Instructions to use sunitha/AQG_CV_Squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sunitha/AQG_CV_Squad 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="sunitha/AQG_CV_Squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sunitha/AQG_CV_Squad") model = AutoModelForQuestionAnswering.from_pretrained("sunitha/AQG_CV_Squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from sunitha/AQG_CV_Squad: direct link, hf CLI and curl.
- Browser
- Download file 531 MB
-
https://huggingface.co/sunitha/AQG_CV_Squad/resolve/main/optimizer.pt
- Command line
-
hf download hf://sunitha/AQG_CV_Squad/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/sunitha/AQG_CV_Squad/resolve/main/optimizer.pt
531 MB
- Xet hash:
- 39adf1869e5c5e4138181c7aac9f46adf46229c416affde7d5abc429ab97ea82
- Size of remote file:
- 531 MB
- SHA256:
- 00e0ff7ed1edeb9f002405baa79a5bf53cf9a14c2a0ae9ddf4dadb4dcce828f7
路
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