Instructions to use autoevaluate/extractive-question-answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/extractive-question-answering 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="autoevaluate/extractive-question-answering")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("autoevaluate/extractive-question-answering") model = AutoModelForQuestionAnswering.from_pretrained("autoevaluate/extractive-question-answering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on squad
Beep boop, I am a bot from Hugging Face's automatic evaluation service! Your model has been evaluated on the [squad](https://huggingface.co/datasets/squad) dataset. Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=squad). Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/autoevaluate?dataset=squad).4adf127 -
Update README.md (#1)
- Update README.md (9904a7abd7cf0dafbf488752822c0c0420317aba)59ea102 -
update model card README.md
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Model save
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Training in progress, step 500
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initial commit
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