Instructions to use sv-adv/qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sv-adv/qa_model 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="sv-adv/qa_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sv-adv/qa_model") model = AutoModelForQuestionAnswering.from_pretrained("sv-adv/qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sv-adv/qa_model: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/sv-adv/qa_model/resolve/main/training_args.bin
- Command line
-
hf download hf://sv-adv/qa_model/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/sv-adv/qa_model/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- ec65a84a45d824037c36eea1f888832b9ad7098bb1556bba6435f165b6122bcd
- Size of remote file:
- 5.11 kB
- SHA256:
- 453ae941c6dfbd05e3b83b3d0c25ad4970c10a6e79ca66b1b92453d01024e3f5
路
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