Instructions to use CATIE-AQ/QAmembert2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CATIE-AQ/QAmembert2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="CATIE-AQ/QAmembert2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("CATIE-AQ/QAmembert2") model = AutoModelForQuestionAnswering.from_pretrained("CATIE-AQ/QAmembert2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Results (french QA test split)
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| Model | Parameters | Context | Exact_match | F1-score | Answer_f1 | NoAnswer_f1 |
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| [etalab/camembert-base-squadFR-fquad-piaf](https://huggingface.co/AgentPublic/camembert-base-squadFR-fquad-piaf) | 110M | 512 tokens | 39.30 | 51.55 | 79.54 | 23.58
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| [QAmembert](https://huggingface.co/CATIE-AQ/QAmembert)| 110M | 512 tokens | 77.14 | 86.88 | 75.66 | 98.11
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| [QAmembert2](https://huggingface.co/CATIE-AQ/QAmembert2) (this version) | 112M | 1024 tokens | 76.47 | 88.25 | 78.66 | 97.84
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## Results (french QA test split)
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| Model | Parameters | Context | Exact_match | F1-score | Answer_f1 | NoAnswer_f1 |
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| [etalab/camembert-base-squadFR-fquad-piaf](https://huggingface.co/AgentPublic/camembert-base-squadFR-fquad-piaf) | 110M | 512 tokens | 39.30 | 51.55 | 79.54 | 23.58
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| [QAmembert](https://huggingface.co/CATIE-AQ/QAmembert)| 110M | 512 tokens | 77.14 | 86.88 | 75.66 | 98.11
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| [QAmembert2](https://huggingface.co/CATIE-AQ/QAmembert2) (this version) | 112M | 1024 tokens | 76.47 | 88.25 | 78.66 | 97.84
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