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Dataset changes (#6)

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- Changed dataset name, added sentence transformer references back in (094fcb692a20853780c0fd4115e044b24239e2fe)

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  1. README.md +17 -14
README.md CHANGED
@@ -39,9 +39,9 @@ model-index:
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  name: Cosine Ap
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  ---
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- # SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
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- This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the Quora csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity for the purpose of semantic caching.
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  ## Model Details
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@@ -52,7 +52,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [A
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  - **Output Dimensionality:** 768 dimensions
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  - **Similarity Function:** Cosine Similarity
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  - **Training Dataset:**
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- - Quora csv
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  <!-- - **Language:** Unknown -->
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  <!-- - **License:** Unknown -->
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@@ -115,17 +115,17 @@ print(similarities.shape)
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  ### Training Dataset
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- #### csv
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- * Dataset: csv
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  * Size: training samples
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  * Columns: <code>question_1</code>, <code>question_2</code>, and <code>label</code>
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  ### Evaluation Dataset
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- #### csv
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- * Dataset: csv
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  * Size: evaluation samples
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  * Columns: <code>question_1</code>, <code>question_2</code>, and <code>label</code>
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@@ -133,15 +133,18 @@ print(similarities.shape)
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  ### BibTeX
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  #### Sentence Transformers
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  ```bibtex
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- @inproceedings{redisetal.,
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- title = "",
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- author = "",
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- month = "",
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- year = "",
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- publisher = "",
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- url = "",
 
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  }
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  ```
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  name: Cosine Ap
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  ---
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+ # Redis Semantic Caching embedding model based on Alibaba-NLP/gte-modernbert-base
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [Quora](https://www.kaggle.com/datasets/quora/question-pairs-dataset) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity for the purpose of semantic caching.
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  ## Model Details
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  - **Output Dimensionality:** 768 dimensions
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  - **Similarity Function:** Cosine Similarity
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  - **Training Dataset:**
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+ - [Quora](https://www.kaggle.com/datasets/quora/question-pairs-dataset)
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  <!-- - **Language:** Unknown -->
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  <!-- - **License:** Unknown -->
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  ### Training Dataset
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+ #### Quora
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+ * Dataset: [Quora](https://www.kaggle.com/datasets/quora/question-pairs-dataset)
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  * Size: training samples
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  * Columns: <code>question_1</code>, <code>question_2</code>, and <code>label</code>
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  ### Evaluation Dataset
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+ #### Quora
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+ * Dataset: [Quora](https://www.kaggle.com/datasets/quora/question-pairs-dataset)
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  * Size: evaluation samples
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  * Columns: <code>question_1</code>, <code>question_2</code>, and <code>label</code>
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  ### BibTeX
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+ #### Redis Langcache-embed Models
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+
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  #### Sentence Transformers
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  ```bibtex
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+ @inproceedings{reimers-2019-sentence-bert,
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+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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+ author = "Reimers, Nils and Gurevych, Iryna",
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+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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+ month = "11",
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+ year = "2019",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://arxiv.org/abs/1908.10084",
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  }
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  ```
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