Datasets:

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ftopal

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huggingface-datasets

like 0

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"dalio-book-handwritten-io-sorted-v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dalio-book-handwritten-io-sorted-v2\"\n\nMore Information needed" ]

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"sentences_truthv2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"sentences_truthv2\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"small-the_pile\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[]

[ "# Dataset Card for \"small-c4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"small-c4\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"small-oscar\"\n\nMore Information needed" ]

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

[ "task_categories:image-classification", "task_categories:text-classification", "task_ids:multi-class-image-classification", "size_categories:10K<n<15K", "source_datasets:extended|other", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "### Data Instances\n\n\nUna muestra del *dataset* se encuentra a continuación:", "### Data Fields\n\n\nCada instancia de datos tiene los siguientes campos:\n\n\n* 'image': imagen RGB de un pájaro\n* 'description': texto con 10 descripciones del pájaro en la foto, cada descripción esta separado por un salto de linea (i.e. '\\n')\n* 'label': un número entero que representa el id de la especie a la que pertenece el pájaro\n\n\n\nId2String\n\n- 'file\\_name': nombre del archivo que tiene la imagen", "### Data Splits\n\n\n\nProblema\n--------\n\n\nEl problema consiste en entrenar un modelo que clasifique instancias del dataset CUB de la mejor manera posible. Algunas preguntas que podrían guiar nuestro desarrollo son:\n\n\n* Se podrá obtener un buen *performance* de clasificación solo usando las imágenes del dataset? Este tipo de problema sería el clásico problema de clasificar imágenes.\n* Se podrá obtener un buen *performance* de clasificación solo usando los textos del dataset? Este tipo de problema sería el clásico problema de clasificar texto.\n* Se podrá obtener un mejor *performance* si combino la información en un modelo multimodal? Cómo construyo un modelo multimodal que reciba una imagen y un texto y clasifique la instancia con su respectiva especie? Hint: piense en cómo una red neuronal (la que sea) es simplemente una función que recibe un dato y genera una representación de alto nivel (vector característico) de ese dato. Una red CNN podría hacerse cargo de calcular la representación de una imagen y una red RNN podría hacerse cargo de calcular la representación del texto. Finalmente concateno ambas representaciones y entreno un MLP final que hace la clasificación.\n\n\nExperimentación\n---------------\n\n\nComo el dataset es grande y los recursos de computación son muy limitados, una estrategia para hacer los experimentos es tomar una muestra más pequeña de datos para ir probando las ideas. Para esta estrategia, éstas son dos ideas válidas:\n\n\n* Tomar menos instancias por cada clase para el desarrollo y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing.\n* Tomar menos clases para el desarrollo inicial y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing.\n\n\nAmbas estrategias nos permiten lidiar con los recursos limitados que tenemos, pero cuáles son sus ventajas o desventajas? Si usas alguna de estas estrategias, puedes comentar este punto en tu desarrollo final.\n\n\nMétrica de Evaluación\n---------------------\n\n\nLa métrica que se debe reportar es el accuracy en conjunto de test.\n\n\nSitio web del *dataset* CUB200, y reporte técnico aquí.\n\n\nContributions\n-------------\n\n\nCreación y adaptación del material de la actividad en un Hugging Face dataset por Cristóbal Alcázar." ]

[ "TAGS\n#task_categories-image-classification #task_categories-text-classification #task_ids-multi-class-image-classification #size_categories-10K<n<15K #source_datasets-extended|other #language-English #license-apache-2.0 #region-us \n", "### Data Instances\n\n\nUna muestra del *dataset* se encuentra a continuación:", "### Data Fields\n\n\nCada instancia de datos tiene los siguientes campos:\n\n\n* 'image': imagen RGB de un pájaro\n* 'description': texto con 10 descripciones del pájaro en la foto, cada descripción esta separado por un salto de linea (i.e. '\\n')\n* 'label': un número entero que representa el id de la especie a la que pertenece el pájaro\n\n\n\nId2String\n\n- 'file\\_name': nombre del archivo que tiene la imagen", "### Data Splits\n\n\n\nProblema\n--------\n\n\nEl problema consiste en entrenar un modelo que clasifique instancias del dataset CUB de la mejor manera posible. Algunas preguntas que podrían guiar nuestro desarrollo son:\n\n\n* Se podrá obtener un buen *performance* de clasificación solo usando las imágenes del dataset? Este tipo de problema sería el clásico problema de clasificar imágenes.\n* Se podrá obtener un buen *performance* de clasificación solo usando los textos del dataset? Este tipo de problema sería el clásico problema de clasificar texto.\n* Se podrá obtener un mejor *performance* si combino la información en un modelo multimodal? Cómo construyo un modelo multimodal que reciba una imagen y un texto y clasifique la instancia con su respectiva especie? Hint: piense en cómo una red neuronal (la que sea) es simplemente una función que recibe un dato y genera una representación de alto nivel (vector característico) de ese dato. Una red CNN podría hacerse cargo de calcular la representación de una imagen y una red RNN podría hacerse cargo de calcular la representación del texto. Finalmente concateno ambas representaciones y entreno un MLP final que hace la clasificación.\n\n\nExperimentación\n---------------\n\n\nComo el dataset es grande y los recursos de computación son muy limitados, una estrategia para hacer los experimentos es tomar una muestra más pequeña de datos para ir probando las ideas. Para esta estrategia, éstas son dos ideas válidas:\n\n\n* Tomar menos instancias por cada clase para el desarrollo y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing.\n* Tomar menos clases para el desarrollo inicial y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing.\n\n\nAmbas estrategias nos permiten lidiar con los recursos limitados que tenemos, pero cuáles son sus ventajas o desventajas? Si usas alguna de estas estrategias, puedes comentar este punto en tu desarrollo final.\n\n\nMétrica de Evaluación\n---------------------\n\n\nLa métrica que se debe reportar es el accuracy en conjunto de test.\n\n\nSitio web del *dataset* CUB200, y reporte técnico aquí.\n\n\nContributions\n-------------\n\n\nCreación y adaptación del material de la actividad en un Hugging Face dataset por Cristóbal Alcázar." ]

[ "task_categories:image-classification", "region:us" ]

[]

[]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "TAGS\n#task_categories-image-classification #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "license:cc-by-4.0", "arxiv:2210.03094", "region:us" ]

[ "2210.03094" ]

[]

[ "# Dataset Card for VIMA-Data", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n- Dataset Structure\n- Dataset Creation\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nThis is the official dataset used to train general robot manipulation agents with multimodal prompts, as presented in paper. It contains 650K trajectories for 13 tasks in VIMA-Bench. All demonstrations are generated by oracles.", "## Dataset Structure\n\nData are grouped into different tasks. Within each trajectory's folder, there are two folders 'rgb_front' and 'rgb_top', and three files 'URL', 'URL', and 'URL'. RGB frames from a certain perspective are separately stored in corresponding folder. 'URL' includes segmentation and state of end effector. 'URL' contains oracle actions. 'URL' contains meta information such as elapsed steps, task information, and object information. Users can build their custom data piepline starting from here. More details and examples can be found here.", "## Dataset Creation\n\nAll demonstrations are generated by scripted oracles.", "## Additional Information", "### Licensing Information\n\nThis dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.\n\n\n\nIf you find our work useful, please consider citing us!" ]

[ "TAGS\n#license-cc-by-4.0 #arxiv-2210.03094 #region-us \n", "# Dataset Card for VIMA-Data", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n- Dataset Structure\n- Dataset Creation\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nThis is the official dataset used to train general robot manipulation agents with multimodal prompts, as presented in paper. It contains 650K trajectories for 13 tasks in VIMA-Bench. All demonstrations are generated by oracles.", "## Dataset Structure\n\nData are grouped into different tasks. Within each trajectory's folder, there are two folders 'rgb_front' and 'rgb_top', and three files 'URL', 'URL', and 'URL'. RGB frames from a certain perspective are separately stored in corresponding folder. 'URL' includes segmentation and state of end effector. 'URL' contains oracle actions. 'URL' contains meta information such as elapsed steps, task information, and object information. Users can build their custom data piepline starting from here. More details and examples can be found here.", "## Dataset Creation\n\nAll demonstrations are generated by scripted oracles.", "## Additional Information", "### Licensing Information\n\nThis dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.\n\n\n\nIf you find our work useful, please consider citing us!" ]

[ "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "commonsense-reasoning", "visual-reasoning", "arxiv:2212.04542", "region:us" ]

[ "2212.04542" ]

[ "en" ]

[ "# Dataset Card for VASR\n- Dataset Description\n - Supported Tasks and Leaderboards\n - How to Submit Predictions?\n - Colab notebook code for VASR evaluation with ViT\n - Languages\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Dataset Creation\n- Considerations for Using the Data\n - Licensing Information\n - Citation Information", "## Dataset Description\nVASR is a challenging dataset for evaluating computer vision commonsense reasoning abilities. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. Our experiments demonstrate that state-of-the-art models struggle with carefully chosen distractors (±53%, compared to 90% human accuracy).\n- Homepage: \nURL\n- Colab\nURL\n- Repository:\nURL\n- Paper: URL \n- Leaderboard:\nURL\n- Point of Contact:\nURL@URL", "## Supported Tasks and Leaderboards\nURL \nURL", "## How to Submit Predictions?\nTo submit predictions, please send a prediction CSV file to vasr.benchmark@URL / URL@URL. \nThe prediction file should include a \"B'\" column with the predicted candidate name that best solves the analogy, and an index from 1 to 4 indicating the location of the predicted candidate in the given candidate list. \nAn example prediction file is available HERE. \nA submission is allowed once a week, and you will receive a response within a week.", "## Colab notebook code for VASR evaluation with ViT\nURL", "### Languages\nEnglish.", "## Dataset Structure", "### Data Fields\nA: datasets.Image() - the first input image, A:A'. \nA': datasets.Image() - the second input image, different from A in a single key, A:A'. \nB: datasets.Image() - the third input image, has the same different item as A, B:B'. \nB': datasets.Image() - the forth image, which is the analogy solution. Different from B in a single key (the same different one as in A:A'), B:B'. Hidden in the test set. \ncandidates_images: [datasets.Image()] - a list of candidate images solutions to the analogy. \nlabel: datasets.Value(\"int64\") - the index of the ground-truth solution. Hidden in the test set. \ncandidates: [datasets.Value(\"string\")] - a list of candidate string solutions to the analogy.", "### Data Splits\nThere are three splits, TRAIN, VALIDATION, and TEST. \nSince there are four candidates and one solution, random chance is 25%.", "## Dataset Creation\n\nWe leverage situation recognition annotations and the CLIP model to generate a large set of 500k candidate analogies.\nThere are two types of labels: \n- Silver labels, obtained from the automatic generation.\n- Gold labels, obtained from human annotations over the silver annotations.\n\nIn the huggingface version we provide only the gold labeled dataset. Please refer to the project website download page if you want to download the silver labels version.", "### Annotations", "#### Annotation process\n\nWe paid Amazon Mechanical Turk Workers to solve analogies, five annotators for each analogy.\nWorkers were asked to select the image that best solves the analogy. \nThe resulting dataset is composed of the 3,820 instances agreed upon with a majority vote of at least 3 annotators, which was obtained in 93% of the cases.", "## Considerations for Using the Data\n\nAll associations were obtained with human annotators. \nAll used images are from the imSitu dataset (URL \nUsing this data is allowed for academic research alone.", "### Licensing Information\n\nCC-By 4.0 \n\n\n\nNA" ]

[ "TAGS\n#annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #commonsense-reasoning #visual-reasoning #arxiv-2212.04542 #region-us \n", "# Dataset Card for VASR\n- Dataset Description\n - Supported Tasks and Leaderboards\n - How to Submit Predictions?\n - Colab notebook code for VASR evaluation with ViT\n - Languages\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Dataset Creation\n- Considerations for Using the Data\n - Licensing Information\n - Citation Information", "## Dataset Description\nVASR is a challenging dataset for evaluating computer vision commonsense reasoning abilities. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. Our experiments demonstrate that state-of-the-art models struggle with carefully chosen distractors (±53%, compared to 90% human accuracy).\n- Homepage: \nURL\n- Colab\nURL\n- Repository:\nURL\n- Paper: URL \n- Leaderboard:\nURL\n- Point of Contact:\nURL@URL", "## Supported Tasks and Leaderboards\nURL \nURL", "## How to Submit Predictions?\nTo submit predictions, please send a prediction CSV file to vasr.benchmark@URL / URL@URL. \nThe prediction file should include a \"B'\" column with the predicted candidate name that best solves the analogy, and an index from 1 to 4 indicating the location of the predicted candidate in the given candidate list. \nAn example prediction file is available HERE. \nA submission is allowed once a week, and you will receive a response within a week.", "## Colab notebook code for VASR evaluation with ViT\nURL", "### Languages\nEnglish.", "## Dataset Structure", "### Data Fields\nA: datasets.Image() - the first input image, A:A'. \nA': datasets.Image() - the second input image, different from A in a single key, A:A'. \nB: datasets.Image() - the third input image, has the same different item as A, B:B'. \nB': datasets.Image() - the forth image, which is the analogy solution. Different from B in a single key (the same different one as in A:A'), B:B'. Hidden in the test set. \ncandidates_images: [datasets.Image()] - a list of candidate images solutions to the analogy. \nlabel: datasets.Value(\"int64\") - the index of the ground-truth solution. Hidden in the test set. \ncandidates: [datasets.Value(\"string\")] - a list of candidate string solutions to the analogy.", "### Data Splits\nThere are three splits, TRAIN, VALIDATION, and TEST. \nSince there are four candidates and one solution, random chance is 25%.", "## Dataset Creation\n\nWe leverage situation recognition annotations and the CLIP model to generate a large set of 500k candidate analogies.\nThere are two types of labels: \n- Silver labels, obtained from the automatic generation.\n- Gold labels, obtained from human annotations over the silver annotations.\n\nIn the huggingface version we provide only the gold labeled dataset. Please refer to the project website download page if you want to download the silver labels version.", "### Annotations", "#### Annotation process\n\nWe paid Amazon Mechanical Turk Workers to solve analogies, five annotators for each analogy.\nWorkers were asked to select the image that best solves the analogy. \nThe resulting dataset is composed of the 3,820 instances agreed upon with a majority vote of at least 3 annotators, which was obtained in 93% of the cases.", "## Considerations for Using the Data\n\nAll associations were obtained with human annotators. \nAll used images are from the imSitu dataset (URL \nUsing this data is allowed for academic research alone.", "### Licensing Information\n\nCC-By 4.0 \n\n\n\nNA" ]

[ "region:us" ]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"proofs2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"proofs2\"\n\nMore Information needed" ]

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

[]

[]

[ "# Dataset Card for covid-qa-tts", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#license-mit #region-us \n", "# Dataset Card for covid-qa-tts", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"rock-paper-scissors\"\n\nMore Information needed" ]

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

[ "license:mit", "arxiv:2205.12696", "region:us" ]

[ "2205.12696" ]

[]

[]

[ "TAGS\n#license-mit #arxiv-2205.12696 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"proofs3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"proofs3\"\n\nMore Information needed" ]

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "question-answering", "english", "causal", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @alamhanz and @andreaschandra for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #question-answering #english #causal #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @alamhanz and @andreaschandra for adding this dataset." ]

[ "task_categories:sentence-similarity", "task_categories:feature-extraction", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:other", "task_ids:document-retrieval", "task_ids:open-domain-qa", "task_ids:closed-domain-qa", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:de", "information retrieval", "ir", "documents retrieval", "passage retrieval", "beir", "benchmark", "qrel", "sts", "semantic search", "arxiv:2104.08663", "region:us" ]

[ "2104.08663" ]

[ "de" ]

[ "# Dataset Card for germanDPR-beir", "## Dataset Summary\n\nThis dataset can be used for BEIR evaluation based on deepset/germanDPR.\nIt already has been used to evaluate a newly trained bi-encoder model.\nThe benchmark framework requires a particular dataset structure by default which has been created locally and uploaded here.\n\nAcknowledgement: The dataset was initially created as \"germanDPR\" by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at URL.", "## Dataset Creation\nFirst, the original dataset deepset/germanDPR was converted into three files for BEIR compatibility:\n - The first file is 'URL' and contains an ID and a question in each line.\n - The second file, 'URL', contains in each line an ID, a title, a text and some metadata.\n - In the 'qrel' folder is the third file. It connects every question from 'URL' (via 'q_id') with a relevant text/answer from 'URL' (via 'c_id')\n \nThis process has been done for 'train' and 'test' split separately based on the original germanDPR dataset.\nApproaching the dataset creation like that is necessary because queries AND corpus both differ in deepset's germanDPR dataset\nand it might be confusion changing this specific split.\nIn conclusion, queries and corpus differ between train and test split and not only qrels data!\nNote: If you want one big corpus use 'datasets.concatenate_datasets()'.\n\nIn the original dataset, there is one passage containing the answer and three \"wrong\" passages for each question.\nDuring the creation of this customized dataset, all four passages are added, but only if they are not already present (... meaning they have been deduplicated).\n\nIt should be noted, that BEIR is combining 'title' + 'text' in 'URL' to a new string which may produce odd results:\nThe original germanDPR dataset does not always contain \"classical\" titles (i.e. short), but sometimes consists of whole sentences, which are also present in the \"text\" field.\nThis results in very long passages as well as duplications.\nIn addition, both title and text contain specially formatted content.\nFor example, the words used in titles are often connected with underscores:\n\n> 'Apple_Magic_Mouse'\n\nAnd texts begin with special characters to distinguish headings and subheadings:\n\n> 'Wirtschaft_der_Vereinigten_Staaten\\n\\n== Verschuldung ==\\nEin durchschnittlicher Haushalt (...)'\n\nLine breaks are also frequently found, as you can see.\n\nOf course, it depends on the application whether these things become a problem or not.\nHowever, it was decided to release two variants of the original dataset:\n- The 'original' variant leaves the titles and texts as they are. There are no modifications.\n- The 'processed' variant removes the title completely and simplifies the texts by removing the special formatting.\n\nThe creation of both variants can be viewed in create_dataset.py.\nIn particular, the following parameters were used:\n- 'original': 'SPLIT=test/train, TEXT_PREPROCESSING=False, KEEP_TITLE=True'\n- 'processed': 'SPLIT=test/Train, TEXT_PREPROCESSING=True, KEEP_TITLE=False'\n\nOne final thing to mention: The IDs for queries and the corpus should not match!!!\nDuring the evaluation using BEIR, it was found that if these IDs match, the result for that entry is completely removed. \nThis means some of the results are missing.\nA correct calculation of the overall result is no longer possible.\nHave a look into BEIR's URL for further understanding.", "## Dataset Usage\nAs earlier mentioned, this dataset is intended to be used with the BEIR benchmark framework.\nThe file and data structure required for BEIR can only be used to a limited extent with Huggingface Datasets or it is necessary to define multiple dataset repositories at once.\nTo make it easier, the dl_dataset.py script is provided to download the dataset and to ensure the correct file and folder structure.\n\n\n\nAlternatively, the data sets can be downloaded directly:\n- URL\n- URL\n\nNow you can use the downloaded files in BEIR framework:\n- For Example: evaluate_sbert.py\n- Just set variable '\"dataset\"' to '\"germandpr-beir-dataset/processed/test\"' or '\"germandpr-beir-dataset/original/test\"'.\n- Same goes for '\"train\"'.", "## Dataset Sizes\n- Original train 'corpus' size, 'queries' size and 'qrels' size: '24009', '9275' and '9275'\n- Original test 'corpus' size, 'queries' size and 'qrels' size: '2876', '1025' and '1025'\n\n- Processed train 'corpus' size, 'queries' size and 'qrels' size: '23993', '9275' and '9275'\n- Processed test 'corpus' size, 'queries' size and 'qrels' size: '2875' and '1025' and '1025'", "## Languages\n\nThis dataset only supports german (aka. de, DE).", "## Acknowledgment\n\nThe dataset was initially created as \"deepset/germanDPR\" by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at URL.\n\nThis work is a collaboration between Technical University of Applied Sciences Wildau (TH Wildau) and URL GmbH.\nYou can contact us via:\n* Philipp Müller (M.Eng.); Author\n* Prof. Dr. Janett Mohnke; TH Wildau\n* Dr. Matthias Boldt, Jörg Oehmichen; URL GmbH \n\nThis work was funded by the European Regional Development Fund (EFRE) and the State of Brandenburg. Project/Vorhaben: \"ProFIT: Natürlichsprachliche Dialogassistenten in der Pflege\".\n\n<div style=\"display:flex\">\n <div style=\"padding-left:20px;\">\n <a href=\"URL src=\"URL alt=\"Logo of European Regional Development Fund (EFRE)\" width=\"200\"/></a>\n </div>\n <div style=\"padding-left:20px;\">\n <a href=\"URL\"><img src=\"URL alt=\"Logo of senseaition GmbH\" width=\"200\"/></a>\n </div>\n <div style=\"padding-left:20px;\">\n <a href=\"URL\"><img src=\"URL alt=\"Logo of TH Wildau\" width=\"180\"/></a>\n </div>\n</div>" ]

[ "TAGS\n#task_categories-sentence-similarity #task_categories-feature-extraction #task_categories-text-retrieval #task_categories-question-answering #task_categories-other #task_ids-document-retrieval #task_ids-open-domain-qa #task_ids-closed-domain-qa #multilinguality-monolingual #size_categories-10K<n<100K #language-German #information retrieval #ir #documents retrieval #passage retrieval #beir #benchmark #qrel #sts #semantic search #arxiv-2104.08663 #region-us \n", "# Dataset Card for germanDPR-beir", "## Dataset Summary\n\nThis dataset can be used for BEIR evaluation based on deepset/germanDPR.\nIt already has been used to evaluate a newly trained bi-encoder model.\nThe benchmark framework requires a particular dataset structure by default which has been created locally and uploaded here.\n\nAcknowledgement: The dataset was initially created as \"germanDPR\" by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at URL.", "## Dataset Creation\nFirst, the original dataset deepset/germanDPR was converted into three files for BEIR compatibility:\n - The first file is 'URL' and contains an ID and a question in each line.\n - The second file, 'URL', contains in each line an ID, a title, a text and some metadata.\n - In the 'qrel' folder is the third file. It connects every question from 'URL' (via 'q_id') with a relevant text/answer from 'URL' (via 'c_id')\n \nThis process has been done for 'train' and 'test' split separately based on the original germanDPR dataset.\nApproaching the dataset creation like that is necessary because queries AND corpus both differ in deepset's germanDPR dataset\nand it might be confusion changing this specific split.\nIn conclusion, queries and corpus differ between train and test split and not only qrels data!\nNote: If you want one big corpus use 'datasets.concatenate_datasets()'.\n\nIn the original dataset, there is one passage containing the answer and three \"wrong\" passages for each question.\nDuring the creation of this customized dataset, all four passages are added, but only if they are not already present (... meaning they have been deduplicated).\n\nIt should be noted, that BEIR is combining 'title' + 'text' in 'URL' to a new string which may produce odd results:\nThe original germanDPR dataset does not always contain \"classical\" titles (i.e. short), but sometimes consists of whole sentences, which are also present in the \"text\" field.\nThis results in very long passages as well as duplications.\nIn addition, both title and text contain specially formatted content.\nFor example, the words used in titles are often connected with underscores:\n\n> 'Apple_Magic_Mouse'\n\nAnd texts begin with special characters to distinguish headings and subheadings:\n\n> 'Wirtschaft_der_Vereinigten_Staaten\\n\\n== Verschuldung ==\\nEin durchschnittlicher Haushalt (...)'\n\nLine breaks are also frequently found, as you can see.\n\nOf course, it depends on the application whether these things become a problem or not.\nHowever, it was decided to release two variants of the original dataset:\n- The 'original' variant leaves the titles and texts as they are. There are no modifications.\n- The 'processed' variant removes the title completely and simplifies the texts by removing the special formatting.\n\nThe creation of both variants can be viewed in create_dataset.py.\nIn particular, the following parameters were used:\n- 'original': 'SPLIT=test/train, TEXT_PREPROCESSING=False, KEEP_TITLE=True'\n- 'processed': 'SPLIT=test/Train, TEXT_PREPROCESSING=True, KEEP_TITLE=False'\n\nOne final thing to mention: The IDs for queries and the corpus should not match!!!\nDuring the evaluation using BEIR, it was found that if these IDs match, the result for that entry is completely removed. \nThis means some of the results are missing.\nA correct calculation of the overall result is no longer possible.\nHave a look into BEIR's URL for further understanding.", "## Dataset Usage\nAs earlier mentioned, this dataset is intended to be used with the BEIR benchmark framework.\nThe file and data structure required for BEIR can only be used to a limited extent with Huggingface Datasets or it is necessary to define multiple dataset repositories at once.\nTo make it easier, the dl_dataset.py script is provided to download the dataset and to ensure the correct file and folder structure.\n\n\n\nAlternatively, the data sets can be downloaded directly:\n- URL\n- URL\n\nNow you can use the downloaded files in BEIR framework:\n- For Example: evaluate_sbert.py\n- Just set variable '\"dataset\"' to '\"germandpr-beir-dataset/processed/test\"' or '\"germandpr-beir-dataset/original/test\"'.\n- Same goes for '\"train\"'.", "## Dataset Sizes\n- Original train 'corpus' size, 'queries' size and 'qrels' size: '24009', '9275' and '9275'\n- Original test 'corpus' size, 'queries' size and 'qrels' size: '2876', '1025' and '1025'\n\n- Processed train 'corpus' size, 'queries' size and 'qrels' size: '23993', '9275' and '9275'\n- Processed test 'corpus' size, 'queries' size and 'qrels' size: '2875' and '1025' and '1025'", "## Languages\n\nThis dataset only supports german (aka. de, DE).", "## Acknowledgment\n\nThe dataset was initially created as \"deepset/germanDPR\" by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at URL.\n\nThis work is a collaboration between Technical University of Applied Sciences Wildau (TH Wildau) and URL GmbH.\nYou can contact us via:\n* Philipp Müller (M.Eng.); Author\n* Prof. Dr. Janett Mohnke; TH Wildau\n* Dr. Matthias Boldt, Jörg Oehmichen; URL GmbH \n\nThis work was funded by the European Regional Development Fund (EFRE) and the State of Brandenburg. Project/Vorhaben: \"ProFIT: Natürlichsprachliche Dialogassistenten in der Pflege\".\n\n<div style=\"display:flex\">\n <div style=\"padding-left:20px;\">\n <a href=\"URL src=\"URL alt=\"Logo of European Regional Development Fund (EFRE)\" width=\"200\"/></a>\n </div>\n <div style=\"padding-left:20px;\">\n <a href=\"URL\"><img src=\"URL alt=\"Logo of senseaition GmbH\" width=\"200\"/></a>\n </div>\n <div style=\"padding-left:20px;\">\n <a href=\"URL\"><img src=\"URL alt=\"Logo of TH Wildau\" width=\"180\"/></a>\n </div>\n</div>" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"small-roots_en\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

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

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

[ "license:creativeml-openrail-m", "region:us" ]

[]

[]

[ "# Supreme Module Module embedding", "## Samples\n\n<img alt=\"Samples\" src=\"URL style=\"max-height: 80vh\"/>\n<img alt=\"Comparsion\" src=\"URL style=\"max-height: 80vh\"/>", "## About\n\nUse this Stable Diffusion embedding to achieve the Project Diva Supreme outfit", "## Usage\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder \nTo use it in a prompt add __supreme_module-*__ \n*Note, that this embedding may require more than usual seed rolling and prompt finetuning*\n\nAdd ( :1.0) around it to modify its weight", "## Additional info\n\nIn order to improve some details you can use the following words: \nPositive prompt: blue eyes, white blouse, black thighhighs \nNegative prompt: black blouse", "## Included Files\n\n- 10000 steps Usage: supreme_module-10000\n- 15000 steps Usage: supreme_module-15000", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "# Supreme Module Module embedding", "## Samples\n\n<img alt=\"Samples\" src=\"URL style=\"max-height: 80vh\"/>\n<img alt=\"Comparsion\" src=\"URL style=\"max-height: 80vh\"/>", "## About\n\nUse this Stable Diffusion embedding to achieve the Project Diva Supreme outfit", "## Usage\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder \nTo use it in a prompt add __supreme_module-*__ \n*Note, that this embedding may require more than usual seed rolling and prompt finetuning*\n\nAdd ( :1.0) around it to modify its weight", "## Additional info\n\nIn order to improve some details you can use the following words: \nPositive prompt: blue eyes, white blouse, black thighhighs \nNegative prompt: black blouse", "## Included Files\n\n- 10000 steps Usage: supreme_module-10000\n- 15000 steps Usage: supreme_module-15000", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here" ]

[ "license:openrail", "doi:10.57967/hf/0308", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-openrail #doi-10.57967/hf/0308 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"biomedical-topic-categorization-cased\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"biomedical-topic-categorization-cased\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:text-to-image", "annotations_creators:machine-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:huggan/KimiNoNaWa", "language:en", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "## Examples\n!URL\n\n> a cityscape with a cityscape of buildings" ]

[ "TAGS\n#task_categories-text-to-image #annotations_creators-machine-generated #language_creators-other #multilinguality-monolingual #size_categories-n<1K #source_datasets-huggan/KimiNoNaWa #language-English #license-cc-by-nc-sa-4.0 #region-us \n", "## Examples\n!URL\n\n> a cityscape with a cityscape of buildings" ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:mt", "license:cc-by-nc-sa-4.0", "masri", "maltese", "masri-project", "malta", "test corpus", "region:us" ]

[]

[ "mt" ]

[ "# Dataset Card for masri_test", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: MASRI Project\n- Repository: MASRI Data Repo\n- Point of Contact: Carlos Mena, Andrea De Marco, Claudia Borg", "### Dataset Summary\nThe MASRI-TEST CORPUS was created out of YouTube videos belonging to the channel of the University of Malta. It has a length of 1 hour and it is gender balanced, as it has the same number of male and female speakers.", "### Example Usage\nThe MASRI-TEST contains only the test split:\n\nIt is also valid to do:", "### Supported Tasks\nautomatic-speech-recognition: The dataset can be used to test a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).", "### Languages\nThe language of the corpus is Maltese.", "## Dataset Structure", "### Data Instances", "### Data Fields\n* 'audio_id' (string) - id of audio segment\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'speaker_id' (string) - id of speaker\n* 'gender' (string) - gender of speaker (male or female)\n* 'duration' (float32) - duration of the audio file in seconds.\n* 'normalized_text' (string) - normalized audio segment transcription", "### Data Splits\nThe corpus counts just with the test split which has a total of 668 speech files from 17 male speakers and 17 female speakers with a total duration of 1 hour.", "## Dataset Creation", "### Curation Rationale\nThe MASRI-TEST CORPUS (MTSC) has the following characteristics:\n* The MTSC has an exact duration of 1 hours and 0 minutes. It has 668 audio files.\n* The MTSC has recordings from 34 different speakers: 17 men and 17 women.\n* Data in MTSC is classified by speaker. Therefore, all the recordings of each individual speaker are stored in one single directory.\n* Data is also classified according to the gender (male/female) of the speakers.\n* Every audio file in the MTSC has a duration between 3 and 10 seconds approximately.\n* Audio files in the MTSC are distributed in a 16khz@16bit mono format.\n* Transcriptions in MTSC are in lowercase. No punctuation marks are permitted except for dashes (-) and apostrophes (') due to their importance in Maltese orthography.", "### Source Data", "#### Initial Data Collection and Normalization\nThe MASRI-TEST CORPUS was possible due to a collaboration of two different Universities. The data selection and audio segmentation was performed by the CIEMPIESS-UNAM Project at the Universidad Nacional Autónoma de México (UNAM) in Mexico City. The audio transcription and corpus edition was performed by the MASRI Team at the University of Malta in the Msida Campus.", "### Annotations", "#### Annotation process\nProper nouns and other words pronounced in languages other than Maltese (mainly from English, Italian, French and German) were transcribed in their respective orthographic system.", "#### Who are the annotators?\nThe audio transcription was performed by expert native speakers at the University of Malta in the Msida Campus.", "### Personal and Sensitive Information\nThe dataset could contain names revealing the identity of some speakers; on the other side, the recordings come from a publicly repository (YouTube), so, there is not a real intent of the participants to be anonymized. Anyway, you agree to not attempt to determine the identity of speakers in this dataset.\nNotice: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here?, please:\n* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.\n* Clearly identify the copyrighted work claimed to be infringed.\n* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.\n* Send the request to Carlos Mena\nTake down: We will comply to legitimate requests by removing the affected sources from the corpus.", "## Considerations for Using the Data", "### Social Impact of Dataset\nThis dataset is challenging because it contains spontaneous speech; so, it will be helpful for the ASR community to evaluate their acoustic models in Maltese with it.", "### Discussion of Biases\nThe dataset intents to be gender balanced. It is comprised of 17 male speakers and 17 female speakers.", "### Other Known Limitations\nNeither the MASRI Team or the CIEMPIESS-UNAM Project guarantee the accuracy of this corpus, nor its suitability for any specific purpose. As a matter of fact, a number of errors, omissions and inconsistencies are expected to be found within the corpus.", "### Dataset Curators\nThe audio recordings were collected and segmented by students belonging to the social service program \"Desarrollo de Tecnologías del Habla\", it was curated by Carlos Daniel Hernández Mena and its transcriptions were manually performed by Ayrton-Didier Brincat during 2020.", "### Licensing Information\nCC-BY-NC-SA-4.0. The copyright remains with the original owners of the video.\nAs the data is taken from YouTube, we invoke the same argument of \"fair use\" as in the Voxlingua107 dataset, which is:\n\"While YouTube users own the copyright to their own videos, using the audio in the videos for training speech recognition models has very limited and transformative purpose and qualifies thus as \"fair use\" of copyrighted materials. YouTube’s terms of service forbid downloading, storing and distribution of videos. However, the aim of this rule is clearly to forbid unfair monetization of the content by third-party sites and applications. Our dataset contains the videos in segmented audio-only form that makes the monetization of the actual distributed content extremely difficult.\"", "### Contributions\nThe authors would like to thank to Alberto Templos Carbajal, Elena Vera and Angélica Gutiérrez for their support to the social service program \"Desarrollo de Tecnologías del Habla\" at the \"Facultad de Ingeniería (FI)\" of the Universidad Nacional Autónoma de México (UNAM). We also thank to the social service students for all the hard work during the audio segmentation." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-Maltese #license-cc-by-nc-sa-4.0 #masri #maltese #masri-project #malta #test corpus #region-us \n", "# Dataset Card for masri_test", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: MASRI Project\n- Repository: MASRI Data Repo\n- Point of Contact: Carlos Mena, Andrea De Marco, Claudia Borg", "### Dataset Summary\nThe MASRI-TEST CORPUS was created out of YouTube videos belonging to the channel of the University of Malta. It has a length of 1 hour and it is gender balanced, as it has the same number of male and female speakers.", "### Example Usage\nThe MASRI-TEST contains only the test split:\n\nIt is also valid to do:", "### Supported Tasks\nautomatic-speech-recognition: The dataset can be used to test a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).", "### Languages\nThe language of the corpus is Maltese.", "## Dataset Structure", "### Data Instances", "### Data Fields\n* 'audio_id' (string) - id of audio segment\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'speaker_id' (string) - id of speaker\n* 'gender' (string) - gender of speaker (male or female)\n* 'duration' (float32) - duration of the audio file in seconds.\n* 'normalized_text' (string) - normalized audio segment transcription", "### Data Splits\nThe corpus counts just with the test split which has a total of 668 speech files from 17 male speakers and 17 female speakers with a total duration of 1 hour.", "## Dataset Creation", "### Curation Rationale\nThe MASRI-TEST CORPUS (MTSC) has the following characteristics:\n* The MTSC has an exact duration of 1 hours and 0 minutes. It has 668 audio files.\n* The MTSC has recordings from 34 different speakers: 17 men and 17 women.\n* Data in MTSC is classified by speaker. Therefore, all the recordings of each individual speaker are stored in one single directory.\n* Data is also classified according to the gender (male/female) of the speakers.\n* Every audio file in the MTSC has a duration between 3 and 10 seconds approximately.\n* Audio files in the MTSC are distributed in a 16khz@16bit mono format.\n* Transcriptions in MTSC are in lowercase. No punctuation marks are permitted except for dashes (-) and apostrophes (') due to their importance in Maltese orthography.", "### Source Data", "#### Initial Data Collection and Normalization\nThe MASRI-TEST CORPUS was possible due to a collaboration of two different Universities. The data selection and audio segmentation was performed by the CIEMPIESS-UNAM Project at the Universidad Nacional Autónoma de México (UNAM) in Mexico City. The audio transcription and corpus edition was performed by the MASRI Team at the University of Malta in the Msida Campus.", "### Annotations", "#### Annotation process\nProper nouns and other words pronounced in languages other than Maltese (mainly from English, Italian, French and German) were transcribed in their respective orthographic system.", "#### Who are the annotators?\nThe audio transcription was performed by expert native speakers at the University of Malta in the Msida Campus.", "### Personal and Sensitive Information\nThe dataset could contain names revealing the identity of some speakers; on the other side, the recordings come from a publicly repository (YouTube), so, there is not a real intent of the participants to be anonymized. Anyway, you agree to not attempt to determine the identity of speakers in this dataset.\nNotice: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here?, please:\n* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.\n* Clearly identify the copyrighted work claimed to be infringed.\n* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.\n* Send the request to Carlos Mena\nTake down: We will comply to legitimate requests by removing the affected sources from the corpus.", "## Considerations for Using the Data", "### Social Impact of Dataset\nThis dataset is challenging because it contains spontaneous speech; so, it will be helpful for the ASR community to evaluate their acoustic models in Maltese with it.", "### Discussion of Biases\nThe dataset intents to be gender balanced. It is comprised of 17 male speakers and 17 female speakers.", "### Other Known Limitations\nNeither the MASRI Team or the CIEMPIESS-UNAM Project guarantee the accuracy of this corpus, nor its suitability for any specific purpose. As a matter of fact, a number of errors, omissions and inconsistencies are expected to be found within the corpus.", "### Dataset Curators\nThe audio recordings were collected and segmented by students belonging to the social service program \"Desarrollo de Tecnologías del Habla\", it was curated by Carlos Daniel Hernández Mena and its transcriptions were manually performed by Ayrton-Didier Brincat during 2020.", "### Licensing Information\nCC-BY-NC-SA-4.0. The copyright remains with the original owners of the video.\nAs the data is taken from YouTube, we invoke the same argument of \"fair use\" as in the Voxlingua107 dataset, which is:\n\"While YouTube users own the copyright to their own videos, using the audio in the videos for training speech recognition models has very limited and transformative purpose and qualifies thus as \"fair use\" of copyrighted materials. YouTube’s terms of service forbid downloading, storing and distribution of videos. However, the aim of this rule is clearly to forbid unfair monetization of the content by third-party sites and applications. Our dataset contains the videos in segmented audio-only form that makes the monetization of the actual distributed content extremely difficult.\"", "### Contributions\nThe authors would like to thank to Alberto Templos Carbajal, Elena Vera and Angélica Gutiérrez for their support to the social service program \"Desarrollo de Tecnologías del Habla\" at the \"Facultad de Ingeniería (FI)\" of the Universidad Nacional Autónoma de México (UNAM). We also thank to the social service students for all the hard work during the audio segmentation." ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:mt", "license:cc-by-nc-sa-4.0", "masri", "maltese", "masri-project", "malta", "evaluation corpus", "development corpus", "dev", "region:us" ]

[]

[ "mt" ]

[ "# Dataset Card for masri_dev", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: MASRI Project\n- Repository: MASRI Data Repo\n- Point of Contact: Carlos Mena, Andrea De Marco, Claudia Borg", "### Dataset Summary\nThe MASRI-DEV CORPUS was created out of YouTube videos belonging to the channel of the University of Malta. It has a length of 1 hour and it is gender balanced, as it has the same number of male and female speakers.", "### Example Usage\nThe MASRI-DEV contains only the validation split:\n\nIt is also valid to do:", "### Supported Tasks\nautomatic-speech-recognition: The dataset can be used as a development/validation corpus when training a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).", "### Languages\nThe language of the corpus is Maltese.", "## Dataset Structure", "### Data Instances", "### Data Fields\n* 'audio_id' (string) - id of audio segment\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'speaker_id' (string) - id of speaker\n* 'gender' (string) - gender of speaker (male or female)\n* 'duration' (float32) - duration of the audio file in seconds.\n* 'normalized_text' (string) - normalized audio segment transcription", "### Data Splits\nThe corpus counts just with the validation split which has a total of 669 speech files from 9 male speakers and 9 female speakers with a total duration of 1 hour.", "## Dataset Creation", "### Curation Rationale\nThe MASRI-DEV CORPUS (MDVC) has the following characteristics:\n* The MDVC has an exact duration of 1 hours and 0 minutes. It has 669 audio files.\n* The MDVC has recordings from 18 different speakers: 9 men and 9 women.\n* Data in MDVC is classified by speaker. Therefore, all the recordings of each individual speaker are stored in one single directory.\n* Data is also classified according to the gender (male/female) of the speakers.\n* Every audio file in the MDVC has a duration between 3 and 10 seconds approximately.\n* Audio files in the MDVC are distributed in a 16khz@16bit mono format.\n* Transcriptions in MDVC are in lowercase. No punctuation marks are permitted except for dashes (-) and apostrophes (') due to their importance in Maltese orthography.", "### Source Data", "#### Initial Data Collection and Normalization\nThe MASRI-DEV CORPUS was possible due to a collaboration of two different Universities. The data selection and audio segmentation was performed by the CIEMPIESS-UNAM Project at the Universidad Nacional Autónoma de México (UNAM) in Mexico City. The audio transcription and corpus edition was performed by the MASRI Team at the University of Malta in the Msida Campus.", "### Annotations", "#### Annotation process\nProper nouns and other words pronounced in languages other than Maltese (mainly from English, Italian, French and German) were transcribed in their respective orthographic system.", "#### Who are the annotators?\nThe audio transcription was performed by expert native speakers at the University of Malta in the Msida Campus.", "### Personal and Sensitive Information\nThe dataset could contain names revealing the identity of some speakers; on the other side, the recordings come from a publicly repository (YouTube), so, there is not a real intent of the participants to be anonymized. Anyway, you agree to not attempt to determine the identity of speakers in this dataset.\nNotice: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here?, please:\n* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.\n* Clearly identify the copyrighted work claimed to be infringed.\n* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.\n* Send the request to Carlos Mena\nTake down: We will comply to legitimate requests by removing the affected sources from the corpus.", "## Considerations for Using the Data", "### Social Impact of Dataset\nThis dataset is challenging because it contains spontaneous speech; so, it will be helpful for the ASR community to validate their acoustic models in Maltese with it.", "### Discussion of Biases\nThe dataset intents to be gender balanced. It is comprised of 9 male speakers and 9 female speakers.", "### Other Known Limitations\nNeither the MASRI Team or the CIEMPIESS-UNAM Project guarantee the accuracy of this corpus, nor its suitability for any specific purpose. As a matter of fact, a number of errors, omissions and inconsistencies are expected to be found within the corpus.", "### Dataset Curators\nThe audio recordings were collected and segmented by students belonging to the social service program \"Desarrollo de Tecnologías del Habla\", it was curated by Carlos Daniel Hernández Mena and its transcriptions were manually performed by Ayrton-Didier Brincat during 2020.", "### Licensing Information\nCC-BY-NC-SA-4.0. The copyright remains with the original owners of the video.\nAs the data is taken from YouTube, we invoke the same argument of \"fair use\" as in the Voxlingua107 dataset, which is:\n\"While YouTube users own the copyright to their own videos, using the audio in the videos for training speech recognition models has very limited and transformative purpose and qualifies thus as \"fair use\" of copyrighted materials. YouTube’s terms of service forbid downloading, storing and distribution of videos. However, the aim of this rule is clearly to forbid unfair monetization of the content by third-party sites and applications. Our dataset contains the videos in segmented audio-only form that makes the monetization of the actual distributed content extremely difficult.\"", "### Contributions\nThe authors would like to thank to Alberto Templos Carbajal, Elena Vera and Angélica Gutiérrez for their support to the social service program \"Desarrollo de Tecnologías del Habla\" at the \"Facultad de Ingeniería (FI)\" of the Universidad Nacional Autónoma de México (UNAM). We also thank to the social service students for all the hard work during the audio segmentation." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-Maltese #license-cc-by-nc-sa-4.0 #masri #maltese #masri-project #malta #evaluation corpus #development corpus #dev #region-us \n", "# Dataset Card for masri_dev", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: MASRI Project\n- Repository: MASRI Data Repo\n- Point of Contact: Carlos Mena, Andrea De Marco, Claudia Borg", "### Dataset Summary\nThe MASRI-DEV CORPUS was created out of YouTube videos belonging to the channel of the University of Malta. It has a length of 1 hour and it is gender balanced, as it has the same number of male and female speakers.", "### Example Usage\nThe MASRI-DEV contains only the validation split:\n\nIt is also valid to do:", "### Supported Tasks\nautomatic-speech-recognition: The dataset can be used as a development/validation corpus when training a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).", "### Languages\nThe language of the corpus is Maltese.", "## Dataset Structure", "### Data Instances", "### Data Fields\n* 'audio_id' (string) - id of audio segment\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'speaker_id' (string) - id of speaker\n* 'gender' (string) - gender of speaker (male or female)\n* 'duration' (float32) - duration of the audio file in seconds.\n* 'normalized_text' (string) - normalized audio segment transcription", "### Data Splits\nThe corpus counts just with the validation split which has a total of 669 speech files from 9 male speakers and 9 female speakers with a total duration of 1 hour.", "## Dataset Creation", "### Curation Rationale\nThe MASRI-DEV CORPUS (MDVC) has the following characteristics:\n* The MDVC has an exact duration of 1 hours and 0 minutes. It has 669 audio files.\n* The MDVC has recordings from 18 different speakers: 9 men and 9 women.\n* Data in MDVC is classified by speaker. Therefore, all the recordings of each individual speaker are stored in one single directory.\n* Data is also classified according to the gender (male/female) of the speakers.\n* Every audio file in the MDVC has a duration between 3 and 10 seconds approximately.\n* Audio files in the MDVC are distributed in a 16khz@16bit mono format.\n* Transcriptions in MDVC are in lowercase. No punctuation marks are permitted except for dashes (-) and apostrophes (') due to their importance in Maltese orthography.", "### Source Data", "#### Initial Data Collection and Normalization\nThe MASRI-DEV CORPUS was possible due to a collaboration of two different Universities. The data selection and audio segmentation was performed by the CIEMPIESS-UNAM Project at the Universidad Nacional Autónoma de México (UNAM) in Mexico City. The audio transcription and corpus edition was performed by the MASRI Team at the University of Malta in the Msida Campus.", "### Annotations", "#### Annotation process\nProper nouns and other words pronounced in languages other than Maltese (mainly from English, Italian, French and German) were transcribed in their respective orthographic system.", "#### Who are the annotators?\nThe audio transcription was performed by expert native speakers at the University of Malta in the Msida Campus.", "### Personal and Sensitive Information\nThe dataset could contain names revealing the identity of some speakers; on the other side, the recordings come from a publicly repository (YouTube), so, there is not a real intent of the participants to be anonymized. Anyway, you agree to not attempt to determine the identity of speakers in this dataset.\nNotice: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here?, please:\n* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.\n* Clearly identify the copyrighted work claimed to be infringed.\n* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.\n* Send the request to Carlos Mena\nTake down: We will comply to legitimate requests by removing the affected sources from the corpus.", "## Considerations for Using the Data", "### Social Impact of Dataset\nThis dataset is challenging because it contains spontaneous speech; so, it will be helpful for the ASR community to validate their acoustic models in Maltese with it.", "### Discussion of Biases\nThe dataset intents to be gender balanced. It is comprised of 9 male speakers and 9 female speakers.", "### Other Known Limitations\nNeither the MASRI Team or the CIEMPIESS-UNAM Project guarantee the accuracy of this corpus, nor its suitability for any specific purpose. As a matter of fact, a number of errors, omissions and inconsistencies are expected to be found within the corpus.", "### Dataset Curators\nThe audio recordings were collected and segmented by students belonging to the social service program \"Desarrollo de Tecnologías del Habla\", it was curated by Carlos Daniel Hernández Mena and its transcriptions were manually performed by Ayrton-Didier Brincat during 2020.", "### Licensing Information\nCC-BY-NC-SA-4.0. The copyright remains with the original owners of the video.\nAs the data is taken from YouTube, we invoke the same argument of \"fair use\" as in the Voxlingua107 dataset, which is:\n\"While YouTube users own the copyright to their own videos, using the audio in the videos for training speech recognition models has very limited and transformative purpose and qualifies thus as \"fair use\" of copyrighted materials. YouTube’s terms of service forbid downloading, storing and distribution of videos. However, the aim of this rule is clearly to forbid unfair monetization of the content by third-party sites and applications. Our dataset contains the videos in segmented audio-only form that makes the monetization of the actual distributed content extremely difficult.\"", "### Contributions\nThe authors would like to thank to Alberto Templos Carbajal, Elena Vera and Angélica Gutiérrez for their support to the social service program \"Desarrollo de Tecnologías del Habla\" at the \"Facultad de Ingeniería (FI)\" of the Universidad Nacional Autónoma de México (UNAM). We also thank to the social service students for all the hard work during the audio segmentation." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"common3k-train-prepared\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"common3k-train-prepared\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"stackoverflow-open-status-classification\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"stackoverflow-open-status-classification\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"stackoverflow-open-status-classification-albert-tokenized\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"stackoverflow-open-status-classification-albert-tokenized\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"hldatasetv2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"hldatasetv2\"\n\nMore Information needed" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:is", "license:cc-by-4.0", "samromur", "children's speech", "icelandic: iceland", "icelandic children", "icelandic kids", "kids", "region:us" ]

[]

[ "is" ]

[ "# Dataset Card for samromur_children", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: Samrómur Children Icelandic Speech 1.0\n- Repository: LDC\n- Paper: Samrómur Children: An Icelandic Speech Corpus\n- Point of Contact: Carlos Mena, Jón Guðnason", "### Dataset Summary\n\nThe Samrómur Children Corpus consists of audio recordings and metadata files containing prompts read by the participants. It contains more than 137000 validated speech-recordings uttered by Icelandic children.\n\nThe corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021).", "### Example Usage\nThe Samrómur Children Corpus is divided in 3 splits: train, validation and test. To load a specific split pass its name as a config name:\n\nTo load an specific split (for example, the validation split) do:", "### Supported Tasks\nautomatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).", "### Languages\nThe audio is in Icelandic.\nThe reading prompts were gathered from a variety of sources, mainly from the Icelandic Gigaword Corpus. The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the Icelandic Web of Science.", "## Dataset Structure", "### Data Instances", "### Data Fields\n* 'audio_id' (string) - id of audio segment\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'speaker_id' (string) - id of speaker\n* 'gender' (string) - gender of speaker (male or female)\n* 'age' (string) - range of age of the speaker: Younger (15-35), Middle-aged (36-60) or Elderly (61+).\n* 'duration' (float32) - duration of the audio file in seconds.\n* 'normalized_text' (string) - normalized audio segment transcription.", "### Data Splits\nThe corpus is split into train, dev, and test portions. Lenghts of every portion are: train = 127h25m, test = 1h50m, dev=1h50m.\n\nTo load an specific portion please see the above section \"Example Usage\".", "## Dataset Creation", "### Curation Rationale\n\nIn the field of Automatic Speech Recognition (ASR) is a known fact that the children's speech is particularly hard to recognise due to its high variability produced by developmental changes in children's anatomy and speech production skills.\n\nFor this reason, the criteria of selection for the train/dev/test portions have to take into account the children's age. Nevertheless, the Samrómur Children is an unbalanced corpus in terms of gender and age of the speakers. This means that the corpus has, for example, a total of 1667 female speakers (73h38m) versus 1412 of male speakers (52h26m).\n\nThese unbalances impose conditions in the type of the experiments than can be performed with the corpus. For example, a equal number of female and male speakers through certain ranges of age is impossible. So, if one can't have a perfectly balance corpus in the training set, at least one can have it in the test portion.\n\nThe test portion of the Samrómur Children was meticulously selected to cover ages between 6 to 16 years in both female and male speakers. Every of these range of age in both genders have a total duration of 5 minutes each.\n\nThe development portion of the corpus contains only speakers with an unknown gender information. Both test and dev sets have a total duration of 1h50m each.\n\nIn order to perform fairer experiments, speakers in the train and test sets are not shared. Nevertheless, there is only one speaker shared between the train and development set. It can be identified with the speaker ID=010363. However, no audio files are shared between these two sets.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was collected using the website URL, code of which is available at URL The age range selected for this corpus is between 4 and 17 years.\n\nThe original audio was collected at 44.1 kHz or 48 kHz sampling rate as *.wav files, which was down-sampled to 16 kHz and converted to *.flac. Each recording contains one read sentence from a script. The script contains 85.080 unique sentences and 90.838 unique tokens. \n\nThere was no identifier other than the session ID, which is used as the speaker ID. The corpus is distributed with a metadata file with a detailed information on each utterance and speaker. The madata file is encoded as UTF-8 Unicode.\n\nThe prompts were gathered from a variety of sources, mainly from The Icelandic Gigaword Corpus, which is available at URL The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the Icelandic Web of Science.", "### Annotations", "#### Annotation process\n\nPrompts were pulled from these corpora if they met the criteria of having only letters which are present in the Icelandic alphabet, and if they are listed in the DIM: Database Icelandic Morphology. \n\nThere are also synthesised prompts consisting of a name followed by a question or a demand, in order to simulate a dialogue with a smart-device.", "#### Who are the annotators?\nThe audio files content was manually verified against the prompts by one or more listener (summer students mainly).", "### Personal and Sensitive Information\nThe dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\nThis is the first ASR corpus of Icelandic children.", "### Discussion of Biases\n\n* The utterances were recorded by a smartphone or the web app. \n\n* Participants self-reported their age group, gender, and the native language.\n\n* Participants are aged between 4 to 17 years. \n \n* The corpus contains 137597 utterances from 3175 speakers, totalling 131 hours.\n\n* The amount of data due to female speakers is 73h38m, the amount of data due to male speakers is 52h26m and the amount of data due to speakers with an unknown gender information is 05h02m\n\n* The number of female speakers is 1667, the number of male speakers is 1412. The number of speakers with an unknown gender information is 96.\n\n* The audios due to female speakers are 78993, the audios due to male speakers are 53927 and the audios due to speakers with an unknown gender information are 4677.", "### Other Known Limitations\n\"Samrómur Children: Icelandic Speech 21.09\" by the Language and Voice Laboratory (LVL) at the Reykjavik University is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.", "## Additional Information", "### Dataset Curators\n\nThe corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021). The corpus was curated by Carlos Daniel Hernández Mena in 2021.", "### Licensing Information\nCC-BY-4.0", "### Contributions\nThis project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.\n\nThe verification for the dataset was funded by the the Icelandic Directorate of Labour's Student Summer Job Program in 2020 and 2021.\n\nSpecial thanks for the summer students for all the hard work." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Icelandic #license-cc-by-4.0 #samromur #children's speech #icelandic- iceland #icelandic children #icelandic kids #kids #region-us \n", "# Dataset Card for samromur_children", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: Samrómur Children Icelandic Speech 1.0\n- Repository: LDC\n- Paper: Samrómur Children: An Icelandic Speech Corpus\n- Point of Contact: Carlos Mena, Jón Guðnason", "### Dataset Summary\n\nThe Samrómur Children Corpus consists of audio recordings and metadata files containing prompts read by the participants. It contains more than 137000 validated speech-recordings uttered by Icelandic children.\n\nThe corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021).", "### Example Usage\nThe Samrómur Children Corpus is divided in 3 splits: train, validation and test. To load a specific split pass its name as a config name:\n\nTo load an specific split (for example, the validation split) do:", "### Supported Tasks\nautomatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).", "### Languages\nThe audio is in Icelandic.\nThe reading prompts were gathered from a variety of sources, mainly from the Icelandic Gigaword Corpus. The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the Icelandic Web of Science.", "## Dataset Structure", "### Data Instances", "### Data Fields\n* 'audio_id' (string) - id of audio segment\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'speaker_id' (string) - id of speaker\n* 'gender' (string) - gender of speaker (male or female)\n* 'age' (string) - range of age of the speaker: Younger (15-35), Middle-aged (36-60) or Elderly (61+).\n* 'duration' (float32) - duration of the audio file in seconds.\n* 'normalized_text' (string) - normalized audio segment transcription.", "### Data Splits\nThe corpus is split into train, dev, and test portions. Lenghts of every portion are: train = 127h25m, test = 1h50m, dev=1h50m.\n\nTo load an specific portion please see the above section \"Example Usage\".", "## Dataset Creation", "### Curation Rationale\n\nIn the field of Automatic Speech Recognition (ASR) is a known fact that the children's speech is particularly hard to recognise due to its high variability produced by developmental changes in children's anatomy and speech production skills.\n\nFor this reason, the criteria of selection for the train/dev/test portions have to take into account the children's age. Nevertheless, the Samrómur Children is an unbalanced corpus in terms of gender and age of the speakers. This means that the corpus has, for example, a total of 1667 female speakers (73h38m) versus 1412 of male speakers (52h26m).\n\nThese unbalances impose conditions in the type of the experiments than can be performed with the corpus. For example, a equal number of female and male speakers through certain ranges of age is impossible. So, if one can't have a perfectly balance corpus in the training set, at least one can have it in the test portion.\n\nThe test portion of the Samrómur Children was meticulously selected to cover ages between 6 to 16 years in both female and male speakers. Every of these range of age in both genders have a total duration of 5 minutes each.\n\nThe development portion of the corpus contains only speakers with an unknown gender information. Both test and dev sets have a total duration of 1h50m each.\n\nIn order to perform fairer experiments, speakers in the train and test sets are not shared. Nevertheless, there is only one speaker shared between the train and development set. It can be identified with the speaker ID=010363. However, no audio files are shared between these two sets.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was collected using the website URL, code of which is available at URL The age range selected for this corpus is between 4 and 17 years.\n\nThe original audio was collected at 44.1 kHz or 48 kHz sampling rate as *.wav files, which was down-sampled to 16 kHz and converted to *.flac. Each recording contains one read sentence from a script. The script contains 85.080 unique sentences and 90.838 unique tokens. \n\nThere was no identifier other than the session ID, which is used as the speaker ID. The corpus is distributed with a metadata file with a detailed information on each utterance and speaker. The madata file is encoded as UTF-8 Unicode.\n\nThe prompts were gathered from a variety of sources, mainly from The Icelandic Gigaword Corpus, which is available at URL The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the Icelandic Web of Science.", "### Annotations", "#### Annotation process\n\nPrompts were pulled from these corpora if they met the criteria of having only letters which are present in the Icelandic alphabet, and if they are listed in the DIM: Database Icelandic Morphology. \n\nThere are also synthesised prompts consisting of a name followed by a question or a demand, in order to simulate a dialogue with a smart-device.", "#### Who are the annotators?\nThe audio files content was manually verified against the prompts by one or more listener (summer students mainly).", "### Personal and Sensitive Information\nThe dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\nThis is the first ASR corpus of Icelandic children.", "### Discussion of Biases\n\n* The utterances were recorded by a smartphone or the web app. \n\n* Participants self-reported their age group, gender, and the native language.\n\n* Participants are aged between 4 to 17 years. \n \n* The corpus contains 137597 utterances from 3175 speakers, totalling 131 hours.\n\n* The amount of data due to female speakers is 73h38m, the amount of data due to male speakers is 52h26m and the amount of data due to speakers with an unknown gender information is 05h02m\n\n* The number of female speakers is 1667, the number of male speakers is 1412. The number of speakers with an unknown gender information is 96.\n\n* The audios due to female speakers are 78993, the audios due to male speakers are 53927 and the audios due to speakers with an unknown gender information are 4677.", "### Other Known Limitations\n\"Samrómur Children: Icelandic Speech 21.09\" by the Language and Voice Laboratory (LVL) at the Reykjavik University is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.", "## Additional Information", "### Dataset Curators\n\nThe corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021). The corpus was curated by Carlos Daniel Hernández Mena in 2021.", "### Licensing Information\nCC-BY-4.0", "### Contributions\nThis project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.\n\nThe verification for the dataset was funded by the the Icelandic Directorate of Labour's Student Summer Job Program in 2020 and 2021.\n\nSpecial thanks for the summer students for all the hard work." ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:tib", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "tib" ]

[ "### Dataset Summary\n\n\nXBMU-AMDO31 dataset is a speech recognition corpus of Amdo Tibetan dialect. The open source corpus contains 31 hours of speech data and resources related to build speech recognition systems, including transcribed texts and a Tibetan pronunciation dictionary.", "### Supported Tasks and Leaderboards\n\n\nautomatic-speech-recognition: The dataset can be used to train a model for Amdo Tibetan Automatic Speech Recognition (ASR). It was recorded by 66 native speakers of Amdo Tibetan, and the recorded audio was processed and manually inspected. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at URL and ranks models based on their WER.", "### Languages\n\n\nXBMU-AMDO31 contains audio, a Tibetan pronunciation dictionary and transcription data in Amdo Tibetan.\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\n\n\nThe dataset has three splits: train, evaluation (dev) and test.Each speaker had approximately 450 sentences, with a small number of individuals having fewer than 200 sen\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThis dataset is distributed under CC BY-SA 4.0.", "### Contributions\n\n\nThanks to @speechless-z for adding this dataset." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #language-Tibetan #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nXBMU-AMDO31 dataset is a speech recognition corpus of Amdo Tibetan dialect. The open source corpus contains 31 hours of speech data and resources related to build speech recognition systems, including transcribed texts and a Tibetan pronunciation dictionary.", "### Supported Tasks and Leaderboards\n\n\nautomatic-speech-recognition: The dataset can be used to train a model for Amdo Tibetan Automatic Speech Recognition (ASR). It was recorded by 66 native speakers of Amdo Tibetan, and the recorded audio was processed and manually inspected. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at URL and ranks models based on their WER.", "### Languages\n\n\nXBMU-AMDO31 contains audio, a Tibetan pronunciation dictionary and transcription data in Amdo Tibetan.\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\n\n\nThe dataset has three splits: train, evaluation (dev) and test.Each speaker had approximately 450 sentences, with a small number of individuals having fewer than 200 sen\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThis dataset is distributed under CC BY-SA 4.0.", "### Contributions\n\n\nThanks to @speechless-z for adding this dataset." ]

[ "region:us" ]

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[ "# Dataset Card for \"stackoverflow-unified-text-open-status-classification\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"stackoverflow-unified-text-open-status-classification\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: Artifact-AI/led_large_16384_billsum_summarization\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Artifact-AI for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: Artifact-AI/led_large_16384_billsum_summarization\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Artifact-AI for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: Artifact-AI/led_large_16384_billsum_summarization\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Artifact-AI for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: Artifact-AI/led_large_16384_billsum_summarization\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Artifact-AI for evaluating this model." ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"stackoverflow-open-status-classification-albert-tokenized\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"stackoverflow-open-status-classification-albert-tokenized\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"libri-augmented-test-prepared\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"libri-augmented-test-prepared\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"step_proofs2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"step_proofs2\"\n\nMore Information needed" ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: arogyaGurkha/kobert-finetuned-squad_kor_v1\n* Dataset: squad_kor_v1\n* Config: squad_kor_v1\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Kdogs for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: arogyaGurkha/kobert-finetuned-squad_kor_v1\n* Dataset: squad_kor_v1\n* Config: squad_kor_v1\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Kdogs for evaluating this model." ]

[ "task_categories:other", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:found", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1M<n<10M", "language:sl", "license:cc-by-sa-4.0", "kolokacije", "gigafida", "region:us" ]

[]

[ "sl" ]

[ "# Dataset Card for Collocations Dictionary of Modern Slovene KSSS 1.0\n\nAlso known as \"Kolokacije 1.0\". Available in application form online: URL", "### Dataset Summary\n\nThe database of the Collocations Dictionary of Modern Slovene 1.0 contains entries for 35,862 headwords (18,043 nouns, 5,148 verbs, 10,259 adjectives and 2,412 adverbs) and 7,310,983 collocations that were automatically extracted from the Gigafida 1.0 corpus. \nFor the automatic extraction via the Sketch Engine API the authors used a specially adapted Sketch grammar for Slovene, and, based on manual evaluation, a set of parameters that determined: maximum number of collocates per grammatical relation, \nminimum frequency of a collocate, minimum frequency of a grammatical relation, minimum salience (logDice) score of a collocate, and minimum salience of a grammatical relation. \n \nThe procedure of automatic extraction, which produced a list of collocates (lemmas) in a particular relation, was followed by a set of post-processing steps:\n- removal of collocations that were represented by repetitions of the same sentence\n- preparation of full collocations by the addition of the headword, and, if needed, the third element in the grammatical relation (such as preposition). The headwords/collocates were also put in the correct case, depending on the grammatical relation.\n- addition of IDs from the Slovenian morphological lexicon Sloleks to every element in the collocation.\n\nFor a detailed description of the data, please see the paper Kosem et al. (2018).", "### Supported Tasks and Leaderboards\n\nOther (the data is a knowledge base).", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nThe structure of the original data is flattened, meaning that each collocation is its own instance.\nThe following example shows the entry for collocation '\"idealizirati preteklost\"' (*to idealize the past*), which is a collocation of the lexical unit '\"idealizirati\"' (*to idealize*).", "### Data Fields\n\n- 'collocation': the string form of the collocation;\n- 'cluster': cluster of the collocation - sometimes, but not always, corresponds to the sense;\n- 'words': tokenized collocation;\n- 'sloleks_ids': Sloleks IDs of collocation words;\n- 'gramrel': grammatical relation; \n- 'sense': sense of the collocation - curently constant (see 'cluster' for a slightly better approximate division);\n- 'id_lex_unit': ID of the lexical unit that the collocation belongs to;\n- 'lex_unit': lexical unit;\n- 'lex_unit_category': category of the lexical unit.", "## Additional Information", "### Dataset Curators\n\nIztok Kosem; et al. (please see URL for the full list).", "### Licensing Information\n\nCC BY-SA 4.0", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-found #language_creators-machine-generated #multilinguality-monolingual #size_categories-1M<n<10M #language-Slovenian #license-cc-by-sa-4.0 #kolokacije #gigafida #region-us \n", "# Dataset Card for Collocations Dictionary of Modern Slovene KSSS 1.0\n\nAlso known as \"Kolokacije 1.0\". Available in application form online: URL", "### Dataset Summary\n\nThe database of the Collocations Dictionary of Modern Slovene 1.0 contains entries for 35,862 headwords (18,043 nouns, 5,148 verbs, 10,259 adjectives and 2,412 adverbs) and 7,310,983 collocations that were automatically extracted from the Gigafida 1.0 corpus. \nFor the automatic extraction via the Sketch Engine API the authors used a specially adapted Sketch grammar for Slovene, and, based on manual evaluation, a set of parameters that determined: maximum number of collocates per grammatical relation, \nminimum frequency of a collocate, minimum frequency of a grammatical relation, minimum salience (logDice) score of a collocate, and minimum salience of a grammatical relation. \n \nThe procedure of automatic extraction, which produced a list of collocates (lemmas) in a particular relation, was followed by a set of post-processing steps:\n- removal of collocations that were represented by repetitions of the same sentence\n- preparation of full collocations by the addition of the headword, and, if needed, the third element in the grammatical relation (such as preposition). The headwords/collocates were also put in the correct case, depending on the grammatical relation.\n- addition of IDs from the Slovenian morphological lexicon Sloleks to every element in the collocation.\n\nFor a detailed description of the data, please see the paper Kosem et al. (2018).", "### Supported Tasks and Leaderboards\n\nOther (the data is a knowledge base).", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nThe structure of the original data is flattened, meaning that each collocation is its own instance.\nThe following example shows the entry for collocation '\"idealizirati preteklost\"' (*to idealize the past*), which is a collocation of the lexical unit '\"idealizirati\"' (*to idealize*).", "### Data Fields\n\n- 'collocation': the string form of the collocation;\n- 'cluster': cluster of the collocation - sometimes, but not always, corresponds to the sense;\n- 'words': tokenized collocation;\n- 'sloleks_ids': Sloleks IDs of collocation words;\n- 'gramrel': grammatical relation; \n- 'sense': sense of the collocation - curently constant (see 'cluster' for a slightly better approximate division);\n- 'id_lex_unit': ID of the lexical unit that the collocation belongs to;\n- 'lex_unit': lexical unit;\n- 'lex_unit_category': category of the lexical unit.", "## Additional Information", "### Dataset Curators\n\nIztok Kosem; et al. (please see URL for the full list).", "### Licensing Information\n\nCC BY-SA 4.0", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"masked_step_label2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"masked_step_label2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"gal_yair_8300_256x256_fixed\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"gal_yair_8300_256x256_fixed\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"gal_yair_83000_100x100_fixed\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"gal_yair_83000_100x100_fixed\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"gal_yair_8300_1664x832_fixed\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"gal_yair_8300_1664x832_fixed\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"gal_yair_83000_256x256_fixed\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"gal_yair_83000_256x256_fixed\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Godardv1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Godardv1\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:question-answering", "task_ids:extractive-qa", "multilinguality:monolingual", "size_categories:1M<", "source_datasets:extended|wikipedia", "language:en", "license:cc-by-4.0", "arxiv:1606.05250", "region:us" ]

[ "1606.05250" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is the SQuAD v1 dataset with the train/validatio/test split used in qg\\_squad.", "### Supported Tasks and Leaderboards\n\n\n* 'question-answering'", "### Languages\n\n\nEnglish (en)\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'id': a 'string' feature of id\n* 'title': a 'string' feature of title of the paragraph\n* 'context': a 'string' feature of paragraph\n* 'question': a 'string' feature of question\n* 'answers': a 'json' feature of answers", "### Data Splits" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-1M< #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-1606.05250 #region-us \n", "### Dataset Summary\n\n\nThis is the SQuAD v1 dataset with the train/validatio/test split used in qg\\_squad.", "### Supported Tasks and Leaderboards\n\n\n* 'question-answering'", "### Languages\n\n\nEnglish (en)\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'id': a 'string' feature of id\n* 'title': a 'string' feature of title of the paragraph\n* 'context': a 'string' feature of paragraph\n* 'question': a 'string' feature of question\n* 'answers': a 'json' feature of answers", "### Data Splits" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"gal_yair_83000_1664x832_fixed\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"gal_yair_83000_1664x832_fixed\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"cat-toy\"\n\nMore Information needed" ]

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

[ "task_categories:automatic-speech-recognition", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:is", "license:cc-by-4.0", "icelandic podcasts", "spontaneous icelandic", "forced-aligned", "ruv.is", "mafia aligner", "region:us" ]

[]

[ "is" ]

[ "# Dataset Card for raddromur_asr", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: Raddrómur Icelandic Speech 22.09\n- Repository: URL\n- Point of Contact: Carlos Mena, Jón Guðnason", "### Dataset Summary\n\nThe \"Raddrómur Icelandic Speech 22.09\" (\"Raddrómur Corpus\" for short) is an Icelandic corpus created by the Language and Voice Laboratory (LVL) at Reykjavík University (RU) in 2022. It is made out of radio podcasts mostly taken from RÚV (URL).", "### Example Usage\nThe Raddrómur Corpus counts with the train split only. To load the training split pass its name as a config name:\n\nTo load the specific \"train\" split do:", "### Supported Tasks\nautomatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).", "### Languages\nThe audio is in Icelandic.", "## Dataset Structure", "### Data Instances", "### Data Fields\n* 'audio_id' (string) - id of audio segment\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'podcast_id' (string) - id of the podcast\n* 'segment_num' (int32) - integer identifing the number of segment\n* 'duration' (float32) - duration of the audio file in seconds.\n* 'mafia_score' (float32) - In order to distinguish the transcriptions with fewer expected mistakes, a quality measure called \"MAFIA Score\" was added. A MAFIA Score close to zero implies a better quality transcription.\n* 'normalized_text' (string) - normalized audio segment transcription.", "### Data Splits\nThe corpus is split into train only. The lenght of the train portion is 49h09m in 13030 utterances.", "## Dataset Creation", "### Curation Rationale\n\n* The corpus was automatically segmented using the tool inaSpeechSegmenter.\n \n* The forced alignment was performed using the tool MAFIA aligner.\n \n* The corpus comes with a metadata file wich is in TSV format. This file contain the normalized transcription of the corpus and the filenames among other relevant information.\n \n* The corpus contains 13030 utterances, totalling 49 hours and 09 minutes.\n\n* The corpus is not split into train/dev/test portions.\n\n* The corpus is distrubuted in the following format: flac, 16kHz@16bits mono.\n\n* The column \"mafia_score\" in the metadata file indicates the expected precision of the transcription. Zero is the highest precision.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe Raddrómur Corpus is composed of different radio podcasts in Icelandic. More information about the origin of these podcasts comes as follows:\n\n* Rokkland | Author: Ólafur Páll Gunnarsson | Podcast/Radio show hosted by RUV.\n\n* A Tonsvidinu | Author: Una Margrét Jónsdóttir | Podcast/Radio show hosted by RUV.\n\n* I ljosu Sogunnar | Author: Vera Illugadóttir | Podcast/Radio show hosted by RUV.\n\n* Nedanmals | Authors: Elísabet Rún Þorsteinsdóttir and Marta Eir Sigurðardóttir. | Elísabet Rún Þorsteinsdóttir og Marta Eir Sigurðardóttir.\n\n* Leikfangavelin | Author: Atla Hergeirssonar | Independent Podcast/Radio show.", "### Annotations", "#### Annotation process\n\nThe podcasts from URL were selected because they count with a text script that matches with certain \nfidelity what is said during the show. After automatic segmentation of the episodes, the transcriptions were inferred using the scripts along with a forced alignment technique.", "#### Who are the annotators?\n\nThe corpus was automatically segmented and aligned by the MAFIA aligner.\n\nThe MAFIA aligner is designed to take a podcast episode along with a text script reflecting what is spoken in the podcast, then segment the podcast and find a transcription that better fits what is in the script. When the script is not accurate, MAFIA is able to infer a transcription using Automatic Speech Recognition.", "### Personal and Sensitive Information\nThe corpus is comprised of speech utterances from professional podcasters. Nevertheless, you agree to not attempt to determine the identity of speakers in this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\nThis ASR corpus is one of the few available Icelandic copora with spontaneous speech.", "### Discussion of Biases\n\nIn order to distinguish the transcriptions with fewer expected mistakes, a quality measure called \"MAFIA Score\" was added in the metadata file included with the corpus. A MAFIA Score close to zero implies a better quality transcription.\n\nTo infer a transcription using the vocabulary of the text script, MAFIA creates a 3-gram language model with SRILM [4] using the text of all the podcasts available at the moment of running it. After this, MAFIA transcribes all the segments using a speech recognizer based on NVIDIA-NeMo.\n\nIn order to calculate the MAFIA Score, a second round of speech recognition is passed to all the segments but using a way more robust 6-gram language model with a size of 5GB. The MAFIA score is then obtained by measuring the Word Error Rate bewteen the first pass transcriptions (reference) and the second pass transcriptions (hyphotesis). According to this, a MAFIA score of 0 reflects a transcription that is equal in both passes and therefore, it is a high quality transcription.", "### Other Known Limitations\n\"Raddrómur Icelandic Speech 22.09\" by the Language and Voice Laboratory (LVL) from Reykjavík University (RU) is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.", "## Additional Information", "### Dataset Curators\n\nThe corpus was curated by Carlos Daniel Hernández Mena in 2022.", "### Licensing Information\nCC-BY-4.0", "### Contributions\n\nThis project was funded by the Language Technology Programme for Icelandic 2019-2022. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.\n\nSpecial thanks to the podcasters and to Aron Berg from RÚV." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Icelandic #license-cc-by-4.0 #icelandic podcasts #spontaneous icelandic #forced-aligned #ruv.is #mafia aligner #region-us \n", "# Dataset Card for raddromur_asr", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: Raddrómur Icelandic Speech 22.09\n- Repository: URL\n- Point of Contact: Carlos Mena, Jón Guðnason", "### Dataset Summary\n\nThe \"Raddrómur Icelandic Speech 22.09\" (\"Raddrómur Corpus\" for short) is an Icelandic corpus created by the Language and Voice Laboratory (LVL) at Reykjavík University (RU) in 2022. It is made out of radio podcasts mostly taken from RÚV (URL).", "### Example Usage\nThe Raddrómur Corpus counts with the train split only. To load the training split pass its name as a config name:\n\nTo load the specific \"train\" split do:", "### Supported Tasks\nautomatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).", "### Languages\nThe audio is in Icelandic.", "## Dataset Structure", "### Data Instances", "### Data Fields\n* 'audio_id' (string) - id of audio segment\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'podcast_id' (string) - id of the podcast\n* 'segment_num' (int32) - integer identifing the number of segment\n* 'duration' (float32) - duration of the audio file in seconds.\n* 'mafia_score' (float32) - In order to distinguish the transcriptions with fewer expected mistakes, a quality measure called \"MAFIA Score\" was added. A MAFIA Score close to zero implies a better quality transcription.\n* 'normalized_text' (string) - normalized audio segment transcription.", "### Data Splits\nThe corpus is split into train only. The lenght of the train portion is 49h09m in 13030 utterances.", "## Dataset Creation", "### Curation Rationale\n\n* The corpus was automatically segmented using the tool inaSpeechSegmenter.\n \n* The forced alignment was performed using the tool MAFIA aligner.\n \n* The corpus comes with a metadata file wich is in TSV format. This file contain the normalized transcription of the corpus and the filenames among other relevant information.\n \n* The corpus contains 13030 utterances, totalling 49 hours and 09 minutes.\n\n* The corpus is not split into train/dev/test portions.\n\n* The corpus is distrubuted in the following format: flac, 16kHz@16bits mono.\n\n* The column \"mafia_score\" in the metadata file indicates the expected precision of the transcription. Zero is the highest precision.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe Raddrómur Corpus is composed of different radio podcasts in Icelandic. More information about the origin of these podcasts comes as follows:\n\n* Rokkland | Author: Ólafur Páll Gunnarsson | Podcast/Radio show hosted by RUV.\n\n* A Tonsvidinu | Author: Una Margrét Jónsdóttir | Podcast/Radio show hosted by RUV.\n\n* I ljosu Sogunnar | Author: Vera Illugadóttir | Podcast/Radio show hosted by RUV.\n\n* Nedanmals | Authors: Elísabet Rún Þorsteinsdóttir and Marta Eir Sigurðardóttir. | Elísabet Rún Þorsteinsdóttir og Marta Eir Sigurðardóttir.\n\n* Leikfangavelin | Author: Atla Hergeirssonar | Independent Podcast/Radio show.", "### Annotations", "#### Annotation process\n\nThe podcasts from URL were selected because they count with a text script that matches with certain \nfidelity what is said during the show. After automatic segmentation of the episodes, the transcriptions were inferred using the scripts along with a forced alignment technique.", "#### Who are the annotators?\n\nThe corpus was automatically segmented and aligned by the MAFIA aligner.\n\nThe MAFIA aligner is designed to take a podcast episode along with a text script reflecting what is spoken in the podcast, then segment the podcast and find a transcription that better fits what is in the script. When the script is not accurate, MAFIA is able to infer a transcription using Automatic Speech Recognition.", "### Personal and Sensitive Information\nThe corpus is comprised of speech utterances from professional podcasters. Nevertheless, you agree to not attempt to determine the identity of speakers in this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\nThis ASR corpus is one of the few available Icelandic copora with spontaneous speech.", "### Discussion of Biases\n\nIn order to distinguish the transcriptions with fewer expected mistakes, a quality measure called \"MAFIA Score\" was added in the metadata file included with the corpus. A MAFIA Score close to zero implies a better quality transcription.\n\nTo infer a transcription using the vocabulary of the text script, MAFIA creates a 3-gram language model with SRILM [4] using the text of all the podcasts available at the moment of running it. After this, MAFIA transcribes all the segments using a speech recognizer based on NVIDIA-NeMo.\n\nIn order to calculate the MAFIA Score, a second round of speech recognition is passed to all the segments but using a way more robust 6-gram language model with a size of 5GB. The MAFIA score is then obtained by measuring the Word Error Rate bewteen the first pass transcriptions (reference) and the second pass transcriptions (hyphotesis). According to this, a MAFIA score of 0 reflects a transcription that is equal in both passes and therefore, it is a high quality transcription.", "### Other Known Limitations\n\"Raddrómur Icelandic Speech 22.09\" by the Language and Voice Laboratory (LVL) from Reykjavík University (RU) is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.", "## Additional Information", "### Dataset Curators\n\nThe corpus was curated by Carlos Daniel Hernández Mena in 2022.", "### Licensing Information\nCC-BY-4.0", "### Contributions\n\nThis project was funded by the Language Technology Programme for Icelandic 2019-2022. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.\n\nSpecial thanks to the podcasters and to Aron Berg from RÚV." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/feed\n* Config: top_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/feed\n* Config: top_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/feed\n* Config: sen_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/feed\n* Config: sen_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/feed\n* Config: top_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/feed\n* Config: top_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-30b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-30b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/feed\n* Config: top_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/feed\n* Config: top_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/feed\n* Config: sen_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/feed\n* Config: sen_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/feed\n* Config: top_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: futin/feed\n* Config: top_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-66b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-66b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/feed\n* Config: sen_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-3b\n* Dataset: futin/feed\n* Config: sen_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/feed\n* Config: sen_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/feed\n* Config: sen_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-13b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-13b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-6.7b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-6.7b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/feed\n* Config: sen_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/feed\n* Config: sen_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/feed\n* Config: top_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/feed\n* Config: top_vi_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-350m\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-350m\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-2.7b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-2.7b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-125m\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-125m\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-1.3b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-1.3b\n* Dataset: futin/feed\n* Config: sen_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-66b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-66b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-30b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-30b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-13b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-13b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-6.7b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-6.7b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-350m\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-350m\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-125m\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-125m\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-2.7b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-2.7b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-1.3b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-1.3b\n* Dataset: futin/feed\n* Config: top_en_\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"librispeech-augmentated-train-prepared\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"librispeech-augmentated-train-prepared\"\n\nMore Information needed" ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: Artifact-AI/led_base_16384_billsum_summarization\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Artifact-AI for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: Artifact-AI/led_base_16384_billsum_summarization\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Artifact-AI for evaluating this model." ]

[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]