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@@ -19,10 +19,10 @@ library_name: sentence-transformers
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  # QulBERT
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  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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-
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  This model originates from the [Camel-Bert_Classical Arabic](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca) model. It was then trained on the Jawami' Kalim dataset,
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  specifically a dataset of 440,000 matns and their corresponding taraf labels.
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- Taraf labels indicate two hadith are about the same report, and as such, are more semantically similar.
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  ## Usage (Sentence-Transformers)
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  ## Evaluation Results
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- The dataset was split into 75% training, 15% eval, 10% test.
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@@ -154,7 +154,7 @@ Triplet Evaluation:
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  | 6 | 20000 | 0.9673 | 0.967 | 0.9665 |
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  | 6 | -1 | 0.9666 | 0.9658 | 0.9666 |
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  ## Training
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  The model was trained with the parameters:
 
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  # QulBERT
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  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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+ <!--
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  This model originates from the [Camel-Bert_Classical Arabic](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca) model. It was then trained on the Jawami' Kalim dataset,
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  specifically a dataset of 440,000 matns and their corresponding taraf labels.
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+ Taraf labels indicate two hadith are about the same report, and as such, are more semantically similar. -->
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  ## Usage (Sentence-Transformers)
 
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  ## Evaluation Results
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+ <!-- The dataset was split into 75% training, 15% eval, 10% test.
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  | 6 | 20000 | 0.9673 | 0.967 | 0.9665 |
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  | 6 | -1 | 0.9666 | 0.9658 | 0.9666 |
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+ -->
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  ## Training
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  The model was trained with the parameters: