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README.md
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@@ -22,9 +22,11 @@ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentence
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This model is a fine-tune of [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) using the HCA case law in the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus) by Umar Butler. The PDF/OCR cases were not used.
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The cases were split into < 512 context chunks using the bge-small-en tokeniser and [semchunk](https://github.com/umarbutler/semchunk).
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[mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) was used to generate a legal question for each context chunk.
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129,137 context-question pairs were used for training.
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14,348 context-question pairs were used for evaluation (see the table below for results).
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Using a 10% subset of the val dataset the following hit-rate performance was reached and is compared to the base model and OpenAI's default ada embedding model.
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This model is a fine-tune of [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) using the HCA case law in the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus) by Umar Butler. The PDF/OCR cases were not used.
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The cases were split into < 512 context chunks using the bge-small-en tokeniser and [semchunk](https://github.com/umarbutler/semchunk).
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[mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) was used to generate a legal question for each context chunk.
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129,137 context-question pairs were used for training.
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14,348 context-question pairs were used for evaluation (see the table below for results).
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Using a 10% subset of the val dataset the following hit-rate performance was reached and is compared to the base model and OpenAI's default ada embedding model.
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