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
Download pytorch_model.bin from autoevaluate/extractive-question-answering: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/autoevaluate/extractive-question-answering/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://autoevaluate/extractive-question-answering/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/autoevaluate/extractive-question-answering/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 24eb2c392fae478a5f097770ce220cbd9ec9a408fa866c706f007e7a45d9add8
- Size of remote file:
- 265 MB
- SHA256:
- e5b27e4e499733ece425ccf78bd2ba3f8e3ea26f450c28b62c745f1c0991b7ac
路
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