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--- |
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datasets: |
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- airesearch/WangchanX-Legal-ThaiCCL-RAG |
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language: |
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- th |
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pipeline_tag: sentence-similarity |
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tags: |
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- legal |
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- RAG |
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widget: [] |
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license: mit |
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base_model: |
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- BAAI/bge-m3 |
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--- |
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## WangchanX-Legal-ThaiCCL-Retriever |
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This model card describes WangchanX-Legal-ThaiCCL-Retriever, a retriever model fine-tuned from the bge-m3 model on the WangchanX-Legal-ThaiCCL-RAG dataset. It is designed to retrieve relevant legal text sections in response to legal questions posed in Thai, specifically focusing on Corporate and Commercial Law (CCL). |
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**Model Details:** |
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* **Base Model:** [bge-m3](https://huggingface.co/BAAI/bge-m3) |
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* **Fine-tuned Dataset:** [WangchanX-Legal-ThaiCCL-RAG dataset](https://huggingface.co/datasets/airesearch/WangchanX-Legal-ThaiCCL-RAG) |
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* **Language:** Thai |
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* **Maximum Sequence Length:** 8192 tokens |
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* **Output Dimensionality:** 1024 tokens |
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* **License:** MIT |
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**WangchanX-Legal-ThaiCCL-RAG** |
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This dataset focuses on supporting Thai legal question-answering systems using Retrieval-Augmented Generation (RAG), focusing on Corporate and Commercial Law. |
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**Intended Use Cases:** |
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This model is designed for use as a retriever model within a larger RAG pipeline. |
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* **Legal Question Answering:** Serving as a core component in a larger question-answering system that provides answers to user queries about Thai law. |
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* **Legal Information Retrieval:** Enabling efficient retrieval of information from Thai legal texts. |
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<!-- ## Usage |
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This model is designed for use as a retriever model within a larger RAG pipeline. Given a legal question in Thai, it will retrieve the most relevant sections from the Thai CCL corpus. You can integrate this model into your application using the Hugging Face Transformers library. |
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### Direct Usage (Transformers) |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Citation |
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### BibTeX |
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