Masiwang #language-Benga #language-Bangi #language-Eastern Tawbuid #language-Bierebo #language-Boon #language-Batanga #language-Bunun #language-Bantoanon #language-Bola #language-Bantik #language-Butmas-Tur #language-Bundeli #language-Bonerif #language-Bisis #language-Bangubangu #language-Bintulu #language-Beezen #language-Bora #language-Aweer #language-Tibetan #language-Mundabli #language-Bolon #language-Boma #language-Barbareño #language-Anjam #language-Bole #language-Berom #language-Bine #language-Bonkiman #language-Bogaya #language-Borôro #language-Bosnian #language-Bongo #language-Bondei #language-Tuwuli #language-Buamu #language-Bodo (Central African Republic) #language-Tiéyaxo Bozo #language-Daakaka #language-Bonggo #language-Botlikh #language-Bagupi #language-Binji #language-Orowe #language-Biyom #language-Dzao Min #language-Kaure #language-Banda Malay #language-Koronadal Blaan #language-Sarangani Blaan #language-Bongu #language-Bian Marind #language-Bo (Papua New Guinea) #language-Bishnupriya #language-Bilba #language-Boko (Benin) #language-Bago-Kusuntu #language-Bakhtiari #language-Bilakura #language-Busa #language-Biritai #language-Burusu #language-Bosngun #language-Bamukumbit #language-Koro Wachi #language-Buru (Nigeria) #language-Bakaka #language-Brao #language-Berbice Creole Dutch #language-Baraamu #language-Breton #language-Bira #language-Baure #language-Brahui #language-Mokpwe #language-Bieria #language-Birked #language-Birwa #language-Barambu #language-Boruca #language-Barapasi #language-Breri #language-Birao #language-Bitare #language-Eastern Bru #language-Western Bru #language-Bodo (India) #language-Bilbil #language-Brunei Bisaya #language-Bassari #language-Wushi #language-Bauchi #language-Bashkardi #language-Kati #language-Bassossi #language-Burushaski #language-Basa-Gumna #language-Busami #language-Barasana-Eduria #language-Baga Sitemu #language-Bassa #language-Akoose #language-Basketo #language-Bahonsuai #language-Baiso #language-Yangkam #language-Sabah Bisaya #language-Bata #language-Bati (Cameroon) #language-Batak Dairi #language-Gamo-Ningi #language-Birgit #language-Biatah Bidayuh #language-Bacanese Malay #language-Batak Mandailing #language-Ratagnon #language-Rinconada Bikol #language-Budibud #language-Batek #language-Baetora #language-Batak Simalungun #language-Bete-Bendi #language-Batu #language-Bateri #language-Butuanon #language-Batak Karo #language-Bobot #language-Batak Alas-Kluet #language-Buriat #language-Bua #language-Bushi #language-Ntcham #language-Beothuk #language-Bushoong #language-Buginese #language-Younuo Bunu #language-Bugawac #language-Bulgarian #language-Bulu (Cameroon) #language-Sherbro #language-Terei #language-Busoa #language-Brem #language-Bokobaru #language-Bungain #language-Bubi #language-Boghom #language-Bullom So #language-Barein #language-Bube #language-Baelelea #language-Baeggu #language-Bonkeng #language-Belanda Viri #language-Baan #language-Bukat #language-Bamunka #language-Buna #language-Bolgo #language-Birri #language-Burarra #language-Baniva #language-Boga #language-Dibole #language-Bauzi #language-Bwatoo #language-Namosi-Naitasiri-Serua #language-Bwaidoka #language-Bwe Karen #language-Boselewa #language-Barwe #language-Baniwa #language-Bauwaki #language-Bwela #language-Biwat #language-Wunai Bunu #language-Boro (Ethiopia) #language-Mandobo Bawah #language-Southern Bobo Madaré #language-Bura-Pabir #language-Bomboma #language-Bafaw-Balong #language-Buli (Ghana) #language-Bwa #language-Bu-Nao Bunu #language-Tairaha #language-Belanda Bor #language-Pela #language-Birale #language-Bilur #language-Bangala #language-Buhutu #language-Pirlatapa #language-Bayungu #language-Bukusu #language-Jalkunan #language-Mongolia Buriat #language-Burduna #language-Beele #language-Russia Buriat #language-China Buriat #language-Berakou #language-Bankagooma #language-Binahari #language-Batak #language-Benyadu' #language-Pouye #language-Bete #language-Buyu #language-Biao #language-Bayono #language-Bidjara #language-Bilin #language-Biyo #language-Bumaji #language-Basay #language-Baruya #language-Burak #language-Berti #language-Medumba #language-Belhariya #language-Qaqet #language-Banaro #language-Bandi #language-Andio #language-Southern Betsimisaraka Malagasy #language-Bribri #language-Jenaama Bozo #language-Boikin #language-Babuza #language-Mapos Buang #language-Bisu #language-Belize Kriol English #language-Nicaragua Creole English #language-Boano (Sulawesi) #language-Boano (Maluku) #language-Kemberano #language-Buli (Indonesia) #language-Biri #language-Brithenig #language-Burmeso #language-Naami #language-Basa (Nigeria) #language-Kɛlɛngaxo Bozo #language-Obanliku #language-Evant #language-Chortí #language-Garifuna #language-Chuj #language-Caddo #language-Lehar #language-Southern Carrier #language-Nivaclé #language-Cahuarano #language-Chané #language-Kaqchikel #language-Carolinian #language-Cemuhî #language-Chambri #language-Chácobo #language-Chipaya #language-Car Nicobarese #language-Galibi Carib #language-Tsimané #language-Catalan #language-Cavineña #language-Callawalla #language-Chiquitano #language-Cayuga #language-Canichana #language-Cabiyarí #language-Carapana #language-Carijona #language-Chimila #language-Chachi #language-Ede Cabe #language-Chavacano #language-Nyahkur #language-Izora #language-Cashibo-Cacataibo #language-Cashinahua #language-Chayahuita #language-Candoshi-Shapra #language-Cacua #language-Carabayo #language-Chamicuro #language-Chopi #language-Samba Daka #language-Atsam #language-Kasanga #language-Comaltepec Chinantec #language-Chakma #language-Cacaopera #language-Chiru #language-Chambeali #language-Churahi #language-Chepang #language-Chaudangsi #language-Min Dong Chinese #language-Cinda-Regi-Tiyal #language-Chadong #language-Koda #language-Lower Chehalis #language-Cebuano #language-Chamacoco #language-Czech #language-Centúúm #language-Dijim-Bwilim #language-Cara #language-Como Karim #language-Falam Chin #language-Kagayanen #language-Chiga #language-Chamorro #language-Chibcha #language-Catawba #language-Highland Oaxaca Chontal #language-Chechen #language-Tabasco Chontal #language-Chagatai #language-Chinook #language-Ojitlán Chinantec #language-Chuukese #language-Cahuilla #language-Mari (Russia) #language-Chinook jargon #language-Choctaw #language-Chipewyan #language-Quiotepec Chinantec #language-Cherokee #language-Cholón #language-Church Slavic #language-Chuvash #language-Chuwabu #language-Chantyal #language-Cheyenne #language-Ozumacín Chinantec #language-Cia-Cia #language-Chickasaw #language-Chimariko #language-Cineni #language-Chitkuli Kinnauri #language-Cimbrian #language-Cinta Larga #language-Chiapanec #language-Tiri #language-Chippewa #language-Chaima #language-Western Cham #language-Chru #language-Upper Chehalis #language-Chamalal #language-Chokwe #language-Eastern Cham #language-Chenapian #language-Ashéninka Pajonal #language-Cabécar #language-Shor #language-Chuave #language-Jinyu Chinese #language-Central Kurdish #language-Chak #language-Cibak #language-Anufo #language-Kajakse #language-Tayo #language-Chukot #language-Koasati #language-Kavalan #language-Caka #language-Ron #language-Chilcotin #language-Chaldean Neo-Aramaic #language-Lealao Chinantec #language-Chilisso #language-Chakali #language-Idu-Mishmi #language-Chala #language-Clallam #language-Lowland Oaxaca Chontal #language-Chulym #language-Eastern Highland Chatino #language-Cerma #language-Classical Mongolian #language-Emberá-Chamí #language-Campalagian #language-Mandarin Chinese #language-Central Mnong #language-Chinbon Chin #language-Northern Qiang #language-Hakha Chin #language-Asháninka #language-Khumi Chin #language-Lalana Chinantec #language-Northern Ping Chinese #language-Montenegrin #language-Central Asmat #language-Tepetotutla Chinantec #language-Chenoua #language-Middle Cornish #language-Chicomuceltec #language-Cocopa #language-Cocama-Cocamilla #language-Koreguaje #language-Colorado #language-Chong #language-Chonyi-Dzihana-Kauma #language-Cochimi #language-Santa Teresa Cora #language-Columbia-Wenatchi #language-Comanche #language-Cofán #language-Comox #language-Coptic #language-Coquille #language-Cornish #language-Corsican #language-Caquinte #language-Wamey #language-Cowlitz #language-Chochotec #language-Palantla Chinantec #language-Ucayali-Yurúa Ashéninka #language-Ajyíninka Apurucayali #language-Cappadocian Greek #language-Chinese Pidgin English #language-Cherepon #language-Capiznon #language-Pichis Ashéninka #language-Pu-Xian Chinese #language-Chuanqiandian Cluster Miao #language-Chara #language-Island Carib #language-Lonwolwol #language-Coeur d'Alene #language-Cree #language-Michif #language-Crimean Tatar #language-Sãotomense #language-Southern East Cree #language-Plains Cree #language-Northern East Cree #language-Moose Cree #language-El Nayar Cora #language-Crow #language-Iyo'wujwa Chorote #language-Carolina Algonquian #language-Seselwa Creole French #language-Iyojwa'ja Chorote #language-Chrau #language-Carrier #language-Cori #language-Cruzeño #language-Chiltepec Chinantec #language-Kashubian #language-Asho Chin #language-Coast Miwok #language-Central Sierra Miwok #language-Sochiapam Chinantec #language-Southern Ping Chinese #language-Southern Ohlone #language-Northern Ohlone #language-Swampy Cree #language-Siyin Chin #language-Coos #language-Tataltepec Chatino #language-Chetco #language-Tedim Chin #language-Tepinapa Chinantec #language-Chittagonian #language-Tlacoatzintepec Chinantec #language-Chitimacha #language-Chhintange #language-Emberá-Catío #language-Western Highland Chatino #language-Northern Catanduanes Bikol #language-Chol #language-Zacatepec Chatino #language-Cua #language-Cubeo #language-Usila Chinantec #language-Chuka #language-Cuiba #language-Mashco Piro #language-San Blas Kuna #language-Culina #language-Cumanagoto #language-Cupeño #language-Cun #language-Chhulung #language-Teutila Cuicatec #language-Tai Ya #language-Cuvok #language-Tepeuxila Cuicatec #language-Cuitlatec #language-Chug #language-Valle Nacional Chinantec #language-Woods Cree #language-Kwere #language-Chewong #language-Kuwaataay #language-Nopala Chatino #language-Cayubaba #language-Welsh #language-Cuyonon #language-Huizhou Chinese #language-Knaanic #language-Zenzontepec Chatino #language-Min Zhong Chinese #language-Zotung Chin #language-Dangaléat #language-Dambi #language-Marik #language-Dagbani #language-Gwahatike #language-Day #language-Dar Fur Daju #language-Dakota #language-Dahalo #language-Damakawa #language-Danish #language-Dandami Maria #language-Dargwa #language-Dar Sila Daju #language-Taita #language-Davawenyo #language-Dayi #language-Bangime #language-Deno #language-Dadiya #language-Dabe #language-Edopi #language-Dogul Dom Dogon #language-Doka #language-Ida'an #language-Dyirbal #language-Duguri #language-Duriankere #language-Duwai #language-Daba #language-Ben Tey Dogon #language-Bondum Dom Dogon #language-Bankan Tey Dogon #language-Dibiyaso #language-Deccan #language-Negerhollands #language-Dadi Dadi #language-Dongotono #language-Fataluku #language-West Goodenough #language-Jaru #language-Dendi (Benin) #language-Dido #language-Donno So Dogon #language-Dawera-Daweloor #language-Dagik #language-Dedua #language-Dewoin #language-Degema #language-Demisa #language-Delaware #language-Dem #language-Slave (Athapascan) #language-Pidgin Delaware #language-Deori #language-Desano #language-German #language-Domung #language-Southern Dagaare #language-Bunoge Dogon #language-Casiguran Dumagat Agta #language-Dagaari Dioula #language-Degenan #language-Doga #language-Dghwede #language-Northern Dagara #language-Andaandi #language-Dogri (individual language) #language-Dogrib #language-Ndra'ngith #language-Daungwurrung #language-Daga #language-Dhundari #language-Dhangu-Djangu #language-Dhimal #language-Dhalandji #language-Dhargari #language-Dhaiso #language-Dhurga #language-Dehu #language-Dia #language-South Central Dinka #language-Lakota Dida #language-Didinga #language-Dieri #language-Digo #language-Kumiai #language-Dimbong #language-Dai #language-Southwestern Dinka #language-Dilling #language-Dime #language-Dinka #language-Dibo #language-Northeastern Dinka #language-Dimli (individual language) #language-Dirim #language-Dimasa #language-Diriku #language-Dhivehi #language-Northwestern Dinka #language-Dixon Reef #language-Ding #language-Djadjawurrung #language-Djinba #language-Dar Daju Daju #language-Djamindjung #language-Zarma #language-Djinang #language-Djeebbana #language-Eastern Maroon Creole #language-Jamsay Dogon #language-Jawoyn #language-Djambarrpuyngu #language-Kapriman #language-Djawi #language-Kuijau #language-Southeastern Dinka #language-Dolgan #language-Dahalik #language-Dalmatian #language-Duma #language-Mombo Dogon #language-Gavak #language-Madhi Madhi #language-Dugwor #language-Medefaidrin #language-Upper Kinabatangan #language-Domaaki #language-Dameli #language-Dama #language-Kemedzung #language-East Damar #language-Dampelas #language-Dubu #language-Dumpas #language-Mudburra #language-Demta #language-Upper Grand Valley Dani #language-Daonda #language-Ndendeule #language-Dungan #language-Lower Grand Valley Dani #language-Dan #language-Dzùùngoo #language-Danaru #language-Mid Grand Valley Dani #language-Danau #language-Western Dani #language-Dení #language-Dom #language-Dobu #language-Northern Dong #language-Doe #language-Domu #language-Dong #language-Dogri (macrolanguage) #language-Dondo #language-Doso #language-Toura (Papua New Guinea) #language-Dongo #language-Dori'o #language-Dogosé #language-Dass #language-Doyayo #language-Bussa #language-Dompo #language-Dorze #language-Papar #language-Dair #language-Darmiya #language-Dolpo #language-Rungus #language-C'Lela #language-Paakantyi #language-West Damar #language-Daro-Matu Melanau #language-Gedeo #language-Rukai #language-Darai #language-Lower Sorbian #language-Daasanach #language-Dusner #language-Desiya #language-Tadaksahak #language-Daur #language-Labuk-Kinabatangan Kadazan #language-Ditidaht #language-Adithinngithigh #language-Ana Tinga Dogon #language-Tene Kan Dogon #language-Tomo Kan Dogon #language-Tommo So Dogon #language-Kadazan Dusun #language-Lotud #language-Toro So Dogon #language-Toro Tegu Dogon #language-Tebul Ure Dogon #language-Dotyali #language-Duala #language-Duna #language-Umiray Dumaget Agta #language-Dumbea #language-Duruma #language-Uyajitaya #language-Middle Dutch (ca. 1050-1350) #language-Dusun Deyah #language-Dupaninan Agta #language-Duano #language-Dusun Malang #language-Dii #language-Dumi #language-Drung #language-Duvle #language-Dusun Witu #language-Duungooma #language-Duau #language-Diri #language-Dawik Kui #language-Dawro #language-Dutton World Speedwords #language-Dhuwal #language-Dawawa #language-Dyan #language-Dyaberdyaber #language-Dyugun #language-Djimini Senoufo #language-Yanda Dom Dogon #language-Dyangadi #language-Jola-Fonyi #language-Dyula #language-Djabugay #language-Djiwarli #language-Dazaga #language-Dzongkha #language-Ebughu #language-Eastern Bontok #language-Teke-Ebo #language-Ebrié #language-Embu #language-Eteocypriot #language-E #language-Efai #language-Efik #language-Ega #language-Emilian #language-Eggon #language-Egyptian (Ancient) #language-Ehueun #language-Eipomek #language-Eitiep #language-Askopan #language-Ejamat #language-Ekajuk #language-Ekit #language-Ekari #language-Standard Estonian #language-Kol (Bangladesh) #language-Elip #language-Koti #language-Ekpeye #language-Yace #language-Eastern Kayah #language-Elepi #language-Nding #language-Elkei #language-Modern Greek (1453-) #language-Eleme #language-El Molo #language-Elamite #language-Emai-Iuleha-Ora #language-Embaloh #language-Emerillon #language-Eastern Meohang #language-Mussau-Emira #language-Eastern Maninkakan #language-Eman #language-Northern Emberá #language-Pacific Gulf Yupik #language-Emplawas #language-Epigraphic Mayan #language-Apali #language-Markweeta #language-En #language-Ende #language-Forest Enets #language-English #language-Tundra Enets #language-Enlhet #language-Middle English (1100-1500) #language-Engenni #language-Enggano #language-Enga #language-Emumu #language-Enwan (Akwa Ibom State) #language-Enxet #language-Beti (Côte d'Ivoire) #language-Epie #language-Esperanto #language-Sie #language-Eruwa #language-Ogea #language-South Efate #language-Horpa #language-Erre #language-Ersu #language-Eritai #language-Erokwanas #language-Ese Ejja #language-Aheri Gondi #language-Eshtehardi #language-North Alaskan Inupiatun #language-Northwest Alaska Inupiatun #language-Estonian Sign Language #language-Esselen #language-Central Siberian Yupik #language-Central Yupik #language-Etebi #language-Etchemin #language-Eton (Vanuatu) #language-Eton (Cameroon) #language-Edolo #language-Yekhee #language-Etruscan #language-Ejagham #language-Eten #language-Semimi #language-Basque #language-Even #language-Uvbie #language-Evenki #language-Ewe #language-Ewondo #language-Extremaduran #language-Eyak #language-Keiyo #language-Fasu #language-Fa d'Ambu #language-Wagi #language-Fagani #language-Finongan #language-Faiwol #language-Faita #language-Fang (Cameroon) #language-South Fali #language-Fam #language-Fang (Equatorial Guinea) #language-Faroese #language-Paloor #language-Fataleka #language-Fanti #language-Fayu #language-Fala #language-Southwestern Fars #language-West Albay Bikol #language-Feroge #language-Maasina Fulfulde #language-Fongoro #language-Nobiin #language-Fyer #language-Fijian #language-Filipino #language-Finnish #language-Fipa #language-Firan #language-Tornedalen Finnish #language-Kirya-Konzəl #language-Kven Finnish #language-Kalispel-Pend d'Oreille #language-Foau #language-Fali #language-North Fali #language-Flinders Island #language-Fuliiru #language-Flaaitaal #language-Fe'fe' #language-Fanagalo #language-Fania #language-Foodo #language-Foi #language-Foma #language-Fon #language-Fore #language-Siraya #language-Fernando Po Creole English #language-Fas #language-French #language-Cajun French #language-Fordata #language-Frankish #language-Middle French (ca. 1400-1600) #language-Old French (842-ca. 1400) #language-Arpitan #language-Forak #language-Northern Frisian #language-Fortsenal #language-Western Frisian #language-Adamawa Fulfulde #language-Pulaar #language-East Futuna #language-Pular #language-Western Niger Fulfulde #language-Ko #language-Fulah #language-Fulniô #language-Friulian #language-Futuna-Aniwa #language-Furu #language-Nigerian Fulfulde #language-Fuyug #language-Fur #language-Fwâi #language-Ga #language-Gabri #language-Mixed Great Andamanese #language-Gaddang #language-Guarequena #language-Gende #language-Gagauz #language-Alekano #language-Borei #language-Gadsup #language-Galolen #language-Kandawo #language-Gan Chinese #language-Gants #language-Gal #language-Gata' #language-Galeya #language-Kenati #language-Nobonob #language-Borana-Arsi-Guji Oromo #language-Gayo #language-West Central Oromo #language-Gbaya (Central African Republic) #language-Kaytetye #language-Karajarri #language-Niksek #language-Gbanziri #language-Galela #language-Bodo Gadaba #language-Garhwali #language-Gbaya-Bossangoa #language-Gbagyi #language-Gagadu #language-Gabi-Gabi #language-Eastern Xwla Gbe #language-Gbari #language-Zoroastrian Dari #language-Mali #language-Ganggalida #language-Galice #language-Guadeloupean Creole French #language-Grenadian Creole English #language-Gaina #language-Guianese Creole French #language-Pottangi Ollar Gadaba #language-Gugu Badhun #language-Gedaged #language-Gude #language-Guduf-Gava #language-Ga'dang #language-Gadjerawang #language-Gundi #language-Gurdjar #language-Dirasha #language-Laal #language-Umanakaina #language-Ghodoberi #language-Mehri #language-Wipi #language-Gudu #language-Godwari #language-Geruma #language-Kire #language-Gade #language-Gengle #language-Hutterite German #language-Gebe #language-Gen #language-Ywom #language-ut-Ma'in #language-Geme #language-Geser-Gorom #language-Eviya #language-Gera #language-Enya #language-Geez #language-Patpatar #language-Gafat #language-Gao #language-Gbii #language-Gugadj #language-Gurr-goni #language-Kungarakany #language-Ganglau #language-Gitua #language-Gagu #language-Gogodala #language-Ghadamès #language-Geko Karen #language-Ghulfan #language-Ghanongga #language-Ghomara #language-Guhu-Samane #language-Kuke #language-Kija #language-Gail #language-Gidar #language-Gilbertese #language-Gimi (Eastern Highlands) #language-Hinukh #language-Gimi (West New Britain) #language-Green Gelao #language-Red Gelao #language-North Giziga #language-Gitxsan #language-White Gelao #language-Gilima #language-Giyug #language-South Giziga #language-Gunditjmara #language-Gonja #language-Gujari #language-Ndai #language-Gokana #language-Guinea Kpelle #language-ǂUngkue #language-Scottish Gaelic #language-Bon Gula #language-Nanai #language-Irish #language-Galician #language-Northwest Pashai #language-Gula Iro #language-Gilaki #language-Galambu #language-Manx #language-Glavda #language-Gule #language-Gambera #language-Gula'alaa #language-Middle High German (ca. 1050-1500) #language-Middle Low German #language-Gbaya-Mbodomo #language-Gumalu #language-Gamo #language-Mycenaean Greek #language-Kaansa #language-Guanche #language-Zulgo-Gemzek #language-Ganang #language-Ngangam #language-Lere #language-Gooniyandi #language-ǁGana #language-Gangulu #language-Ginuman #language-Gumatj #language-Northern Gondi #language-Gana #language-Gureng Gureng #language-Gnau #language-Guro #language-Playero #language-Gorakor #language-Godié #language-Gofa #language-Gogo #language-Old High German (ca. 750-1050) #language-Gobasi #language-Gola #language-Goan Konkani #language-Gondi #language-Yeretuar #language-Gorontalo #language-Gronings #language-Gothic #language-Gavar #language-Gorowa #language-Gobu #language-Gupa-Abawa #language-Ghanaian Pidgin English #language-Taiap #language-Ga'anda #language-Guiqiong #language-Guana (Brazil) #language-Gor #language-Qau #language-Rajput Garasia #language-Grebo #language-Ancient Greek (to 1453) #language-Guruntum-Mbaaru #language-Madi #language-Gbiri-Niragu #language-Ghari #language-Guarani #language-Groma #language-Gorovu #language-Taznatit #language-Gresi #language-Garo #language-Kistane #language-Guriaso #language-Gusilay #language-Nema #language-Southwest Gbaya #language-Wasembo #language-Swiss German #language-Guató #language-Aghu-Tharnggala #language-Shiki #language-Guajajára #language-Wayuu #language-Yocoboué Dida #language-Gurindji #language-Gupapuyngu #language-Paraguayan Guaraní #language-Guahibo #language-Eastern Bolivian Guaraní #language-Gujarati #language-Gumuz #language-Sea Island Creole English #language-Guambiano #language-Mbyá Guaraní #language-Guayabero #language-Gunwinggu #language-Aché #language-Farefare #language-Maléku Jaíka #language-Yanomamö #language-Gourmanchéma #language-Gusii #language-Guana (Paraguay) #language-Guanano #language-Duwet #language-Golin #language-Guajá #language-Gulay #language-Gurmana #language-Kuku-Yalanji #language-Gavião Do Jiparaná #language-Pará Gavião #language-Gurung #language-Gumawana #language-Mbato #language-Gawri #language-Gawwada #language-Gweno #language-Gowro #language-Gwichʼin #language-ǀGwi #language-Gwandara #language-Gwere #language-Gawar-Bati #language-Guwamu #language-Kwini #language-Gua #language-Wè Southern #language-Northwest Gbaya #language-Garus #language-Kayardild #language-Gyem #language-Gayil #language-Ngäbere #language-Guyanese Creole English #language-Gyalsumdo #language-Guarayu #language-Gunya #language-Ganza #language-Gazi #language-Gane #language-Han #language-Gurani #language-Hatam #language-Eastern Oromo #language-Hanga #language-Hahon #language-Haida #language-Hajong #language-Hakka Chinese #language-Halang #language-Hewa #language-Hangaza #language-Hakö #language-Ha #language-Harari #language-Haisla #language-Haitian #language-Hausa #language-Havu #language-Hawaiian #language-Southern Haida #language-Haya #language-Hazaragi #language-Huba #language-Heiban #language-Ancient Hebrew #language-Serbo-Croatian #language-Habu #language-Andaman Creole Hindi #language-Huichol #language-Northern Haida #language-Hadiyya #language-Northern Qiandong Miao #language-Hebrew #language-Herdé #language-Helong #language-Hehe #language-Heiltsuk #language-Hemba #language-Herero #language-Haiǁom #language-Lamang #language-Hibito #language-Hidatsa #language-Fiji Hindi #language-Kamwe #language-Pamosu #language-Seit-Kaitetu #language-Hiligaynon #language-Hindi #language-Tsoa #language-Hittite #language-Hiw #language-Hixkaryána #language-Kahe #language-Hunde #language-Hunjara-Kaina Ke #language-Halia #language-Halbi #language-Hieroglyphic Luwian #language-Humburi Senni Songhay #language-Large Flowery Miao #language-Luopohe Hmong #language-Hmong #language-Hiri Motu #language-Eastern Qiandong Miao #language-Hmar #language-Southern Qiandong Miao #language-Hamtai #language-Hamap #language-Hmong Dô #language-Mina (Cameroon) #language-Southern Hindko #language-Chhattisgarhi #language-Hungu #language-ǁAni #language-Hani #language-Hmong Njua #language-Hanunoo #language-Northern Hindko #language-Caribbean Hindustani #language-Hung #language-Hoava #language-Mari (Madang Province) #language-Ho #language-Holma #language-Horom #language-Hobyót #language-Holikachuk #language-Hadothi #language-Holu #language-Holoholo #language-Hopi #language-Hote #language-Hovongan #language-Honi #language-Hozo #language-Hpon #language-Hrangkhol #language-Niwer Mil #language-Hre #language-Haruku #language-Haroi #language-Hértevin #language-Hruso #language-Croatian #language-Warwar Feni #language-Hunsrik #language-Harzani #language-Upper Sorbian #language-Xiang Chinese #language-Harsusi #language-Hoti #language-Minica Huitoto #language-Hadza #language-Hitu #language-Middle Hittite #language-Huambisa #language-ǂHua #language-Huaulu #language-San Francisco Del Mar Huave #language-Humene #language-Huachipaeri #language-Huilliche #language-Huli #language-Hulung #language-Hula #language-Hungana #language-Hungarian #language-Hu #language-Hupa #language-Tsat #language-Halkomelem #language-Huastec #language-Humla #language-Murui Huitoto #language-San Mateo Del Mar Huave #language-Hukumina #language-Nüpode Huitoto #language-Hunzib #language-Haitian Vodoun Culture Language #language-San Dionisio Del Mar Huave #language-Sabu #language-Santa María Del Mar Huave #language-Wané #language-Hawai'i Creole English #language-Hwana #language-Hya #language-Armenian #language-Western Armenian #language-Iaai #language-Iatmul #language-Purari #language-Iban #language-Ibibio #language-Iwaidja #language-Akpes #language-Ibanag #language-Ibaloi #language-Ibino #language-Igbo #language-Ibuoro #language-Ibu #language-Ibani #language-Ede Ica #language-Etkywan #language-Idakho-Isukha-Tiriki #language-Indo-Portuguese #language-Idon #language-Ede Idaca #language-Idi #language-Ido #language-Indri #language-Idaté #language-Idoma #language-Amganad Ifugao #language-Batad Ifugao #language-Ifè #language-Ifo #language-Tuwali Ifugao #language-Teke-Fuumu #language-Mayoyao Ifugao #language-Keley-I Kallahan #language-Ebira #language-Igede #language-Igala #language-Kanggape #language-Ignaciano #language-Isebe #language-Interglossa #language-Iha #language-Sichuan Yi #language-Thiin #language-Izon #language-Biseni #language-Ede Ije #language-Kalabari #language-Southeast Ijo #language-Eastern Canadian Inuktitut #language-Iko #language-Ikulu #language-Olulumo-Ikom #language-Inuinnaqtun #language-Inuktitut #language-Ikwere #language-Ik #language-Ikizu #language-Ila #language-Interlingue #language-Ilongot #language-Iloko #language-Iranun (Philippines) #language-Ili'uun #language-Ilue #language-Anamgura #language-Miluk #language-Imonda #language-Imbongu #language-Imroing #language-Marsian #language-Interlingua (International Auxiliary Language Association) #language-Inga #language-Indonesian #language-Degexit'an #language-Ingush #language-Jungle Inga #language-Minaean #language-Isinai #language-Inoke-Yate #language-Iñapari #language-Intha #language-Ineseño #language-Inor #language-Tuma-Irumu #language-Iowa-Oto #language-Ipili #language-Inupiaq #language-Ipiko #language-Iquito #language-Iresim #language-Irarutu #language-Rigwe #language-Iraqw #language-Irántxe #language-Irula #language-Kamberau #language-Iraya #language-Isabi #language-Isconahua #language-Isnag #language-Nkem-Nkum #language-Ishkashimi #language-Icelandic #language-Isoko #language-Istriot #language-Isu (Menchum Division) #language-Italian #language-Binongan Itneg #language-Southern Tidung #language-Itene #language-Inlaod Itneg #language-Judeo-Italian #language-Itelmen #language-Itu Mbon Uzo #language-Itonama #language-Iteri #language-Itawit #language-Ito #language-Itik #language-Itzá #language-Iu Mien #language-Ibatan #language-Ivatan #language-Iwam #language-Sepik Iwam #language-Ixcatec #language-Ixil #language-Mesaka #language-Yaka (Congo) #language-Ingrian #language-Izere #language-Izii #language-Jamamadí #language-Hyam #language-Popti' #language-Yabem #language-Jara #language-Jah Hut #language-Zazao #language-Jakun #language-Yalahatan #language-Jamaican Creole English #language-Yanyuwa #language-Yaqay #language-Yaur #language-Javanese #language-Jambi Malay #language-Yan-nhangu #language-Jawe #language-Judeo-Berber #language-Badjiri #language-Arandai #language-Barikewa #language-Nafusi #language-Lojban #language-Jabutí #language-Jukun Takum #language-Krymchak #language-Judeo-Tat #language-Jebero #language-Jerung #language-Jeh #language-Yei #language-Jeri Kuo #language-Yelmek #language-Dza #language-Jere #language-Manem #language-Jonkor Bourmataguil #language-Judeo-Georgian #language-Gwak #language-Ngomba #language-Jehai #language-Jina #language-Jibu #language-Tol #language-Bu (Kaduna State) #language-Jingulu #language-Jiiddu #language-Jilim #language-Jimi (Cameroon) #language-Jiamao #language-Jita #language-Youle Jinuo #language-Shuar #language-Bankal #language-Kaera #language-Mobwa Karen #language-Kubo #language-Paku Karen #language-Labir #language-Ngile #language-Dima #language-Zumbun #language-Machame #language-Yamdena #language-Jimi (Nigeria) #language-Jumli #language-Kamara #language-Mashi (Nigeria) #language-Western Juxtlahuaca Mixtec #language-Jangshung #language-Yangman #language-Janji #language-Yemsa #language-Jaunsari #language-Joba #language-Wojenaka #language-Jorá #language-Jowulu #language-Jewish Palestinian Aramaic #language-Japanese #language-Jaqaru #language-Jarai #language-Judeo-Arabic #language-Jiru #language-Japrería #language-Júma #language-Wannu #language-Jurchen #language-Worodougou #language-Hõne #language-Wapan #language-Jirel #language-Jumjum #language-Juang #language-Jiba #language-Hupdë #language-Jurúna #language-Jutish #language-Ju #language-Wãpha #language-Juray #language-Caribbean Javanese #language-Jiarong #language-Kara-Kalpak #language-Kabyle #language-Kachin #language-Adara #language-Ketangalan #language-Kajaman #language-Kara (Central African Republic) #language-Karekare #language-Jju #language-Kalanguya #language-Kalaallisut #language-Kamba (Kenya) #language-Kannada #language-Xaasongaxango #language-Bezhta #language-Capanahua #language-Kashmiri #language-Georgian #language-Kanuri #language-Katukína #language-Kawi #language-Kamayurá #language-Kazakh #language-Kaxuiâna #language-Kadiwéu #language-Kabardian #language-Kanju #language-Camsá #language-Kaptiau #language-Kari #language-Grass Koiari #language-Kanembu #language-Iwal #language-Kare (Central African Republic) #language-Keliko #language-Kabiyè #language-Kamano #language-Kafa #language-Kande #language-Abadi #language-Dera (Indonesia) #language-Kaiep #language-Ap Ma #language-Manga Kanuri #language-Duhwa #language-Khanty #language-Kawacha #language-Ngkâlmpw Kanum #language-Ukaan #language-Tyap #language-Kamantan #language-Kobiana #language-Kalanga #language-Kela (Papua New Guinea) #language-Gula (Central African Republic) #language-Nubi #language-Kinalakna #language-Kanga #language-Katla #language-Kaian #language-Kami (Tanzania) #language-Kete #language-Kabwari #language-Kachama-Ganjule #language-Korandje #language-Worimi #language-Kutu #language-Yankunytjatjara #language-Makonde #language-Mamusi #language-Tem #language-Kumam #language-Karamojong #language-Numèè #language-Tsikimba #language-Kagoma #language-Kunda #language-Kaningdon-Nindem #language-Koch #language-Karaim #language-Kuy #language-Kadaru #language-Koneraw #language-Kam #language-Kwaja #language-Kabuverdianu #language-Kélé #language-Keiga #language-Kerewe #language-Eastern Keres #language-Kpessi #language-Tese #language-Kei #language-Kekchí #language-Kela (Democratic Republic of Congo) #language-Kemak #language-Kenyang #language-Kakwa #language-Kera #language-Kugbo #language-Ket #language-Akebu #language-West Kewa #language-Kukna #language-Kukele #language-Kodava #language-Northwestern Kolami #language-Konda-Dora #language-Korra Koraga #language-Kota (India) #language-Koya #language-Kannada Kurumba #language-Kinnauri #language-Khunsari #language-Kuk #language-Koro (Côte d'Ivoire) #language-Korwa #language-Korku #language-Kachhi #language-Kumaoni #language-Koromfé #language-Koyaga #language-Kawe #language-Komering #language-Kube #language-Kusunda #language-Gamale Kham #language-Kaiwá #language-Kunggari #language-Krongo #language-Kaingang #language-Kamoro #language-Abun #language-Kumbainggar #language-Somyev #language-Kobol #language-Karas #language-Karon Dori #language-Kamaru #language-Kyerung #language-Khasi #language-Lü #language-Tukang Besi North #language-Korowai #language-Khuen #language-Khams Tibetan #language-Kehu #language-Kuturmi #language-Halh Mongolian #language-Lusi #language-Khmer #language-Khandesi #language-Khotanese #language-Kapori #language-Koyra Chiini Songhay #language-Kharia #language-Kasua #language-Khamti #language-Nkhumbi #language-Khvarshi #language-Khowar #language-Kele (Democratic Republic of Congo) #language-Keapara #language-Kim #language-Koalib #language-Kickapoo #language-Koshin #language-Kibet #language-Kimaama #language-Kilmeri #language-Kitsai #language-Kilivila #language-Kikuyu #language-Kariya #language-Karagas #language-Kinyarwanda #language-Kiowa #language-Kosadle #language-Kirghiz #language-Kis #language-Agob #language-Kirmanjki (individual language) #language-Kimbu #language-Northeast Kiwai #language-Kirikiri #language-Kisi #language-Mlap #language-Q'anjob'al #language-Coastal Konjo #language-Southern Kiwai #language-Kisar #language-Khmu #language-Khakas #language-Zabana #language-Khinalugh #language-Highland Konjo #language-Western Parbate Kham #language-Kháng #language-Kunjen #language-Pwo Eastern Karen #language-Western Keres #language-Kurudu #language-East Kewa #language-Kashaya #language-Ramopa #language-Erave #language-Bumthangkha #language-Kakanda #language-Kwerisa #language-Odoodee #language-Kakabe #language-Khün #language-Kagulu #language-Kako #language-Kokota #language-Kosarek Yale #language-Kiong #language-Karko #language-Gugubera #language-Giiwo #language-Teke-Kukuya #language-Guugu Yimidhirr #language-Kaska #language-Klamath-Modoc #language-Kiliwa #language-Kolbila #language-Gamilaraay #language-Kulung (Nepal) #language-Tagakaulo #language-Weliki #language-Kalumpang #language-Khalaj #language-Kagan Kalagan #language-Migum #language-Kalenjin #language-Kamasa #language-Rumu #language-Khaling #language-Kalasha #language-Nukna #language-Klao #language-Maskelynes #language-Tado #language-Koluwawa #language-Kalao #language-Kabola #language-Konni #language-Kimbundu #language-Southern Dong #language-Majukayang Kalinga #language-Kare (Papua New Guinea) #language-Kâte #language-Kalam #language-Kami (Nigeria) #language-Limos Kalinga #language-Tanudan Kalinga #language-Kom (India) #language-Awtuw #language-Kwoma #language-Kwama #language-Northern Kurdish #language-Kamasau #language-Kemtuik #language-Kanite #language-Karipúna Creole French #language-Komo (Democratic Republic of Congo) #language-Waboda #language-Khorasani Turkish #language-Dera (Nigeria) #language-Lubuagan Kalinga #language-Central Kanuri #language-Konda #language-Kankanaey #language-Mankanya #language-Koongo #language-Kanufi #language-Western Kanjobal #language-Kuranko #language-Kanamarí #language-Konkani (individual language) #language-Kono (Sierra Leone) #language-Kwanja #language-Kintaq #language-Kaningra #language-Kensiu #language-Panoan Katukína #language-Tabo #language-Kung-Ekoka #language-Kendayan #language-Kanyok #language-Kodi #language-Kacipo-Bale Suri #language-Kubi #language-Cogui #language-Koyo #language-Komi-Permyak #language-Konkani (macrolanguage) #language-Kol (Papua New Guinea) #language-Kongo #language-Konzo #language-Waube #language-Kota (Gabon) #language-Korean #language-Kosraean #language-Lagwan #language-Koke #language-Kudu-Camo #language-Koyukon #language-Korak #language-Curripaco #language-Kpelle #language-Komba #language-Kapingamarangi #language-Kplang #language-Karajá #language-Kpan #language-Kpala #language-Koho #language-Kepkiriwát #language-Ikposo #language-Korupun-Sela #language-Korafe-Yegha #language-Tehit #language-Karata #language-Kafoa #language-Komi-Zyrian #language-Kobon #language-Mountain Koiali #language-Koryak #language-Kupsabiny #language-Mum #language-Kovai #language-Doromu-Koki #language-Kalagan #language-Kakabai #language-Koitabu #language-Koromira #language-Kyenele #language-Kaonde #language-Eastern Krahn #language-Krenak #language-Kimaragang #language-Northern Kissi #language-Klias River Kadazan #language-Seroa #language-Okolod #language-Kandas #language-Mser #language-Koorete #language-Korana #language-Karkin #language-Karachay-Balkar #language-Kairui-Midiki #language-Panará #language-Koro (Vanuatu) #language-Kurama #language-Krio #language-Kinaray-A #language-Kerek #language-Karelian #language-Durop #language-Krung #language-Gbaya (Sudan) #language-Kurukh #language-Karon #language-Kryts #language-Sota Kanum #language-Shambala #language-Southern Kalinga #language-Kuanua #language-Kuni #language-Bafia #language-Kusaghe #language-Kölsch #language-Krisa #language-Uare #language-Kansa #language-Kumalu #language-Kumba #language-Kasiguranin #language-Kaba #language-Kwaami #language-Borong #language-Southern Kisi #language-Winyé #language-Kusu #language-S'gaw Karen #language-Kedang #language-Kodaku #language-Kambaata #language-Kokata #language-Nubri #language-Kalkutung #language-Karanga #language-North Muyu #language-Plapo Krumen #language-Kaniet #language-Kurti #language-Karitiâna #language-Kuot #language-Kaduo #language-South Muyu #language-Ketum #language-Kituba (Democratic Republic of Congo) #language-Eastern Katu #language-Kato #language-Kaxararí #language-Juǀʼhoan #language-Kuanyama #language-Kutep #language-'Auhelawa #language-Kuman (Papua New Guinea) #language-Kupa #language-Kuikúro-Kalapálo #language-Kuria #language-Kulere #language-Kumyk #language-Kunama #language-Kumukio #language-Kunimaipa #language-Karipuna #language-Kusaal #language-Kutenai #language-Upper Kuskokwim #language-Kukatja #language-Kuuku-Ya'u #language-Kunza #language-Bagvalal #language-Kubu #language-Kove #language-Kui (Indonesia) #language-Kalabakan #language-Kabalai #language-Kuni-Boazi #language-Komodo #language-Psikye #language-Kendem #language-Border Kuna #language-Dobel #language-Geba Karen #language-Kerinci #language-Yinbaw Karen #language-Kola #language-Wersing #language-Yintale Karen #language-Dâw #language-Kwa #language-Kwaio #language-Kwerba #language-Kwara'ae #language-Sara Kaba Deme #language-Kowiai #language-Awa-Cuaiquer #language-Kwanga #language-Kwakiutl #language-Kofyar #language-Kwangali #language-Kwomtari #language-Kodia #language-Kwese #language-Sara Kaba Náà #language-San Salvador Kongo #language-Kwadi #language-Kairiru #language-Krobu #language-Konso #language-Brunei #language-Manumanaw Karen #language-Karo (Ethiopia) #language-Keningau Murut #language-Kulfa #language-Northern Khmer #language-Kanowit-Tanjong Melanau #language-Kanoé #language-Kuvi #language-Konai #language-Kerewo #language-Kwaya #language-Kyaka #language-Krache #language-Kouya #language-Karok #language-Kiput #language-Karao #language-Kamayo #language-Kalapuya #language-Kpatili #language-Kelon #language-Kenga #language-Kuruáya #language-Baram Kayan #language-Kayagar #language-Western Kayah #language-Rapoisi #language-Kayabí #language-Western Karaboro #language-Kaibobo #language-Bondoukou Kulango #language-Kadai #language-Kosena #language-Da'a Kaili #language-Kikai #language-Kelabit #language-Kazukuru #language-Kayeli #language-Kais #language-Kokola #language-Kaningi #language-Kaidipang #language-Kaike #language-Karang #language-Sugut Dusun #language-Kayupulau #language-Karirí-Xocó #language-Kamarian #language-Kalabra #language-Southern Subanen #language-Lacandon #language-Ladino #language-Pattani #language-Lafofa #language-Rangi #language-Lahnda #language-Lambya #language-Lango (Uganda) #language-Lamba #language-Laru #language-Lao #language-Laka (Chad) #language-Qabiao #language-Larteh #language-Lama (Togo) #language-Latin #language-Laba #language-Lauje #language-Tiwa #language-Aribwatsa #language-Label #language-Lakkia #language-Lak #language-Tinani #language-Ladakhi #language-Central Bontok #language-Libon Bikol #language-Rmeet #language-Laven #language-Wampar #language-Lohorung #language-Lachi #language-Labu #language-Lavatbura-Lamusong #language-Tolaki #language-Lawangan #language-Lamalama #language-Lardil #language-Legenyem #language-Luchazi #language-Tungag #language-Western Lawa #language-Luhu #language-Lisabata-Nuniali #language-Kla-Dan #language-Dũya #language-Laari #language-Lemoro #language-Leelau #language-Kaan #language-Landoma #language-Láadan #language-Lega-Shabunda #language-Lala-Bisa #language-Leco #language-Lendu #language-Lyélé #language-Lelemi #language-Lenje #language-Lemio #language-Lengola #language-Leipon #language-Nomaande #language-Lenca #language-Lepcha #language-Lembena #language-Lenkau #language-Lese #language-Lesing-Gelimi #language-Kara (Papua New Guinea) #language-Lamma #language-Ledo Kaili #language-Luang #language-Lezghian #language-Lefa #language-Lingua Franca Nova #language-Lungga #language-Laghu #language-Lugbara #language-Laghuu #language-Lengilu #language-Lingarak #language-Wala #language-T'apo #language-Logba #language-Lengo #language-Pahi #language-Longgu #language-Laha (Viet Nam) #language-Laha (Indonesia) #language-Lahu Shi #language-Lhomi #language-Lahanan #language-Mlahsö #language-Lo-Toga #language-Lahu #language-West-Central Limba #language-Likum #language-Hlai #language-Nyindrou #language-Limbu #language-Ligbi #language-Lihir #language-Ligurian #language-Lillooet #language-Limburgan #language-Lingala #language-Liki #language-Sekpele #language-Libido #language-Liberian English #language-Lisu #language-Lithuanian #language-Logorik #language-Liv #language-Liabuku #language-Banda-Bambari #language-Libinza #language-Golpa #language-Rampi #language-Laiyolo #language-Li'o #language-Lampung Api #language-Lakalei #language-Kabras #language-Lakondê #language-Kenyi #language-Lakha #language-Laki #language-Laeko-Libuat #language-Lakon #language-Päri #language-Lakota #language-Kungkari #language-Lokoya #language-Lala-Roba #language-Lele (Guinea) #language-Ladin #language-Lele (Papua New Guinea) #language-Hermit #language-Lole #language-Teke-Laali #language-Lelak #language-Lilau #language-Lasalimu #language-Lele (Chad) #language-North Efate #language-Lau #language-East Limba #language-Merei #language-Limilngan #language-Pévé #language-Lamogai #language-Lambichhong #language-Lamkang #language-Hano #language-Lambadi #language-Lombard #language-Limbum #language-Lamalera #language-Lamenu #language-Lake Miwok #language-Laimbue #language-Lamboya #language-Langbashe #language-Mbalanhu #language-Lundayeh #language-Lanoh #language-Leningitij #language-South Central Banda #language-Lorediakarkar #language-Lamnso' #language-Longuda #language-Loloda #language-Lobi #language-Inonhan #language-Saluan #language-Logol #language-Logo #language-Laarim #language-Loma (Côte d'Ivoire) #language-Lou #language-Loko #language-Mongo #language-Loma (Liberia) #language-Malawi Lomwe #language-Lombo #language-Lopa #language-Téén #language-Loniu #language-Otuho #language-Louisiana Creole #language-Loke #language-Lozi #language-Lelepa #language-Lepki #language-Rara Bakati' #language-Northern Luri #language-Laurentian #language-Laragia #language-Lari #language-Laro #language-Larevat #language-Lemerig #language-Lasgerdi #language-Lishana Deni #language-Lusengo #language-Lish #language-Lashi #language-Saamia #language-Aruop #language-Late Middle Chinese #language-Latgalian #language-Leti (Indonesia) #language-Latundê #language-Luxembourgish #language-Luba-Lulua #language-Luba-Katanga #language-Aringa #language-Ludian #language-Luvale #language-Laua #language-Ganda #language-Luiseno #language-Luna #language-Olu'bo #language-Luimbi #language-Lunda #language-Luo (Kenya and Tanzania) #language-Lumbu #language-Lucumi #language-Laura #language-Lushai #language-Lushootseed #language-Luo (Cameroon) #language-Luyia #language-Maku'a #language-Lavukaleve #language-Standard Latvian #language-White Lachi #language-Eastern Lawa #language-Luwo #language-Lewo #language-Lyngngam #language-Luyana #language-Literary Chinese #language-Litzlitz #language-Laz #language-San Jerónimo Tecóatl Mazatec #language-Madurese #language-Bo-Rukul #language-Mafa #language-Magahi #language-Marshallese #language-Maithili #language-Jalapa De Díaz Mazatec #language-Makasar #language-Malayalam #language-Mam #language-Mandingo #language-Chiquihuitlán Mazatec #language-Marathi #language-Masai #language-San Francisco Matlatzinca #language-Huautla Mazatec #language-Sateré-Mawé #language-Mampruli #language-North Moluccan Malay #language-Central Mazahua #language-Western Bukidnon Manobo #language-Macushi #language-Dibabawon Manobo #language-Molale #language-Baba Malay #language-Mangseng #language-Ilianen Manobo #language-Nadëb #language-Maxakalí #language-Ombamba #language-Macaguán #language-Mbo (Cameroon) #language-Malayo #language-Maisin #language-Nukak Makú #language-Sarangani Manobo #language-Matigsalug Manobo #language-Mbula-Bwazza #language-Mbulungish #language-Maring #language-Mari (East Sepik Province) #language-Amoltepec Mixtec #language-Maca #language-Machiguenga #language-Bitur #language-Sharanahua #language-Matsés #language-Mapoyo #language-Maquiritari #language-Mese #language-Mvanip #language-Mbunda #language-Malaccan Creole Portuguese #language-Masana #language-Coatlán Mixe #language-Makaa #language-Ese #language-Menya #language-Mambai #language-Mengisa #language-Cameroon Mambila #language-Minanibai #language-Mawa (Chad) #language-Mpiemo #language-South Watut #language-Mawan #language-Mada (Nigeria) #language-Morigi #language-Male (Papua New Guinea) #language-Mbum #language-Maba (Chad) #language-Moksha #language-Maguindanaon #language-Mamvu #language-Mangbetu #language-Mayogo #language-Mbala #language-Mbole #language-Mandar #language-Maria (Papua New Guinea) #language-Mbere #language-Santa Lucía Monteverde Mixtec #language-Mbosi #language-Dizin #language-Male (Ethiopia) #language-Suruí Do Pará #language-Menka #language-Ikobi #language-Marra #language-Melpa #language-Mengen #language-Megam #language-Midob #language-Meyah #language-Mekeo #language-Central Melanau #language-Mangala #language-Mende (Sierra Leone) #language-Kedah Malay #language-Miriwoong #language-Merey #language-Meru #language-Mato #language-Motu #language-Mano #language-Maaka #language-Hassaniyya #language-Menominee #language-Pattani Malay #language-Bangka #language-Mba #language-Mendankwe-Nkwen #language-Morisyen #language-Naki #language-Mogofin #language-Matal #language-Wandala #language-Mefele #language-Putai #language-Marghi South #language-Cross River Mbembe #language-Mbe #language-Makassar Malay #language-Moba #language-Marrithiyel #language-Mokerang #language-Mbwela #language-Mandjak #language-Mulaha #language-Melo #language-Mayo #language-Mabaan #language-Middle Irish (900-1200) #language-Mararit #language-Morokodo #language-Moru #language-Mango #language-Maklew #language-Makhuwa-Meetto #language-Lijili #language-Mawes #language-Maleu-Kilenge #language-Mambae #language-Meta' #language-Eastern Magar #language-Malila #language-Mambwe-Lungu #language-Manda (Tanzania) #language-Mailu #language-Matengo #language-Matumbi #language-Mbunga #language-Mbugwe #language-Manda (India) #language-Mahongwe #language-Mocho #language-Mbugu #language-Besisi #language-Mamaa #language-Margu #language-Ma'di #language-Mogholi #language-Mungaka #language-Mauwake #language-Mócheno #language-Mashi (Zambia) #language-Mandan #language-Eastern Mari #language-Buru (Indonesia) #language-Mandahuaca #language-Digaro-Mishmi #language-Mbukushu #language-Maru #language-Ma'anyan #language-Mor (Mor Islands) #language-Miami #language-Atatláhuca Mixtec #language-Mi'kmaq #language-Mandaic #language-Ocotepec Mixtec #language-Mofu-Gudur #language-San Miguel El Grande Mixtec #language-Chayuco Mixtec #language-Abar #language-Mikasuki #language-Peñoles Mixtec #language-Alacatlatzala Mixtec #language-Minangkabau #language-Pinotepa Nacional Mixtec #language-Apasco-Apoala Mixtec #language-Mískito #language-Isthmus Mixe #language-Southern Puebla Mixtec #language-Cacaloxtepec Mixtec #language-Akoye #language-Mixtepec Mixtec #language-San Juan Colorado Mixtec #language-Northwest Maidu #language-Tu #language-Mwera (Nyasa) #language-Kim Mun #language-Mawak #language-Matukar #language-Medebur #language-Miship #language-Sauria Paharia #language-Karbi #language-Mahali #language-Mahican #language-Mbre #language-Siliput #language-Macedonian #language-Mawchi #language-Miya #language-Mak (China) #language-Dhatki #language-Mokilese #language-Mokole #language-Moklen #language-Kupang Malay #language-Moikodi #language-Bay Miwok #language-Malas #language-Silacayoapan Mixtec #language-Mafea #language-Kituba (Congo) #language-East Makian #language-Makasae #language-Malo #language-Cao Lan #language-Manambu #language-Mal #language-Malagasy #language-Mape #language-Miltu #language-Ilwana #language-Malua Bay #language-Mulam #language-Malango #language-Bargam #language-Western Maninkakan #language-Vame #language-Masalit #language-Maltese #language-To'abaita #language-Motlav #language-Moloko #language-Malfaxal #language-Mama #language-Momina #language-Maonan #language-Mae #language-Mundat #language-North Ambrym #language-Mehináku #language-Musar #language-Maii #language-Mamanwa #language-Mangga Buang #language-Siawi #language-Musak #language-Western Xiangxi Miao #language-Malalamai #language-Mmaala #language-Emae #language-Madak #language-Migaama #language-Mabaale #language-Mbula #language-Muna #language-Manchu #language-Mondé #language-Naba #language-Mundani #language-Eastern Mnong #language-Mono (Democratic Republic of Congo) #language-Manipuri #language-Munji #language-Mandinka #language-Tiale #language-Mapena #language-Min Bei Chinese #language-Minriq #language-Mono (USA) #language-Mansi #language-Rennell-Bellona #language-Mon #language-Manikion #language-Manyawa #language-Moni #language-Mwan #language-Mocoví #language-Mobilian #language-Innu #language-Mongondow #language-Mohawk #language-Mboi #language-Monzombo #language-Morori #language-Mangue #language-Mopán Maya #language-Mor (Bomberai Peninsula) #language-Moro #language-Mossi #language-Barí #language-Mogum #language-Mohave #language-Molima #language-Shekkacho #language-Mukulu #language-Mpoto #language-Malak Malak #language-Mangarrayi #language-Machinere #language-Majang #language-Marba #language-Maung #language-Mpade #language-Martu Wangka #language-Mbara (Chad) #language-Middle Watut #language-Yosondúa Mixtec #language-Mindiri #language-Migabac #language-Matís #language-Vangunu #language-Dadibi #language-Mian #language-Makuráp #language-Mungkip #language-Mapidian #language-Misima-Panaeati #language-Mapia #language-Mpi #language-Maba (Indonesia) #language-Mbuko #language-Mangole #language-Matepi #language-Momuna #language-Mamasa #language-Rajah Kabunsuwan Manobo #language-Mbelime #language-South Marquesan #language-Moronene #language-Modole #language-Manipa #language-Mander #language-West Makian #language-Mandari #language-Mosimo #language-Murupi #language-Mamuju #language-Manggarai #language-Pano #language-Mlabri #language-Marino #language-Maricopa #language-Elseng #language-Mising #language-Mara Chin #language-Maori #language-Western Mari #language-Hmwaveke #language-Mortlockese #language-Merlav #language-Cheke Holo #language-Mru #language-Morouas #language-North Marquesan #language-Maria (India) #language-Maragus #language-Marghi Central #language-Mono (Cameroon) #language-Mangareva #language-Maranao #language-Maremgi #language-Mandaya #language-Marind #language-Malay (macrolanguage) #language-Masbatenyo #language-Musey #language-Mekwei #language-Moraid #language-Masikoro Malagasy #language-Ma (Democratic Republic of Congo) #language-Mansaka #language-Molof #language-Agusan Manobo #language-Vurës #language-Mombum #language-West Masela #language-Musom #language-Mansoanka #language-Moresada #language-Aruamu #language-Momare #language-Cotabato Manobo #language-Munit #language-Mualang #language-Mono (Solomon Islands) #language-Murik (Papua New Guinea) #language-Una #language-Munggui #language-Maiwa (Papua New Guinea) #language-Moskona #language-Mbe' #language-Montol #language-Mator #language-Matagalpa #language-Totontepec Mixe #language-Wichí Lhamtés Nocten #language-Muong #language-Mewari #language-Yora #language-Mota #language-Tututepec Mixtec #language-Asaro'o #language-Nabi #language-Mundang #language-Mubi #language-Ajumbu #language-Media Lengua #language-Musgu #language-Mündü #language-Musi #language-Mugom #language-Multiple languages #language-Nyong #language-Malvi #language-Murle #language-Creek #language-Yaaku #language-Bo-Ung #language-Muyang #language-Mursi #language-Manam #language-Mattole #language-Mamboru #language-Marwari (Pakistan) #language-Peripheral Mongolian #language-Miyako #language-Minaveha #language-Marovo #language-Duri #language-Moere #language-Marau #language-Mpotovoro #language-Marfa #language-Tagal Murut #language-Meoswar #language-Indus Kohistani #language-Mesqan #language-Mwatebu #language-Juwal #language-Are #language-Mwera (Chimwera) #language-Murrinh-Patha #language-Aiklep #language-Mouk-Aria #language-Labo #language-Mirandese #language-Sar #language-Nyamwanga #language-Central Maewo #language-Kala Lagaw Ya #language-Mün Chin #language-Marwari #language-Moken #language-Mentawai #language-Hmong Daw #language-Tezoatlán Mixtec #language-Manyika #language-Modang #language-Mele-Fila #language-Malgbe #language-Mbangala #language-Mozarabic #language-Miju-Mishmi #language-Monumbo #language-Meramera #language-Moi (Indonesia) #language-Tlahuitoltepec Mixe #language-Juquila Mixe #language-Murik (Malaysia) #language-Jamiltepec Mixtec #language-Mada (Cameroon) #language-Metlatónoc Mixtec #language-Namo #language-Mahou #language-Southeastern Nochixtlán Mixtec #language-Central Masela #language-Burmese #language-Mbay #language-Myene #language-Bambassi #language-Manta #language-Makah #language-Mangayat #language-Mamara Senoufo #language-Moma #language-Me'en #language-Anfillo #language-Pirahã #language-Muniche #language-Mesmes #language-Mundurukú #language-Erzya #language-Muyuw #language-Masaaba #language-Macuna #language-Classical Mandaic #language-Tumzabt #language-Malimba #language-Morawa #language-Wichí Lhamtés Güisnay #language-Ixcatlán Mazatec #language-Manya #language-Nigeria Mambila #language-Mumuye #language-Mazanderani #language-Matipuhy #language-Movima #language-Mori Atas #language-Marúbo #language-Macanese #language-Mintil #language-Inapang #language-Manza #language-Deg #language-Maiadomu #language-Southern Nambikuára #language-Narak #language-Naka'ela #language-Nabak #language-Naga Pidgin #language-Nalu #language-Nakanai #language-Nalik #language-Ngan'gityemerri #language-Neapolitan #language-Khoekhoe #language-Iguta #language-Naasioi #language-Ca̱hungwa̱rya̱ #language-Nauru #language-Navajo #language-Nawuri #language-Nakwi #language-Ngarrindjeri #language-Coatepec Nahuatl #language-Nyemba #language-Ndoe #language-Chang Naga #language-Konyak Naga #language-Ngamo #language-Mao Naga #language-Ngarinyman #language-Nake #language-South Ndebele #language-Ngbaka Ma'bo #language-Kuri #language-Nkukoli #language-Nnam #language-Nggem #language-Numana #language-Rongmei Naga #language-Ngamambo #language-Southern Ngbandi #language-Ningera #language-Iyo #language-Central Nicobarese #language-Ponam #language-Nachering #language-Yale #language-Notsi #language-Nisga'a #language-Central Huasteca Nahuatl #language-Classical Nahuatl #language-Northern Puebla Nahuatl #language-Na-kara #language-Michoacán Nahuatl #language-Nambo #language-Nauna #language-Sibe #language-Ncane #language-Chothe Naga #language-Chumburung #language-Central Puebla Nahuatl #language-Natchez #language-Ndasa #language-Kenswei Nsei #language-Ndau #language-Nde-Nsele-Nta #language-North Ndebele #language-Ndengereko #language-Ndali #language-Samba Leko #language-Ndamba #language-Ndam #language-Ngundi #language-Ndonga #language-Ndombe #language-Ndoola #language-Low German #language-Ndunga #language-Dugun #language-Ndut #language-Nduga #language-Lutos #language-Ndogo #language-Toura (Côte d'Ivoire) #language-Nedebang #language-Nêlêmwa-Nixumwak #language-Negidal #language-Nyenkha #language-Neko #language-Nemi #language-Nengone #language-Ná-Meo #language-North Central Mixe #language-Yahadian #language-Nete #language-Neo #language-Nyaheun #language-Newari #language-Neyo #language-Nez Perce #language-Dhao #language-Ahwai #language-Ayiwo #language-Nafaanra #language-Mfumte #language-Ngbaka #language-Northern Ngbandi #language-Ngombe (Democratic Republic of Congo) #language-Ngemba #language-Ngbaka Manza #language-Nǁng #language-Ngizim #language-Ngie #language-Dalabon #language-Lomwe #language-Ngwo #language-Ngulu #language-Ngurimi #language-Engdewu #language-Gvoko #language-Kriang #language-Guerrero Nahuatl #language-Nagumi #language-Nggwahyi #language-Tibea #language-Ngungwel #language-Nhanda #language-Beng #language-Tabasco Nahuatl #language-Chiripá #language-Eastern Huasteca Nahuatl #language-Nhuwala #language-Tetelcingo Nahuatl #language-Zacatlán-Ahuacatlán-Tepetzintla Nahuatl #language-Morelos Nahuatl #language-Central Nahuatl #language-Takuu #language-Isthmus-Pajapan Nahuatl #language-Huaxcaleca Nahuatl #language-Naro #language-Ometepec Nahuatl #language-Noone #language-Temascaltepec Nahuatl #language-Western Huasteca Nahuatl #language-Isthmus-Mecayapan Nahuatl #language-Northern Oaxaca Nahuatl #language-Nias #language-Nakame #language-Ngandi #language-Niellim #language-Nek #language-Ngalakgan #language-Nyiha (Tanzania) #language-Nii #language-Ngaju #language-Nila #language-Nilamba #language-Ninzo #language-Nganasan #language-Nandi #language-Nimboran #language-Nimi #language-Southeastern Kolami #language-Niuean #language-Gilyak #language-Nimo #language-Ngiti #language-Ningil #language-Nzanyi #language-Nocte Naga #language-Lotha Naga #language-Gudanji #language-Njen #language-Njalgulgule #language-Angami Naga #language-Liangmai Naga #language-Ao Naga #language-Njerep #language-Ngadjunmaya #language-Njyem #language-Nyishi #language-Nkoya #language-Khoibu Naga #language-Nkongho #language-Koireng #language-Duke #language-Inpui Naga #language-Nekgini #language-Khezha Naga #language-Thangal Naga #language-Nakai #language-Nokuku #language-Nkonya #language-Niuatoputapu #language-Nukuoro #language-North Asmat #language-Bouna Kulango #language-Nkutu #language-Nkoroo #language-Nkari #language-Ngombale #language-Nalca #language-Dutch #language-East Nyala #language-Gela #language-Grangali #language-Nyali #language-Nihali #language-Ngul #language-Nchumbulu #language-Nahali #language-Nyamal #language-Nalögo #language-Maram Naga #language-Big Nambas #language-Ngam #language-Ndumu #language-Mzieme Naga #language-Tangkhul Naga (India) #language-Kwasio #language-Monsang Naga #language-Ngombe (Central African Republic) #language-Namakura #language-Ndemli #language-Manangba #language-ǃXóõ #language-Nimanbur #language-Letemboi #language-Namonuito #language-Northeast Maidu #language-Ngamini #language-Nimoa #language-Nama (Papua New Guinea) #language-Namuyi #language-Nawdm #language-Nyangumarta #language-Nande #language-Nancere #language-West Ambae #language-Ngaing #language-Maring Naga #language-Ngiemboon #language-Nyangatom #language-Nankina #language-Northern Rengma Naga #language-Namia #language-Norwegian Nynorsk #language-Wancho Naga #language-Ngindo #language-Narungga #language-Nanticoke #language-Dwang #language-Nugunu (Australia) #language-Southern Nuni #language-Nyangga #language-Nda'nda' #language-Woun Meu #language-Norwegian Bokmål #language-Nuk #language-Northern Thai #language-Nimadi #language-Nogai #language-Nomu #language-Nonuya #language-Nooksack #language-Nomlaki #language-nom #language-Old Norse #language-Numanggang #language-Ngongo #language-Norwegian #language-Nomatsiguenga #language-Ewage-Notu #language-Novial #language-Nyambo #language-Nayi #language-Phom Naga #language-Nepali (individual language) #language-Southeastern Puebla Nahuatl #language-Mondropolon #language-Pochuri Naga #language-Nipsan #language-Napu #language-Southern Nago #language-Kura Ede Nago #language-Ndom #language-N'Ko #language-Ngom #language-Nara #language-Southern Rengma Naga #language-Jèrriais #language-Narango #language-Chokri Naga #language-Ngarla #language-Ngarluma #language-Narom #language-Norn #language-Northern Kalapuya #language-Narua #language-Lala #language-Sangtam Naga #language-Lower Nossob #language-Nsenga #language-Ngasa #language-Naskapi #language-Sumi Naga #language-Nehan #language-Pedi #language-Northern Sierra Miwok #language-Nali #language-Tase Naga #language-Sierra Negra Nahuatl #language-Navut #language-Nsongo #language-Nasal #language-Nisenan #language-Natioro #language-Ngaanyatjarra #language-Ikoma-Nata-Isenye #language-Nateni #language-Ntomba #language-Northern Tepehuan #language-Natügu #language-Nottoway #language-Mantsi #language-Natanzi #language-Yuanga #language-Nukuini #language-Ngala #language-Nusu #language-Nungali #language-Ngumbi #language-Nyole #language-Nuu-chah-nulth #language-Nusa Laut #language-Niuafo'ou #language-Anong #language-Nguôn #language-Nupe-Nupe-Tako #language-Nukuria #language-Nuer #language-Nung (Viet Nam) #language-Nguluwan #language-Mehek #language-Nunggubuyu #language-Tlamacazapa Nahuatl #language-Nasarian #language-Nyokon #language-Nawathinehena #language-Nyabwa #language-Ngwe #language-Southwest Tanna #language-Nawaru #language-Nottoway-Meherrin #language-Nauete #language-Nage #language-Ngad'a #language-Nindi #language-South Nuaulu #language-Ngawun #language-Naxi #language-Ninggerum #language-Nafri #language-Nyanja #language-Nyangbo #language-Nyore #language-Nyengo #language-Giryama #language-Nyikina #language-Ama (Sudan) #language-Nyanga #language-Nyaneka #language-Nyeu #language-Nyamwezi #language-Nyankole #language-Nyoro #language-Nyang'i #language-Nayini #language-Nyungar #language-Nyawaygi #language-Nyungwe #language-Nyulnyul #language-Nganyaywana #language-Nyakyusa-Ngonde #language-Tigon Mbembe #language-Njebi #language-Nzadi #language-Nzima #language-Nzakara #language-Zeme Naga #language-Nzakambay #language-Nanga Dama Dogon #language-Orok #language-Oroch #language-Old Aramaic (up to 700 BCE) #language-Obispeño #language-Southern Bontok #language-Moabite #language-Obo Manobo #language-Old Burmese #language-Old Breton #language-Ocaina #language-Old Chinese #language-Occitan (post 1500) #language-Old Cornish #language-Atzingo Matlatzinca #language-Old Dutch #language-Odual #language-Ofo #language-Old Frisian #language-Efutop #language-Ogbia #language-Ogbah #language-Old Georgian #language-Khana #language-Ogbronuagum #language-Old Hittite #language-Oirata #language-Northwestern Ojibwa #language-Central Ojibwa #language-Eastern Ojibwa #language-Ojibwa #language-Old Japanese #language-Severn Ojibwa #language-Ontong Java #language-Western Ojibwa #language-Okanagan #language-Okobo #language-Okodia #language-Okpe (Southwestern Edo) #language-Okiek #language-Oko-Juwoi #language-Oki-No-Erabu #language-Old Korean (3rd-9th cent.) #language-Kirike #language-Oko-Eni-Osayen #language-Oku #language-Orokaiva #language-Walungge #language-Mochi #language-Olekha #language-Oloma #language-Livvi #language-Old Lithuanian #language-Omaha-Ponca #language-East Ambae #language-Mochica #language-Omagua #language-Omok #language-Ombo #language-Utarmbung #language-Old Marathi #language-Omotik #language-Omurano #language-South Tairora #language-Old Mon #language-Ona #language-Lingao #language-Oneida #language-Olo #language-Onin #language-Onjob #language-Onobasulu #language-Onondaga #language-Sartang #language-Ono #language-Unua #language-Old Nubian #language-Tohono O'odham #language-Ong #language-Önge #language-Okpamheri #language-Kopkaka #language-Oksapmin #language-Opao #language-Opata #language-Ofayé #language-Oroha #language-Orma #language-Orejón #language-Oring #language-Oroqen #language-Oriya (macrolanguage) #language-Oromo #language-Orokolo #language-Oruma #language-Adivasi Oriya #language-Ormuri #language-Old Russian #language-Oro Win #language-Oro #language-Odia #language-Ormu #language-Osage #language-Oscan #language-Osing #language-Old Spanish #language-Ossetian #language-Old Saxon #language-Ottoman Turkish (1500-1928) #language-Ot Danum #language-Mezquital Otomi #language-Oti #language-Old Turkish #language-Tilapa Otomi #language-Eastern Highland Otomi #language-Querétaro Otomi #language-Otoro #language-Estado de México Otomi #language-Temoaya Otomi #language-Otuke #language-Ottawa #language-Ixtenco Otomi #language-Tagargrent #language-Glio-Oubi #language-Ouma #language-Elfdalian #language-Owiniga #language-Old Welsh #language-Oy #language-Oyda #language-Wayampi #language-Koonzime #language-Parecís #language-Pacoh #language-Paumarí #language-Pagibete #language-Paranawát #language-Pangasinan #language-Tenharim #language-Pe #language-Parakanã #language-Pahlavi #language-Pampanga #language-Panjabi #language-Northern Paiute #language-Papiamento #language-Parya #language-Panamint #language-Papasena #language-Palauan #language-Pakaásnovos #language-Pawnee #language-Pech #language-Pankararú #language-Páez #language-Mezontla Popoloca #language-Coyotepec Popoloca #language-Paraujano #language-E'ñapa Woromaipu #language-Parkwa #language-Kpasam #language-Papel #language-Badyara #language-Pangwa #language-Central Pame #language-Southern Pashto #language-Northern Pashto #language-Pnar #language-Pyu (Papua New Guinea) #language-Santa Inés Ahuatempan Popoloca #language-Pear #language-Bouyei #language-Picard #language-Ruching Palaung #language-Duruwa #language-Parenga #language-Paite Chin #language-Nigerian Pidgin #language-Piti #language-Pacahuara #language-Anam #language-Pennsylvania German #language-Padoe #language-Plautdietsch #language-Kayan #language-Eastern Pomo #language-Mala (Papua New Guinea) #language-Taje #language-Northeastern Pomo #language-Pengo #language-Bonan #language-Chichimeca-Jonaz #language-Northern Pomo #language-Penchal #language-Phende #language-Old Persian (ca. 600-400 B.C.) #language-Kunja #language-Southern Pomo #language-Iranian Persian #language-Petats #language-Eastern Penan #language-Pere #language-Pfaelzisch #language-Sudanese Creole Arabic #language-Gāndhārī #language-Rerep #language-Paelignian #language-Pagu #language-Pa-Hng #language-Phuong #language-Phake #language-Phalura #language-Phoenician #language-Phunoi #language-Phu Thai #language-Pima Bajo #language-Yine #language-Pinji #language-Piaroa #language-Piro #language-Pingelapese #language-Pitcairn-Norfolk #language-Pijao #language-Yom #language-Powhatan #language-Piame #language-Piapoco #language-Pero #language-Piratapuyo #language-Pijin #language-Pitta Pitta #language-Pintupi-Luritja #language-Pileni #language-Pimbwe #language-Piu #language-Pije #language-Pitjantjatjara #language-Ardhamāgadhī Prākrit #language-Pokomo #language-Paekche #language-Pak-Tong #language-Pankhu #language-Pakanha #language-Pökoot #language-Pukapuka #language-Maleng #language-Paku #language-Miani #language-Polonombauk #language-Central Palawano #language-Palu'e #language-Pilagá #language-Paulohi #language-Pali #language-plj #language-Kohistani Shina #language-Shwe Palaung #language-Palenquero #language-Oluta Popoluca #language-Palaic #language-Palaka Senoufo #language-San Marcos Tlacoyalco Popoloca #language-Plateau Malagasy #language-Palikúr #language-Southwest Palawano #language-Brooke's Point Palawano #language-Bolyu #language-Paluan #language-Paama #language-Pambia #language-Pamona #language-Māhārāṣṭri Prākrit #language-Northern Pumi #language-Pamlico #language-Lingua Franca #language-Pomo #language-Pam #language-Pom #language-Northern Pame #language-Paynamar #language-Piemontese #language-Tuamotuan #language-Plains Miwok #language-Papuan Malay #language-Southern Pame #language-Punan Bah-Biau #language-Western Panjabi #language-Western Penan #language-Pangu #language-Penrhyn #language-Aoheng #language-Pinjarup #language-Pinai-Hagahai #language-Panobo #language-Pancana #language-Pana (Burkina Faso) #language-Panim #language-Ponosakan #language-Pontic #language-Jiongnai Bunu #language-Pinigura #language-Banyjima #language-Pinyin #language-Poqomam #language-San Juan Atzingo Popoloca #language-Poke #language-Potiguára #language-Poqomchi' #language-Highland Popoluca #language-Pokangá #language-Polish #language-Southeastern Pomo #language-Pohnpeian #language-Central Pomo #language-Texistepec Popoluca #language-Portuguese #language-Sayula Popoluca #language-Potawatomi #language-Upper Guinea Crioulo #language-San Felipe Otlaltepec Popoloca #language-Polabian #language-Pogolo #language-Papi #language-Paipai #language-Uma #language-Pipil #language-Papuma #language-Papapana #language-Folopa #language-Pei #language-Pare #language-Papora #language-Pa'a #language-Malecite-Passamaquoddy #language-Parachi #language-Parsi-Dari #language-Principense #language-Paranan #language-Prussian #language-Porohanon #language-Paicî #language-Parauk #language-Kibiri #language-Prasuni #language-Old Provençal (to 1500) #language-Ashéninka Perené #language-Puri #language-Dari #language-Puragi #language-Parawen #language-Purik #language-Asue Awyu #language-Central Malay #language-Southwest Pashai #language-Southeast Pashai #language-Pauserna #language-Panasuan #language-Pasi #language-Kaulong #language-Central Pashto #language-Sauraseni Prākrit #language-Port Sandwich #language-Pai Tavytera #language-Pataxó Hã-Ha-Hãe #language-Pindiini #language-Patani #language-Zo'é #language-Patep #language-Piamatsina #language-Bambam #language-Port Vato #language-Western Highland Purepecha #language-Purum #language-Punan Merap #language-Puelche #language-Puinave #language-Punan Tubu #language-Puma #language-Puoc #language-Pulabu #language-Puquina #language-Puruborá #language-Punu #language-Puluwatese #language-Purisimeño #language-Pawaia #language-Gapapaiwa #language-Patwin #language-Molbog #language-Paiwan #language-Pwo Western Karen #language-Fyam #language-Poyanáwa #language-Puyuma #language-Pyen #language-Quapaw #language-Huallaga Huánuco Quechua #language-K'iche' #language-Quechua #language-Lambayeque Quechua #language-Chimborazo Highland Quichua #language-South Bolivian Quechua #language-Quileute #language-Chachapoyas Quechua #language-North Bolivian Quechua #language-Sipacapense #language-Quinault #language-Southern Pastaza Quechua #language-Santiago del Estero Quichua #language-Sacapulteco #language-Tena Lowland Quichua #language-Yauyos Quechua #language-Ayacucho Quechua #language-Cusco Quechua #language-Ambo-Pasco Quechua #language-Cajamarca Quechua #language-Eastern Apurímac Quechua #language-Imbabura Highland Quichua #language-Cajatambo North Lima Quechua #language-North Junín Quechua #language-Napo Lowland Quechua #language-San Martín Quechua #language-Huaylla Wanca Quechua #language-Queyu #language-Northern Pastaza Quichua #language-Corongo Ancash Quechua #language-Huaylas Ancash Quechua #language-Kuman (Russia) #language-Kwalhioqua-Tlatskanai #language-Chiquián Ancash Quechua #language-Northern Conchucos Ancash Quechua #language-Southern Conchucos Ancash Quechua #language-Puno Quechua #language-Qashqa'i #language-Cañar Highland Quichua #language-Southern Qiang #language-Arequipa-La Unión Quechua #language-Jauja Wanca Quechua #language-Quenya #language-Quiripi #language-Dungmali #language-Camling #language-Rasawa #language-Rade #language-Western Meohang #language-Logooli #language-Rabha #language-Ramoaaina #language-Rajasthani #language-Tulu-Bohuai #language-Ralte #language-Canela #language-Riantana #language-Rao #language-Rapanui #language-Rarotongan #language-Tegali #language-Raute #language-Sampang #language-Rawang #language-Rapa #language-Rahambuu #language-Rumai Palaung #language-Barababaraba #language-Réunion Creole French #language-Rerau #language-Rembong #language-Rejang Kayan #language-Kara (Tanzania) #language-Rejang #language-Rendille #language-Remo #language-Rengao #language-Reshe #language-Reyesano #language-Roria #language-Rangkas #language-Romagnol #language-Resígaro #language-Southern Roglai #language-Rohingya #language-Yahang #language-Tarifit #language-Riang Lang #language-Nyaturu #language-Nungu #language-Ribun #language-Ritharrngu #language-Riung #language-Raji #language-Rajbanshi #language-Rikbaktsa #language-Rakahanga-Manihiki #language-Rakhine #language-Marka #language-Rama #language-Rembarrnga #language-Carpathian Romani #language-Angloromani #language-Kalo Finnish Romani #language-Traveller Norwegian #language-Murkim #language-Lomavren #language-Romkun #language-Baltic Romani #language-Roma #language-Balkan Romani #language-Sinte Romani #language-Rempi #language-Caló #language-Domari #language-Tavringer Romani #language-Romanova #language-Welsh Romani #language-Vlax Romani #language-Ruund #language-Ronga #language-Roon #language-Rongpo #language-Nari Nari #language-Rungwa #language-Tae' #language-Rogo #language-Ronji #language-Rombo #language-Northern Roglai #language-Romansh #language-Romblomanon #language-Romany #language-Romanian #language-Rotokas #language-Kriol #language-Runga #language-Repanbitip #language-Rapting #language-Ririo #language-Waima #language-Romano-Serbian #language-Ratahan #language-Rotuman #language-Rathawi #language-Gungu #language-Rusyn #language-Luguru #language-Roviana #language-Rufiji #language-Che #language-Rundi #language-Istro Romanian #language-Macedo-Romanian #language-Megleno Romanian #language-Russian #language-Rutul #language-Lanas Lobu #language-Rawo #language-Rwa #language-Amba (Uganda) #language-Rawa #language-Marwari (India) #language-Northern Amami-Oshima #language-Yaeyama #language-Central Okinawan #language-Buglere #language-Meskwaki #language-Sandawe #language-Sabanê #language-Safaliba #language-Sango #language-Yakut #language-Sahu #language-Sake #language-Samaritan Aramaic #language-Sanskrit #language-Sause #language-Samburu #language-Saraveca #language-Sasak #language-Santali #language-Saleman #language-Saafi-Saafi #language-Sawi #language-Sa #language-Saya #language-Saurashtra #language-Ngambay #language-Simbo #language-Kele (Papua New Guinea) #language-Southern Samo #language-Saliba #language-Chabu #language-Seget #language-Sori-Harengan #language-Seti #language-Safwa #language-Botolan Sambal #language-Sagala #language-Sabüm #language-Sangu (Tanzania) #language-Sileibi #language-Sembakung Murut #language-Subiya #language-Kimki #language-Stod Bhoti #language-Sabine #language-Simba #language-Chut #language-Dongxiang #language-Sanggau #language-Sri Lankan Creole Malay #language-Sadri #language-Shina #language-Sicilian #language-Scots #language-Hyolmo #language-Sa'och #language-North Slavey #language-Shumcho #language-Sheni #language-Sha #language-Toraja-Sa'dan #language-Sassarese Sardinian #language-Surubu #language-Savi #language-Southern Kurdish #language-Suundi #language-Gallurese Sardinian #language-Bukar-Sadung Bidayuh #language-Sherdukpen #language-Semandang #language-Sened #language-Shuadit #language-Sallands #language-Semai #language-Sechelt #language-Sedang #language-Seneca #language-Cebaara Senoufo #language-Sena #language-Seri #language-Sene #language-Sekani #language-Selkup #language-Nanerigé Sénoufo #language-Suarmin #language-Senara Sénoufo #language-Serrano #language-Koyraboro Senni Songhai #language-Sentani #language-Serui-Laut #language-Nyarafolo Senoufo #language-Sewa Bay #language-Secoya #language-Old Irish (to 900) #language-Kipsigis #language-Surigaonon #language-Segai #language-Shughni #language-Suga #language-Singa #language-Singpho #language-Sangisari #language-Samogitian #language-Brokpake #language-Sebat Bet Gurage #language-Sanglechi #language-Sursurunga #language-Shall-Zwall #language-Ninam #language-Sheko #language-Shua #language-Shoshoni #language-Tachelhit #language-Shatt #language-Shilluk #language-Shendu #language-Shan #language-Shanga #language-Shipibo-Conibo #language-Sala #language-Shi #language-Shuswap #language-Shasta #language-Chadian Arabic #language-Shehri #language-Shwai #language-She #language-Tachawit #language-Syenara Senoufo #language-Akkala Sami #language-Sebop #language-Sidamo #language-Simaa #language-Siamou #language-Paasaal #language-Shom Peng #language-Numbami #language-Sikiana #language-Tumulung Sisaala #language-Mende (Papua New Guinea) #language-Sinhala #language-Sikkimese #language-Sonia #language-Siri #language-Siuslaw #language-Sinagen #language-Siwai #language-Sumau #language-Sivandi #language-Siwi #language-Epena #language-Kildin Sami #language-Pite Sami #language-Assangori #language-Kemi Sami #language-Sajalong #language-Mapun #language-Sindarin #language-Xibe #language-Siar-Lak #language-Senhaja De Srair #language-Ter Sami #language-Ume Sami #language-Shawnee #language-Skagit #language-Saek #language-Ma Manda #language-Southern Sierra Miwok #language-Seke (Vanuatu) #language-Sakirabiá #language-Sakalava Malagasy #language-Sikule #language-Sika #language-Seke (Nepal) #language-Kutong #language-Saraiki #language-Maia #language-Sakata #language-Sakao #language-Skou #language-Skepi Creole Dutch #language-Seko Padang #language-Sikaiana #language-Sekar #language-Sáliba #language-Selungai Murut #language-Southern Puget Sound Salish #language-Lower Silesian #language-Salumá #language-Slovak #language-Salt-Yui #language-Pangutaran Sama #language-Salinan #language-Lamaholot #language-Salar #language-Selaru #language-Slovenian #language-Sialum #language-Selayar #language-Ma'ya #language-Southern Sami #language-Simbari #language-Som #language-Northern Sami #language-Simbali #language-Lule Sami #language-Bolinao #language-Central Sama #language-Inari Sami #language-Samoan #language-Samaritan #language-Samo #language-Simeulue #language-Skolt Sami #language-Sumbawa #language-Samba #language-Semnani #language-Shona #language-Sinaugoro #language-Sindhi #language-Bau Bidayuh #language-Noon #language-Sanga (Democratic Republic of Congo) #language-Sensi #language-Soninke #language-Sangil #language-Siona #language-Snohomish #language-Siane #language-Sangu (Gabon) #language-Sihan #language-South West Bay #language-Senggi #language-Sa'ban #language-Selee #language-Sam #language-Saniyo-Hiyewe #language-Kou #language-Sobei #language-So (Democratic Republic of Congo) #language-Songoora #language-Songomeno #language-Sogdian #language-Aka #language-Sokoro #language-Solos #language-Somali #language-Songe #language-Kanasi #language-Somrai #language-Seeku #language-Southern Sotho #language-Southern Thai #language-Sonsorol #language-Sowanda #language-Temi #language-Spanish #language-Sepa (Indonesia) #language-Sapé #language-Saep #language-Sepa (Papua New Guinea) #language-Saponi #language-Selepet #language-Akukem #language-Sanapaná #language-Spokane #language-Supyire Senoufo #language-Saparua #language-Saposa #language-Sapuan #language-South Picene #language-Sabaot #language-Shama-Sambuga #language-Albanian #language-Susquehannock #language-Sorkhei #language-Sou #language-Siculo Arabic #language-Soqotri #language-Squamish #language-Saruga #language-Sora #language-Logudorese Sardinian #language-Sardinian #language-Nafi #language-Sarikoli #language-Siriano #language-Serudung Murut #language-Isirawa #language-Saramaccan #language-Sranan Tongo #language-Campidanese Sardinian #language-Serbian #language-Sirionó #language-Serer #language-Sarsi #language-Suruí #language-Southern Sorsoganon #language-Serua #language-Sera #language-Shahmirzadi #language-Southern Sama #language-Suba-Simbiti #language-Siroi #language-Balangingi #language-Thao #language-Seimat #language-Sausi #language-Sunam #language-Western Sisaala #language-Semnam #language-Sissano #language-Spanish Sign Language #language-So'a #language-Sô #language-Sinasina #language-Susuami #language-Shark Bay #language-Swati #language-Samberigi #language-Saho #language-Sengseng #language-Liana-Seti #language-Seta #language-Trieng #language-Shelta #language-Bulo Stieng #language-Matya Samo #language-Arammba #language-Stellingwerfs #language-Owa #language-Stoney #language-Southeastern Tepehuan #language-Saterfriesisch #language-Straits Salish #language-Shumashti #language-Samtao #language-Silt'e #language-Satawalese #language-Sulka #language-Suku #language-Western Subanon #language-Suena #language-Suganga #language-Suki #language-Shubi #language-Sukuma #language-Sundanese #language-Tirmaga-Chai Suri #language-Mwaghavul #language-Susu #language-Subtiaba #language-Puroik #language-Sumbwa #language-Sumerian #language-Suyá #language-Sunwar #language-Svan #language-Ulau-Suain #language-Vincentian Creole English #language-Serili #language-Savosavo #language-Maore Comorian #language-Congo Swahili #language-Swedish #language-Sere #language-Swabian #language-Swahili (individual language) #language-Sui #language-Sira #language-Samosa #language-Sawknah #language-Shanenawa #language-Suau #language-Sharwa #language-Seluwasan #language-Sawila #language-Suwawa #language-Shekhawati #language-Sowa #language-Suruahá #language-Sarua #language-Suba #language-Sighu #language-Shuhi #language-Samre #language-Sangir #language-Saaroa #language-Upper Saxon #language-Siang #language-Central Subanen #language-Classical Syriac #language-Seki #language-Sukur #language-Sylheti #language-Maya Samo #language-Senaya #language-Syriac #language-Sinyar #language-Kagate #language-Semelai #language-Ngalum #language-Semaq Beri #language-szd #language-Seze #language-Sengele #language-Silesian #language-Sula #language-Suabo #language-Isu (Fako Division) #language-Sawai #language-Lower Tanana #language-Tabassaran #language-Lowland Tarahumara #language-Tause #language-Tariana #language-Tapirapé #language-Tagoi #language-Tahitian #language-Eastern Tamang #language-Tal #language-Tamil #language-Tangale #language-Yami #language-Taabwa #language-Tamasheq #language-Central Tarahumara #language-Tay Boi #language-Tatar #language-Upper Tanana #language-Tatuyo #language-Atayal #language-Tocho #language-Aikanã #language-Takia #language-Kaki Ae #language-Tanimbili #language-Mandara #language-North Tairora #language-Dharawal #language-Gaam #language-Tiang #language-Calamian Tagbanwa #language-Tboli #language-Tagbu #language-Barro Negro Tunebo #language-Tawala #language-Taworta #language-Tumtum #language-Tanguat #language-Tembo (Kitembo) #language-Tubar #language-Tobo #language-Tagbanwa #language-Kapin #language-Tabaru #language-Ditammari #language-Ticuna #language-Tanacross #language-Datooga #language-Tafi #language-Southern Tutchone #language-Malinaltepec Me'phaa #language-Tamagario #language-Wára #language-Tchitchege #language-Taman (Myanmar) #language-Tanahmerah #language-Tichurong #language-Kaiy #language-Torres Strait Creole #language-T'en #language-Toda #language-Tulu #language-Thado Chin #language-Tagdal #language-Emberá-Tadó #language-Tai Nüa #language-Tiranige Diga Dogon #language-Talieng #language-Western Tamang #language-Thulung #language-Tomadino #language-Tajio #language-Tambas #language-Sur #language-Taruma #language-Tondano #language-Teme #language-Todrah #language-Doutai #language-Tetun Dili #language-Toro #language-Tandroy-Mahafaly Malagasy #language-Tadyawan #language-Temiar #language-Terik #language-Tepo Krumen #language-Huehuetla Tepehua #language-Teke-Tege #language-Tehuelche #language-Torricelli #language-Ibali Teke #language-Telugu #language-Timne #language-Tama (Colombia) #language-Teso #language-Tepecano #language-Temein #language-Tereno #language-Tengger #language-Tetum #language-Soo #language-Teor #language-Tewa (USA) #language-Tennet #language-Tulishi #language-Tanaina #language-Teribe #language-Ternate #language-Tobilung #language-Tigak #language-Ciwogai #language-Tobagonian Creole English #language-Lawunuia #language-Tagin #language-Tajik #language-Tagalog #language-Tandaganon #language-Sudest #language-Tangoa #language-Tring #language-Tareng #language-Nume #language-Central Tagbanwa #language-Tanggu #language-Tagwana Senoufo #language-Tagish #language-Togoyo #language-Thai #language-Kuuk Thaayorre #language-Chitwania Tharu #language-Thangmi #language-Tharaka #language-Dangaura Tharu #language-Aheu #language-Thompson #language-Kochila Tharu #language-Rana Tharu #language-Thakali #language-Tahltan #language-Tahaggart Tamahaq #language-Tayart Tamajeq #language-Tidikelt Tamazight #language-Tira #language-Tifal #language-Tigre #language-Timugon Murut #language-Tiene #language-Tilung #language-Tikar #language-Tillamook #language-Timbe #language-Tindi #language-Teop #language-Trimuris #language-Tiéfo #language-Tigrinya #language-Masadiit Itneg #language-Tinigua #language-Adasen #language-Tiv #language-Tiwi #language-Southern Tiwa #language-Tiruray #language-Tai Hongjin #language-Tunjung #language-Northern Tujia #language-Timucua #language-Southern Tujia #language-Tjurruru #language-Djabwurrung #language-Tukudede #language-Takwane #language-Tesaka Malagasy #language-Tokelau #language-Takelma #language-Toku-No-Shima #language-Tikopia #language-Tee #language-Tsakhur #language-Takestani #language-Upper Necaxa Totonac #language-Teanu #language-Tobelo #language-Yecuatla Totonac #language-Talaud #language-Telefol #language-Tofanma #language-Klingon #language-Tlingit #language-Talinga-Bwisi #language-Taloki #language-Tetela #language-Tolomako #language-Talodi #language-Filomena Mata-Coahuitlán Totonac #language-Tai Loi #language-Talise #language-Tambotalo #language-Sou Nama #language-Tulehu #language-Taliabu #language-Khehek #language-Talysh #language-Tama (Chad) #language-Katbol #language-Tumak #language-Haruai #language-Toba-Maskoy #language-Tutuba #language-Samarokena #language-Tai Thanh #language-Taman (Indonesia) #language-Temoq #language-Tumleo #language-Jewish Babylonian Aramaic (ca. 200-1200 CE) #language-Tima #language-Tasmate #language-Iau #language-Temuan #language-Tami #language-Tamanaku #language-Tacana #language-Tanimuca-Retuarã #language-Maiani #language-Tandia #language-Kwamera #language-Lenakel #language-Tabla #language-North Tanna #language-Toromono #language-Whitesands #language-Taino #language-Ménik #language-Tontemboan #language-Tonsawang #language-Tanema #language-Ten'edn #language-Toba #language-Coyutla Totonac #language-Gizrra #language-Tonga (Nyasa) #language-Gitonga #language-Tonga (Zambia) #language-Tojolabal #language-Tolowa #language-Tombulu #language-Tonga (Tonga Islands) #language-Xicotepec De Juárez Totonac #language-Papantla Totonac #language-Highland Totonac #language-Jemez #language-Tobian #language-Taupota #language-Azoyú Me'phaa #language-Tippera #language-Tarpia #language-Kula #language-Tok Pisin #language-Tapieté #language-Tlacoapa Me'phaa #language-Tampulma #language-Tupinambá #language-Pisaflores Tepehua #language-Tuparí #language-Tlachichilco Tepehua #language-Tampuan #language-tpw #language-Acatepec Me'phaa #language-Trumai #language-Tinputz #language-Tembé #language-Lehali #language-Toaripi #language-Tomoip #language-Tunni #language-Touo #language-Tonkawa #language-Tirahi #language-Copala Triqui #language-Turi #language-Trinidadian Creole English #language-Lishán Didán #language-Turaka #language-Trió #language-Tregami #language-Trinitario #language-Tarao Naga #language-Kok Borok #language-San Martín Itunyoso Triqui #language-Taushiro #language-Chicahuaxtla Triqui #language-Tunggare #language-Turoyo #language-Sediq #language-Torwali #language-Tringgus-Sembaan Bidayuh #language-Torá #language-Tsaangi #language-Tsamai #language-Tswa #language-Tsakonian #language-Tausug #language-Tsuvan #language-Tsimshian #language-Tshangla #language-Tseku #language-Ts'ün-Lao #language-Tswana #language-Tsonga #language-Akei #language-Tsou #language-Tsogo #language-Tsishingini #language-Mubami #language-Purepecha #language-Tutelo #language-Tektiteko #language-Tauade #language-Bwanabwana #language-Tuotomb #language-Tutong #language-Tobati #language-Tooro #language-Totoro #language-Northern Tutchone #language-Towei #language-Tawallammat Tamajaq #language-Tera #language-Northeastern Thai #language-Muslim Tat #language-Torau #language-Titan #language-Long Wat #language-Sikaritai #language-Wiarumus #language-Tübatulabal #language-Mutu #language-Tuxá #language-Tuyuca #language-Central Tunebo #language-Tunia #language-Taulil #language-Tupuri #language-Turkmen #language-Tula #language-Tumbuka #language-Tunica #language-Tucano #language-Tedaga #language-Turkish #language-Tuscarora #language-Tututni #language-Turkana #language-Tuxináwa #language-Tugen #language-Turka #language-Vaghua #language-Tsuvadi #language-Te'un #language-Southeast Ambrym #language-Tuvalu #language-Tela-Masbuar #language-Tidore #language-Taveta #language-Tunen #language-Sedoa #language-Timor Pidgin #language-Twana #language-Western Tawbuid #language-Twents #language-Tewa (Indonesia) #language-Northern Tiwa #language-Tereweng #language-Tai Dón #language-Twi #language-Tawang Monpa #language-Ere #language-Tasawaq #language-Turiwára #language-Termanu #language-Tuwari #language-Tewe #language-Tawoyan #language-Tombonuo #language-Tokharian B #language-Tsetsaut #language-Totoli #language-Tangut #language-Thracian #language-Ikpeng #language-Tomini #language-West Tarangan #language-Toto #language-Tonsea #language-Citak #language-Kayapó #language-Tatana #language-Tanosy Malagasy #language-Tauya #language-Kyanga #language-Teke-Tsaayi #language-Tai Do #language-Kombai #language-Thaypan #language-Tai Daeng #language-Kua #language-Tuvinian #language-Teke-Tyee #language-Tày #language-Tzeltal #language-Tz'utujil #language-Talossan #language-Central Atlas Tamazight #language-Tugun #language-Tzotzil #language-Tabriak #language-Uamué #language-Kuan #language-Tairuma #language-Ubang #language-Ubi #language-Buhi'non Bikol #language-Ubir #language-Umbu-Ungu #language-Ubykh #language-Uda #language-Udihe #language-Udi #language-Ujir #language-Wuzlam #language-Udmurt #language-Uduk #language-Kioko #language-Ufim #language-Ugaritic #language-Ughele #language-Ugong #language-Uhami #language-Damal #language-Uighur #language-Iyive #language-Tanjijili #language-Kaburi #language-Ukuriguma #language-Ukhwejo #language-Kui (India) #language-Ukpe-Bayobiri #language-Ukwa #language-Ukrainian #language-Ukue #language-Kuku #language-Fungwa #language-Ulch #language-Lule #language-Usku #language-Ulithian #language-Meriam Mir #language-Ulumanda' #language-Unserdeutsch #language-Uma' Lung #language-Ulwa #language-Umatilla #language-Umbundu #language-Marrucinian #language-Morrobalama #language-Umon #language-Umotína #language-Umpila #language-Umbugarla #language-Pendau #language-Munsee #language-North Watut #language-Undetermined #language-Uneme #language-Ngarinyin #language-Enawené-Nawé #language-Unami #language-Kurnai #language-Mundari #language-Unde Kaili #language-Uripiv-Wala-Rano-Atchin #language-Urarina #language-Urubú-Kaapor #language-Urningangg #language-Urdu #language-Uru #language-Uradhi #language-Urigina #language-Urhobo #language-Urim #language-Urak Lawoi' #language-Uruangnirin #language-Uru-Pa-In #language-Lehalurup #language-Urat #language-Urumi #language-Uruava #language-Sop #language-Urimo #language-Orya #language-Uru-Eu-Wau-Wau #language-Usarufa #language-Ushojo #language-Usaghade #language-Uspanteco #language-Uya #language-Otank #language-Ute-Southern Paiute #language-Amba (Solomon Islands) #language-Etulo #language-Utu #language-Urum #language-Ura (Vanuatu) #language-U #language-West Uvean #language-Uri #language-Lote #language-Kuku-Uwanh #language-Doko-Uyanga #language-Northern Uzbek #language-Southern Uzbek #language-Vaagri Booli #language-Vale #language-Vafsi #language-Vagla #language-Varhadi-Nagpuri #language-Vai #language-Sekele #language-Vehes #language-Vanimo #language-Valman #language-Vao #language-Huarijio #language-Vasavi #language-Vanuma #language-Wayu #language-Southeast Babar #language-Southwestern Bontok #language-Venetian #language-Veddah #language-Veluws #language-Vemgo-Mabas #language-Venda #language-Ventureño #language-Veps #language-Mom Jango #language-Virgin Islands Creole English #language-Vietnamese #language-Vili #language-Viemo #language-Vilela #language-Vinza #language-Viti #language-Iduna #language-Kariyarra #language-Kujarge #language-Kulisusu #language-Kamakan #language-Kodeoha #language-Korlai Creole Portuguese #language-Kurrama #language-Valpei #language-Vlaams #language-Martuyhunira #language-Barbaram #language-East Masela #language-Mainfränkisch #language-Lungalunga #language-Miwa #language-Makhuwa-Shirima #language-Malgana #language-Mitlatongo Mixtec #language-Soyaltepec Mazatec #language-Marenje #language-Makhuwa #language-Ayautla Mazatec #language-Mazatlán Mazatec #language-Vano #language-Vinmavis #language-Vunapu #language-Volapük #language-Voro #language-Votic #language-Vera'a #language-Võro #language-Varisi #language-Burmbar #language-Vunjo #language-Vute #language-Walla Walla #language-Wab #language-Wasco-Wishram #language-Wamesa #language-Walser #language-Watubela #language-Waffa #language-Wolaytta #language-Wampanoag #language-Wan #language-Wappo #language-Wapishana #language-Wagiman #language-Waray (Philippines) #language-Washo #language-Kaninuwa #language-Waurá #language-Waka #language-Waiwai #language-Watam #language-Wayana #language-Wampur #language-Warao #language-Wabo #language-Waritai #language-Wara #language-Wanda #language-Vwanji #language-Alagwa #language-Waigali #language-Wakhi #language-Wa #language-Warlpiri #language-Wagdi #language-Warnman #language-Wajarri #language-Woi #language-Yanomámi #language-Waci Gbe #language-Wandji #language-Wadaginam #language-Wadjiginy #language-Wewaw #language-Wedau #language-Wergaia #language-Weh #language-Kiunum #language-Wemale #language-Westphalien #language-Weri #language-Cameroon Pidgin #language-Perai #language-Wejewa #language-Yafi #language-Wagaya #language-Wagawaga #language-Wangkangurru #language-Wahgi #language-Waigeo #language-Wirangu #language-Warrgamay #language-Sou Upaa #language-Wahau Kenyah #language-Southern Toussian #language-Wichita #language-Wik Ngathan #language-Wik-Me'anha #language-Minidien #language-Wikalkan #language-Wilawila #language-Wik-Mungkan #language-Ho-Chunk #language-Wiraféd #language-Wiru #language-Vitu #language-Wiyot #language-Waja #language-Warji #language-Kw'adza #language-Wakawaka #language-Walio #language-Mwali Comorian #language-Wolane #language-Kunbarlang #language-Welaun #language-Waioli #language-Wailaki #language-Wali (Sudan) #language-Middle Welsh #language-Walloon #language-Wolio #language-Wailapa #language-Wallisian #language-Wuliwuli #language-Wichí Lhamtés Vejoz #language-Walak #language-Waling #language-Wambaya #language-Wamas #language-Mamaindé #language-Wambule #language-Waima'a #language-Wamin #language-Wom (Papua New Guinea) #language-Wambon #language-Walmajarri #language-Mwani #language-Womo #language-Mokati #language-Wantoat #language-Wandarang #language-Waneci #language-Ndzwani Comorian #language-Wanukaka #language-Wano #language-Wanap #language-Usan #language-Wintu #language-Wanyi #language-Kuwema #language-Wè Northern #language-Wogeo #language-Wolani #language-Woleaian #language-Gambian Wolof #language-Wogamusin #language-Kamang #language-Longto #language-Wolof #language-Wom (Nigeria) #language-Wongo #language-Hanga Hundi #language-Wawonii #language-Maco #language-Waluwarra #language-Warungu #language-Wiradjuri #language-Wariyangga #language-Garrwa #language-Warlmanpa #language-Warumungu #language-Warnang #language-Worrorra #language-Waropen #language-Wardaman #language-Waris #language-Waru #language-Waruna #language-Gugu Warra #language-Merwari #language-Waray (Australia) #language-Warembori #language-Adilabad Gondi #language-Wusi #language-Waskia #language-Owenia #language-Wotapuri-Katarqalai #language-Watiwa #language-Wathawurrung #language-Berta #language-Mewati #language-Wotu #language-Wunambal #language-Wutunhua #language-Silimo #language-Wumbvu #language-Bungu #language-Wutung #language-Wu Chinese #language-Wuvulu-Aua #language-Wulna #language-Wauyai #language-Waama #language-Wetamut #language-Warrwa #language-Wawa #language-Waxianghua #language-Wangaaybuwan-Ngiyambaa #language-Woiwurrung #language-Wymysorys #language-Wayoró #language-Western Fijian #language-Andalusian Arabic #language-Sambe #language-Kachari #language-Adai #language-Kaimbé #language-Ararandewára #language-Kalmyk #language-ǀXam #language-Xamtanga #language-Apalachee #language-Aquitanian #language-Karami #language-Kamas #language-Katawixi #language-Kauwera #language-Xavánte #language-Kawaiisu #language-Bactrian #language-Bigambal #language-Bunganditj #language-Kombio #language-Middle Breton #language-Kenaboi #language-Kambera #language-Kambiwá #language-Celtiberian #language-Cisalpine Gaulish #language-Chemakum #language-Classical Armenian #language-Comecrudo #language-Cotoname #language-Chorasmian #language-Carian #language-Classical Tibetan #language-Coahuilteco #language-Cayuse #language-Dacian #language-Dharuk #language-Edomite #language-Malayic Dayak #language-Eblan #language-Hdi #language-ǁXegwi #language-Kelo #language-Kembayan #language-Epi-Olmec #language-Xerénte #language-Kesawai #language-Xetá #language-Keoru-Ahia #language-Faliscan #language-Galatian #language-Gudang #language-Gabrielino-Fernandeño #language-Dharumbal #language-Hadrami #language-Xhosa #language-Hattic #language-Hurrian #language-Iberian #language-Xiri #language-Illyrian #language-Xinca #language-Xiriâna #language-Xipaya #language-Jaitmatang #language-Kalkoti #language-Northern Nago #language-Kereho #language-Kagoro #language-Mainstream Kenyah #language-Kayan River Kayan #language-Koroni #language-Xakriabá #language-Kumbewaha #language-Kgalagadi #language-Uma' Lasan #language-Kamula #language-Loup B #language-Lycian #language-Lydian #language-Ligurian (Ancient) #language-Loup A #language-Lepontic #language-Lusitanian #language-Cuneiform Luwian #language-Elymian #language-Mbonga #language-Mingrelian #language-Mengaka #language-Kugu-Muminh #language-Ancient Macedonian #language-Manado Malay #language-Manichaean Middle Persian #language-Meroitic #language-Matbat #language-Kamu #language-Antankarana Malagasy #language-Tsimihety Malagasy #language-Mayaguduna #language-Mori Bawah #language-Kanakanabu #language-Middle Mongolian #language-Ngarigu #language-Ngumbarl #language-Northern Kankanay #language-Anglo-Norman #language-Kanashi #language-Nyiyaparli #language-Kenzi #language-Kokoda #language-Soga #language-Xokleng #language-Komo (Sudan) #language-Konkomba #language-Xukurú #language-Kopar #language-Korubo #language-Kowaki #language-Northeastern Tasmanian #language-Oyster Bay Tasmanian #language-Liberia Kpelle #language-Southeast Tasmanian #language-Phrygian #language-Kulina Pano #language-Pumpokol #language-Mohegan-Pequot #language-Parthian #language-Punthamara #language-Punic #language-Northern Tasmanian #language-Puyo #language-Qatabanian #language-Krahô #language-Kreye #language-Krikati-Timbira #language-Arin #language-Aranama-Tamique #language-Marriammu #language-Karawa #language-Sabaean #language-Sambal #language-Scythian #language-Sempan #language-Shamang #language-Sio #language-South Slavey #language-Kasem #language-Silopi #language-Sherpa #language-xss #language-Sanumá #language-Sudovian #language-Saisiyat #language-Alcozauca Mixtec #language-Katcha-Kadugli-Miri #language-Diuxi-Tilantongo Mixtec #language-Ketengban #language-Transalpine Gaulish #language-Magdalena Peñasco Mixtec #language-Tokharian A #language-Thawa #language-Alu Kurumba #language-Betta Kurumba #language-Kunigami #language-Ngunawal #language-Umbrian #language-Kuo #language-Upper Umpqua #language-Urartian #language-Kuthant #language-Kxoe #language-Kamviri #language-Vandalic #language-Vestinian #language-Kwaza #language-Woccon #language-Wadi Wadi #language-Kwegu #language-Wangkumara #language-Written Oirat #language-Wemba Wemba #language-Ke'o #language-Tambora #language-Mayi-Yapi #language-Mayi-Kulan #language-Mayi-Thakurti #language-Yorta Yorta #language-Zhang-Zhung #language-Ancient Zapotec #language-Yaminahua #language-Yuhup #language-Yagua #language-Pumé #language-Yaka (Democratic Republic of Congo) #language-Yámana #language-Yazgulyam #language-Yagnobi #language-Banda-Yangere #language-Yakama #language-Yalunka #language-Yamba #language-Mayangna #language-Yao #language-Yapese #language-Yaqui #language-Yabarana #language-Nugunu (Cameroon) #language-Yambeta #language-Yuwana #language-Yangben #language-Yawalapití #language-Agwagwune #language-Lokaa #language-Yala #language-Yemba #language-West Yugur #language-Yakha #language-Yamphu #language-Hasha #language-Yukuben #language-Yaben #language-Yabaâna #language-Yabong #language-Yawiyo #language-Yaweyuha #language-Lolopo #language-Yucuna #language-Yanda #language-Eastern Yiddish #language-Yidgha #language-Yoidik #language-Ravula #language-Yeniche #language-Yimas #language-Tarok #language-Nyankpa #language-Yetfa #language-Yapunda #language-Yeyi #language-Malyangapa #language-Yagaria #language-Yagwoia #language-Baha Buyang #language-Hlepho Phowa #language-Yinggarda #language-Wusa Nasu #language-Western Yiddish #language-Yidiny #language-Yindjibarndi #language-Yimchungru Naga #language-Yis #language-Axi Yi #language-Yakan #language-Northern Yukaghir #language-Yoke #language-Kap #language-Yasa #language-Yekora #language-Kathu #language-Yakoma #language-Yele #language-Yelogu #language-Angguruk Yali #language-Yil #language-Langnian Buyang #language-Yalarnnga #language-Aribwaung #language-Nyâlayu #language-Yambes #language-Southern Muji #language-Yameo #language-Iamalele #language-Maay #language-Yangum Mon #language-Yamap #language-Yandruwandha #language-Naukan Yupik #language-Yangulam #language-Yana #language-Yendang #language-Yansi #language-Yahuna #language-Yoba #language-Yogad #language-Yonaguni #language-Yokuts #language-Yola #language-Yombe #language-Yongkom #language-Yoruba #language-Yotti #language-Yoron #language-Yoy #language-Phola #language-Phupha #language-Phuza #language-Yerakai #language-Yareba #language-Yaouré #language-Nenets #language-Nhengatu #language-Yerong #language-Yarawata #language-Sani #language-Sirenik Yupik #language-Yessan-Mayo #language-Yucateco #language-Yugambal #language-Yuchi #language-Yue Chinese #language-Havasupai-Walapai-Yavapai #language-Yug #language-Yurutí #language-Karkar-Yuri #language-Yuki #language-Yulu #language-Quechan #language-Bena (Nigeria) #language-Yukpa #language-Yurok #language-Yopno #language-Yau (Morobe Province) #language-Southern Yukaghir #language-East Yugur #language-Yuracare #language-Yawa #language-Yavitero #language-Kalou #language-Yawanawa #language-Wuding-Luquan Yi #language-Yawuru #language-Xishanba Lalo #language-Yawarawarga #language-Yagara #language-Yardliyawarra #language-Yir Yoront #language-Yau (Sandaun Province) #language-E'ma Buyang #language-Sierra de Juárez Zapotec #language-Western Tlacolula Valley Zapotec #language-Ocotlán Zapotec #language-Cajonos Zapotec #language-Yareni Zapotec #language-Ayoquesco Zapotec #language-Zaghawa #language-Isthmus Zapotec #language-Zaramo #language-Zanaki #language-Zauzou #language-Ozolotepec Zapotec #language-Zapotec #language-Aloápam Zapotec #language-Rincón Zapotec #language-Yatzachi Zapotec #language-Mitla Zapotec #language-Zayse-Zergulla #language-Zari #language-Balaibalan #language-Central Berawan #language-East Berawan #language-Blissymbols #language-West Berawan #language-Coatecas Altas Zapotec #language-Central Hongshuihe Zhuang #language-Ngazidja Comorian #language-Zeeuws #language-Zenag #language-Zenaga #language-Kinga #language-Guibei Zhuang #language-Standard Moroccan Tamazight #language-Guibian Zhuang #language-Magori #language-Zhuang #language-Zhaba #language-Dai Zhuang #language-Zhire #language-Nong Zhuang #language-Chinese #language-Zhoa #language-Zia #language-Zimakani #language-Mesme #language-Zinza #language-Zigula #language-Zizilivakan #language-Kaimbulawa #language-zkb #language-Kadu #language-Koguryo #language-Karankawa #language-Kott #language-São Paulo Kaingáng #language-Zakhring #language-Kitan #language-Kaurna #language-Liujiang Zhuang #language-Malay (individual language) #language-Lianshan Zhuang #language-Manda (Australia) #language-Zimba #language-Margany #language-Maridan #language-Mangerr #language-Marti Ke #language-Maridjabin #language-Mandandanyi #language-Matngala #language-Marimanindji #language-Mbangwe #language-Molo #language-Mpuono #language-Maranunggu #language-Maringarr #language-Muruwari #language-Mbariman-Gudhinma #language-Mbo (Democratic Republic of Congo) #language-Bomitaba #language-Mariyedi #language-Mbandja #language-Zande (individual language) #language-Mang #language-Mangas #language-Copainalá Zoque #language-Chimalapa Zoque #language-Zou #language-Tabasco Zoque #language-Rayón Zoque #language-Francisco León Zoque #language-Choapan Zapotec #language-San Pedro Quiatoni Zapotec #language-Santa María Quiegolani Zapotec #language-Lachixío Zapotec #language-Mixtepec Zapotec #language-Santa Inés Yatzechi Zapotec #language-Amatlán Zapotec #language-Zoogocho Zapotec #language-Santiago Xanica Zapotec #language-Coatlán Zapotec #language-Yalálag Zapotec #language-Chichicapan Zapotec #language-Zaniza Zapotec #language-San Baltazar Loxicha Zapotec #language-Texmelucan Zapotec #language-Qiubei Zhuang #language-Záparo #language-Zarphatic #language-Mairasi #language-Sarasira #language-Standard Malay #language-Sukurum #language-Xanaguía Zapotec #language-Loxicha Zapotec #language-Quioquitani-Quierí Zapotec #language-Tilquiapan Zapotec #language-Güilá Zapotec #language-Zeem #language-Tokano #language-Zulu #language-Kumzari #language-Zuni #language-Zay #language-Yongbei Zhuang #language-Yang Zhuang #language-Youjiang Zhuang #language-Yongnan Zhuang #language-Zuojiang Zhuang #license-cc0-1.0 #lexicons #region-us \n", "# PanLex\n\nJanuary 1, 2024 version of PanLex Language Vocabulary with 24,650,274 rows covering 6,152 languages.", "# Columns\n- 'vocab': contains the text entry. \n- '639-3': contains the ISO 639-3 languages tags to allow users to filter on the language(s) of their choice.\n- '639-3_english_name': the English language name associated to the code ISO 639-3. \n- 'var_code': contains a code to differentiate language variants. In practice, this is the code 639-3 + a number. If 000, it corresponds to the classic 639-3, if the number is different, it corresponds to a variant (e.g. 'fra-006' corresponds to Québécois according to URL This is a code created by Panlex, and therefore does not follow an international standard. \n- 'english_name_var': the English language name associated to the 'var_code'.", "# Source\nReaders are invited to consult the URL page, which summarizes all the sources.\n\n> Kamholz, David, Jonathan Pool, and Susan M. Colowick. 2014. \n PanLex: Building a Resource for Panlingual Lexical Translation. \n Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014). \n URL" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-classification", "size_categories:n<1K", "language:en", "doi:10.57967/hf/1667", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #size_categories-n<1K #language-English #doi-10.57967/hf/1667 #region-us \n" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "license:other", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-other #region-us \n" ]

[ "task_categories:conversational", "task_categories:question-answering", "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-conversational #task_categories-question-answering #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.11\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.11 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T17:37:46.856873(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.11\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.11 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T17:37:46.856873(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of freeCS-dot-org/ThetaZero-7B-1\n\n\n\nDataset automatically created during the evaluation run of model freeCS-dot-org/ThetaZero-7B-1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T17:39:05.540734(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of freeCS-dot-org/ThetaZero-7B-1\n\n\n\nDataset automatically created during the evaluation run of model freeCS-dot-org/ThetaZero-7B-1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T17:39:05.540734(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-generation", "language:la", "region:us" ]

[]

[ "la" ]

[ "# Cicero's De finibus bonorum et malorum", "## Overview\nThis dataset contains the text from Cicero's \"De finibus bonorum et malorum.\" It is intended for use in comparing different neural network architectures within the \"Neural Network Evolution\" project.", "## Source\nTexts are sourced from The Latin Library. The Latin Library", "## Preprocessing\nThe dataset was prepared using a Python script for fetching and processing the text. The script ensures consistent formatting and encoding.\n\nFormatting decision\n- remove all text in html header\n- remove all html tags and tag bodies for anchors\n- remove any references in brackets. ex: [1]\n- remove any implicit text additions. ex: [et]\n- collapse newlines to two separating paragraphs\n- standardize paragraph intent to one tab\n\nSee fetch_text.py for details.", "## Structure of the Dataset\n- Format: Plain text\n- Encoding: UTF-8\n- Size: Approximately 94,616 words and 638,272 characters across 5 books", "## License\nThis dataset is released under the Creative Commons Attribution 4.0 International License." ]

[ "TAGS\n#task_categories-text-generation #language-Latin #region-us \n", "# Cicero's De finibus bonorum et malorum", "## Overview\nThis dataset contains the text from Cicero's \"De finibus bonorum et malorum.\" It is intended for use in comparing different neural network architectures within the \"Neural Network Evolution\" project.", "## Source\nTexts are sourced from The Latin Library. The Latin Library", "## Preprocessing\nThe dataset was prepared using a Python script for fetching and processing the text. The script ensures consistent formatting and encoding.\n\nFormatting decision\n- remove all text in html header\n- remove all html tags and tag bodies for anchors\n- remove any references in brackets. ex: [1]\n- remove any implicit text additions. ex: [et]\n- collapse newlines to two separating paragraphs\n- standardize paragraph intent to one tab\n\nSee fetch_text.py for details.", "## Structure of the Dataset\n- Format: Plain text\n- Encoding: UTF-8\n- Size: Approximately 94,616 words and 638,272 characters across 5 books", "## License\nThis dataset is released under the Creative Commons Attribution 4.0 International License." ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of cloudyu/Venus_DPO_50\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/Venus_DPO_50 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T17:50:57.443719(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of cloudyu/Venus_DPO_50\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/Venus_DPO_50 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T17:50:57.443719(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"math_23k_double_kernel\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"math_23k_double_kernel\"\n\nMore Information needed" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of AA051612/B0121\n\n\n\nDataset automatically created during the evaluation run of model AA051612/B0121 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T18:18:25.234696(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of AA051612/B0121\n\n\n\nDataset automatically created during the evaluation run of model AA051612/B0121 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T18:18:25.234696(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:ar", "license:apache-2.0", "region:us" ]

[]

[ "ar" ]

[ "### Model Details\n\nDeveloped by: Prince Sultan University - Riotu Lab\n\nThis dataset is intended for use in natural language processing tasks, particularly for understanding classical Arabic and religious texts, including text analysis, language modeling, and thematic studies.\nPrimary Users: Researchers and developers in the field of natural language processing, religious studies, and AI, specifically those working with classical Arabic texts.\nOut-of-scope Use Cases: This dataset is not intended for predictive modeling that could lead to ethical concerns, such as surveillance or profiling based on religious texts.\nModel/Data Specifications\n\nFormat: Json\nDataset Size: Contains more than 57K rows\nLanguage: Arabic", "### Dataset Structure\n\nFields:\n- sura_number: Integer representing the Surah number in the Quran.-\n- Aya_number: Integer representing the Ayah number in the Surah.\n- tafseers: Dictionary mapping Tafseer sources to their text for each Ayah:\n- Tafseer Name : \n 1: \"التفسير الميسر\",\n 2: \"تفسير الجلالين\",\n 3:\"تفسير ابن كثير\",\n 4: \"تفسير الوسيط لطنطاوي\",\n 5: \"تفسير البغوي\",\n 6: \"تفسير القرطبي\",\n 7: \"تفسير الطبري\"," ]

[ "TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-Arabic #license-apache-2.0 #region-us \n", "### Model Details\n\nDeveloped by: Prince Sultan University - Riotu Lab\n\nThis dataset is intended for use in natural language processing tasks, particularly for understanding classical Arabic and religious texts, including text analysis, language modeling, and thematic studies.\nPrimary Users: Researchers and developers in the field of natural language processing, religious studies, and AI, specifically those working with classical Arabic texts.\nOut-of-scope Use Cases: This dataset is not intended for predictive modeling that could lead to ethical concerns, such as surveillance or profiling based on religious texts.\nModel/Data Specifications\n\nFormat: Json\nDataset Size: Contains more than 57K rows\nLanguage: Arabic", "### Dataset Structure\n\nFields:\n- sura_number: Integer representing the Surah number in the Quran.-\n- Aya_number: Integer representing the Ayah number in the Surah.\n- tafseers: Dictionary mapping Tafseer sources to their text for each Ayah:\n- Tafseer Name : \n 1: \"التفسير الميسر\",\n 2: \"تفسير الجلالين\",\n 3:\"تفسير ابن كثير\",\n 4: \"تفسير الوسيط لطنطاوي\",\n 5: \"تفسير البغوي\",\n 6: \"تفسير القرطبي\",\n 7: \"تفسير الطبري\"," ]

[ "task_categories:text-classification", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #source_datasets-original #language-English #license-mit #region-us \n" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:question-answering", "size_categories:1K < n < 10K", "region:us" ]

[]

[]

[ "# DogLLaMA-small\n\nThe dataset was created using the GPT-3.5 Turbo model by OpenAI. The process involved providing a system prompt instructing the model to respond as if it were a dog, conveying translations or responses in a playful and enthusiastic manner. The dataset includes a variety of prompts covering different topics to ensure diversity in the training data.\n\nThe code for the same can be found here: URL" ]

[ "TAGS\n#task_categories-question-answering #size_categories-1K < n < 10K #region-us \n", "# DogLLaMA-small\n\nThe dataset was created using the GPT-3.5 Turbo model by OpenAI. The process involved providing a system prompt instructing the model to respond as if it were a dog, conveying translations or responses in a playful and enthusiastic manner. The dataset includes a variety of prompts covering different topics to ensure diversity in the training data.\n\nThe code for the same can be found here: URL" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:de", "license:mit", "legal", "region:us" ]

[]

[ "de" ]

[ "# Dataset Card for german-court-decisions\n60k judicial decisions in Germany retrieved on January 1, 2024.", "# Dataset Description\n\nLanguage(s) (NLP): German<br>\nLicense: MIT\n\nCopyright notice: Automated retrieval of decisions from federal and state databases in Germany is permitted for non-commercial purposes only. As a result, the use of this dataset is permitted for non-commercial purposes only.", "## Uses\n\nPrediction of verdicts based on statement of facts.", "### Direct Use\n\nClassification convicted/dismissed etc.", "### Out-of-Scope Use\n\nText generation of verdicts based on statement of facts.", "## Dataset Structure\n\n\n\n: &emsp;court name (string), e.g. 'AG Bad Neuenahr-Ahrweiler'; may be empty if it does not match the search mask<br>\n: &emsp;federated state (string), e.g. 'Brandenburg', 'Berlin', ... , 'Thüringen' <br>\n: &emsp;date (string), e.g. '03.04.2013'; may be empty if it does not match the search mask<br>\n: &emsp;statement of facts (string; _Tatbestand_ in German)<br>\n: &emsp;verdict (string; _Tenor_ in German)<br>\n\n: &emsp;Boolean (0/1), text phrase detection (see data processing below)<br>\n: &emsp;Boolean (0/1), text phrase detection (see data processing below)<br>\n: &emsp;Boolean (0/1), text phrase detection (see data processing below)<br>\n: &emsp;Boolean (0/1), text phrase detection (see data processing below)<br>", "## Dataset Creation, Data Collection and Processing\n\nData creation: &emsp;Federal governments in Germany<br>\nData collection: &emsp;URL on January 1, 2024<br>\nData processing: &emsp;URL" ]

[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-German #license-mit #legal #region-us \n", "# Dataset Card for german-court-decisions\n60k judicial decisions in Germany retrieved on January 1, 2024.", "# Dataset Description\n\nLanguage(s) (NLP): German<br>\nLicense: MIT\n\nCopyright notice: Automated retrieval of decisions from federal and state databases in Germany is permitted for non-commercial purposes only. As a result, the use of this dataset is permitted for non-commercial purposes only.", "## Uses\n\nPrediction of verdicts based on statement of facts.", "### Direct Use\n\nClassification convicted/dismissed etc.", "### Out-of-Scope Use\n\nText generation of verdicts based on statement of facts.", "## Dataset Structure\n\n\n\n: &emsp;court name (string), e.g. 'AG Bad Neuenahr-Ahrweiler'; may be empty if it does not match the search mask<br>\n: &emsp;federated state (string), e.g. 'Brandenburg', 'Berlin', ... , 'Thüringen' <br>\n: &emsp;date (string), e.g. '03.04.2013'; may be empty if it does not match the search mask<br>\n: &emsp;statement of facts (string; _Tatbestand_ in German)<br>\n: &emsp;verdict (string; _Tenor_ in German)<br>\n\n: &emsp;Boolean (0/1), text phrase detection (see data processing below)<br>\n: &emsp;Boolean (0/1), text phrase detection (see data processing below)<br>\n: &emsp;Boolean (0/1), text phrase detection (see data processing below)<br>\n: &emsp;Boolean (0/1), text phrase detection (see data processing below)<br>", "## Dataset Creation, Data Collection and Processing\n\nData creation: &emsp;Federal governments in Germany<br>\nData collection: &emsp;URL on January 1, 2024<br>\nData processing: &emsp;URL" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "synthetic", "distilabel", "region:us" ]

[]

[]

[]

[ "TAGS\n#synthetic #distilabel #region-us \n" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "language:en", "region:us" ]

[]

[ "en" ]

[ "# BabyLM 100M\n\nThis curated dataset is originally from the BabyLM Challenge.\n\nIt consists of ~100M words of mixed domain, consisting of the following sources:\n- CHILDES (child-directed speech)\n- Subtitles (speech)\n- BNC (speech)\n- TED talks (speech)\n- children's books (simple written language)" ]

[ "TAGS\n#language-English #region-us \n", "# BabyLM 100M\n\nThis curated dataset is originally from the BabyLM Challenge.\n\nIt consists of ~100M words of mixed domain, consisting of the following sources:\n- CHILDES (child-directed speech)\n- Subtitles (speech)\n- BNC (speech)\n- TED talks (speech)\n- children's books (simple written language)" ]

[ "language:en", "region:us" ]

[]

[ "en" ]

[ "# BabyLM 10M\n\nThis curated dataset is originally from the BabyLM Challenge.\n\nIt consists of ~10M words of mixed domain, consisting of the following sources:\n- CHILDES (child-directed speech)\n- Subtitles (speech)\n- BNC (speech)\n- TED talks (speech)\n- children's books (simple written language)" ]

[ "TAGS\n#language-English #region-us \n", "# BabyLM 10M\n\nThis curated dataset is originally from the BabyLM Challenge.\n\nIt consists of ~10M words of mixed domain, consisting of the following sources:\n- CHILDES (child-directed speech)\n- Subtitles (speech)\n- BNC (speech)\n- TED talks (speech)\n- children's books (simple written language)" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of binbi/SF-72B-V1.8.6-V1.2\n\n\n\nDataset automatically created during the evaluation run of model binbi/SF-72B-V1.8.6-V1.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T20:26:22.258506(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of binbi/SF-72B-V1.8.6-V1.2\n\n\n\nDataset automatically created during the evaluation run of model binbi/SF-72B-V1.8.6-V1.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T20:26:22.258506(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca\n\n\n\nDataset automatically created during the evaluation run of model robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T20:50:11.424256(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca\n\n\n\nDataset automatically created during the evaluation run of model robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T20:50:11.424256(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T20:50:04.239190(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T20:50:04.239190(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of openagi-project/OpenAGI-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model openagi-project/OpenAGI-7B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-26T17:39:46.193538(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of openagi-project/OpenAGI-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model openagi-project/OpenAGI-7B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-26T17:39:46.193538(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Fonts-Individual-Letters\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Fonts-Individual-Letters\"\n\nMore Information needed" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-7B-V0.2\n\n\n\nDataset automatically created during the evaluation run of model RatanRohith/NeuralPizza-7B-V0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T21:04:32.551333(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-7B-V0.2\n\n\n\nDataset automatically created during the evaluation run of model RatanRohith/NeuralPizza-7B-V0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T21:04:32.551333(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of The-Face-Of-Goonery/Huginn-V5-10.7B\n\n\n\nDataset automatically created during the evaluation run of model The-Face-Of-Goonery/Huginn-V5-10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T21:34:43.018988(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of The-Face-Of-Goonery/Huginn-V5-10.7B\n\n\n\nDataset automatically created during the evaluation run of model The-Face-Of-Goonery/Huginn-V5-10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T21:34:43.018988(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

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[ "# Dataset Card for Evaluation run of flemmingmiguel/MBX-7B\n\n\n\nDataset automatically created during the evaluation run of model flemmingmiguel/MBX-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T21:40:54.851696(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of flemmingmiguel/MBX-7B\n\n\n\nDataset automatically created during the evaluation run of model flemmingmiguel/MBX-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T21:40:54.851696(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of cognitivecomputations/MegaDolphin-120b\n\n\n\nDataset automatically created during the evaluation run of model cognitivecomputations/MegaDolphin-120b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T21:48:58.549252(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of cognitivecomputations/MegaDolphin-120b\n\n\n\nDataset automatically created during the evaluation run of model cognitivecomputations/MegaDolphin-120b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T21:48:58.549252(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]