Update README.md

README.md

@@ -1,79 +1,44 @@
 ---
-datasets: []
-language: []
-library_name: sentence-transformers
+datasets:
+- airesearch/WangchanX-Legal-ThaiCCL-RAG
+language:
+- th
 pipeline_tag: sentence-similarity
 tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
+- legal
+- RAG
 widget: []
+license: mit
+base_model:
+- BAAI/bge-m3
 ---
 
-# SentenceTransformer
+## WangchanX-Legal-ThaiCCL-Retriever: A Thai Legal Text Retriever 
 
-This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+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).
 
-## Model Details
+**Model Details:**
 
-### Model Description
-- **Model Type:** Sentence Transformer
-<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
-- **Maximum Sequence Length:** 8192 tokens
-- **Output Dimensionality:** 1024 tokens
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+* **Base Model:** [bge-m3](https://huggingface.co/BAAI/bge-m3)
+* **Fine-tuned Dataset:** [WangchanX-Legal-ThaiCCL-RAG dataset](https://huggingface.co/datasets/airesearch/WangchanX-Legal-ThaiCCL-RAG)
+* **Language:** Thai
+* **Maximum Sequence Length:** 8192 tokens
+* **Output Dimensionality:** 1024 tokens
+* **License:** MIT
 
-### Model Sources
+**WangchanX-Legal-ThaiCCL-RAG**
 
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+This dataset focuses on supporting Thai legal question-answering systems using Retrieval-Augmented Generation (RAG), focusing on Corporate and Commercial Law. 
 
-### Full Model Architecture
+**Intended Use Cases:**
+This model is designed for use as a retriever model within a larger RAG pipeline.
+* **Legal Question Answering:** Serving as a core component in a larger question-answering system that provides answers to user queries about Thai law.
+* **Legal Information Retrieval:** Enabling efficient retrieval of information from Thai legal texts.
 
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
-  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-  (2): Normalize()
-)
-```
-
-## Usage
-
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
-
-```bash
-pip install -U sentence-transformers
-```
-
-Then you can load this model and run inference.
-```python
-from sentence_transformers import SentenceTransformer
-
-# Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 1024]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
-```
+<!-- ## Usage
 
+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.
+ -->
 <!--
 ### Direct Usage (Transformers)
 
@@ -109,36 +74,8 @@ You can finetune this model on your own dataset.
 
 *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
 -->
-
-## Training Details
-
-### Framework Versions
-- Python: 3.10.14
-- Sentence Transformers: 3.0.1
-- Transformers: 4.34.0
-- PyTorch: 2.1.0+cu121
-- Accelerate: 0.21.0
-- Datasets: 2.21.0
-- Tokenizers: 0.14.1
-
+<!-- 
 ## Citation
 
 ### BibTeX
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+ -->