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--- |
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base_model: cmarkea/bloomz-3b-dpo-chat |
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library_name: peft |
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license: apache-2.0 |
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datasets: |
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- cmarkea/table-vqa |
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language: |
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- fr |
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- en |
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pipeline_tag: text-generation |
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--- |
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## Model Description |
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**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. |
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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. |
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### Key Features |
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- **Domain:** Table-based question answering (QA), particularly for extracting information from LaTeX-format tables. |
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- **Language Support:** French and English, making it suitable for multilingual environments. |
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- **Model Type:** Text-to-text language model. |
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- **Precision:** bfloat16, optimizing computational efficiency. |
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- **Training Duration:** 2 days on A100 40GB GPU. |
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- **Fine-Tuning Method:** Full fine-tuning. |
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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. |
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## Usage |
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Here’s an example of how to use this model for table-based question answering: |
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```python |
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import torch |
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from transformers import pipeline |
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device = 0 if torch.cuda.is_available() else -1 |
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table = '''\begin{tabular}{|c|c|c|} |
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\hline |
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Model & MAE-{$TKE$}-low & MAE-{$TKE$}-high\\ |
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\hline |
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U-FNET & 0.0048 & $1.09 \times 10^{-5}$\\ |
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\hline |
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\end{tabular}''' |
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question = "What is the MAE-TKE-high value for the U-FNET model?" |
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prompt = table + '\n' + question |
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model = pipeline("text-generation", "cmarkea/bloomz-3b-dpo-chat", device=device) |
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result = model(f"</s>{prompt}<s>", max_new_tokens=512) |
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print(result) |
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``` |
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The model processes tables written in LaTeX format, so be sure to provide your tables in that form. |
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## Performance |
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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. |
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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. |
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Here’s a visualization of the results: |
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## Citation |
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```bibtex |
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@online{AgDePaligemmaTableQALatex, |
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AUTHOR = {Tom Agonnoude, Cyrile Delestre}, |
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URL = {https://huggingface.co./cmarkea/bloomz-3b-dpo-table-qa-latex}, |
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YEAR = {2024}, |
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KEYWORDS = {Table understanding, LaTeX, Multilingual, QA}, |
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} |
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``` |
