Instructions to use Hansollll/Question_Answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hansollll/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="Hansollll/Question_Answering")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Hansollll/Question_Answering") model = AutoModelForQuestionAnswering.from_pretrained("Hansollll/Question_Answering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Hansollll/Question_Answering: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/Hansollll/Question_Answering/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Hansollll/Question_Answering/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Hansollll/Question_Answering/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 5a281173c7bf184b85b4cb7ced6666a01ca51678e87f2b8823a27a6f6d7c008d
- Size of remote file:
- 265 MB
- SHA256:
- 4942116b8910460d14a5646c643bab1d9d705f4383e93097c1caa19f69587833
路
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