Create README.md

README.md

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+---
+base_model: cmarkea/bloomz-3b-dpo-chat  
+library_name: peft  
+license: apache-2.0  
+datasets:  
+- cmarkea/table-vqa  
+language:  
+- fr  
+- en  
+pipeline_tag: Table Question Answering  
+---
+
+## Model Description
+
+**cmarkea/bloomz-3b-dpo-table-qa-latex** is a fine-tuned version of the **[cmarkea/bloomz-3b-dpo-chat](https://huggingface.co/cmarkea/bloomz-3b-dpo-chat)** model, specialized for table-based question answering (QA) tasks. The model has been trained on the **[table-vqa](https://huggingface.co/datasets/cmarkea/table-vqa)** dataset, which was developed by Crédit Mutuel Arkéa, and it processes tables provided in their LaTeX source format. 
+
+This model is optimized for multilingual environments, supporting both French and English, and is especially effective in extracting and interpreting tabular data from documents. It has been fine-tuned for 2 days on an A100 40GB GPU and operates in bfloat16 precision to maximize resource efficiency.
+
+### Key Features
+- **Domain:** Table-based question answering (QA), particularly for extracting information from LaTeX-format tables.
+- **Language Support:** French and English, making it suitable for multilingual environments.
+- **Model Type:** Text-to-text language model.
+- **Precision:** bfloat16, optimizing computational efficiency.
+- **Training Duration:** 2 days on A100 40GB GPU.
+- **Fine-Tuning Method:** Full fine-tuning.
+
+This model is highly applicable in fields where tabular data needs to be queried and analyzed, such as financial reports, academic papers, and technical documentation.
+
+## Usage
+
+Here’s an example of how to use this model for table-based question answering:
+
+```python
+import torch
+from transformers import pipeline
+
+device = 0 if torch.cuda.is_available() else -1
+table = '''\begin{tabular}{|c|c|c|}
+        \hline
+         Model & MAE-{$TKE$}-low &  MAE-{$TKE$}-high\\
+         \hline
+         U-FNET & 0.0048 & $1.09 \times 10^{-5}$\\
+         \hline
+    \end{tabular}'''
+question = "What is the MAE-TKE-high value for the U-FNET model?"
+prompt = table + '\n' + question
+
+model = pipeline("text-generation", "cmarkea/bloomz-3b-dpo-chat", device=device)
+result = model(f"</s>{prompt}<s>", max_new_tokens=512)
+print(result)
+```
+
+The model processes tables written in LaTeX format, so be sure to provide your tables in that form.
+
+## Performance
+
+This model was evaluated on 200 question-answer pairs extracted from 100 tables in the **[table-vqa](https://huggingface.co/datasets/cmarkea/table-vqa)** test set. Each table had two question-answer pairs: one in French and one in English.
+
+The evaluation used the **[LLM-as-Juries](https://arxiv.org/abs/2404.18796)** method, employing three judge models (GPT-4o, Gemini1.5 Pro, and Claude 3.5-Sonnet). The scoring was adapted to the table QA context, with a scale from 0 to 5 to ensure precision in assessing the model’s performance.
+
+Here’s a visualization of the results:
+
+![constellation](https://i.postimg.cc/t4tjhy6b/constellation-0.png)
+
+
+## Citation
+
+```bibtex
+@online{AgDePaligemmaTableQALatex,
+  AUTHOR = {Tom Agonnoude, Cyrile Delestre},
+  URL = {https://huggingface.co/cmarkea/bloomz-3b-dpo-table-qa-latex},
+  YEAR = {2024},
+  KEYWORDS = {Table understanding, LaTeX, Multilingual, QA},
+}
+```