Instructions to use AndyChiang/Pre-CoFactv3-Question-Answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndyChiang/Pre-CoFactv3-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="AndyChiang/Pre-CoFactv3-Question-Answering")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("AndyChiang/Pre-CoFactv3-Question-Answering") model = AutoModelForQuestionAnswering.from_pretrained("AndyChiang/Pre-CoFactv3-Question-Answering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AndyChiang/Pre-CoFactv3-Question-Answering: direct link, hf CLI and curl.
- Browser
- Download file 1.74 GB
-
https://huggingface.co/AndyChiang/Pre-CoFactv3-Question-Answering/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AndyChiang/Pre-CoFactv3-Question-Answering/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/AndyChiang/Pre-CoFactv3-Question-Answering/resolve/main/pytorch_model.bin
1.74 GB
- Xet hash:
- d3cb635cbf4d442f6312eae3eb19f2123d767d1ef963e8af8f3cf97d173a858f
- Size of remote file:
- 1.74 GB
- SHA256:
- af9234c383bf2ec65ccc897e2f94897764d8f5129a2edad105817a9ba03779ec
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