Instructions to use CATIE-AQ/QAmemberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CATIE-AQ/QAmemberta 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/QAmemberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("CATIE-AQ/QAmemberta") model = AutoModelForQuestionAnswering.from_pretrained("CATIE-AQ/QAmemberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# QAmemBERTa
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## Model Description
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Our methodology is described in a blog post available in [English](https://blog.vaniila.ai/en/QA_en/) or [French](https://blog.vaniila.ai/QA/).
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# QAmemBERTa
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## Model Description
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We present **QAmemBERT2**, which is a [CamemBERTa v2 base](https://huggingface.co/almanach/camembertav2-base) fine-tuned for the Question-Answering task for the French language on four French Q&A datasets composed of contexts and questions with their answers inside the context (= SQuAD 1.0 format) but also contexts and questions with their answers not inside the context (= SQuAD 2.0 format).
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All these datasets were concatenated into a single dataset that we called [frenchQA](https://huggingface.co/datasets/CATIE-AQ/frenchQA).
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This represents a total of over **221,348 context/question/answer triplets used to finetune this model and 6,376 to test it**.
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Our methodology is described in a blog post available in [English](https://blog.vaniila.ai/en/QA_en/) or [French](https://blog.vaniila.ai/QA/).
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