Instructions to use JohnLei/5-shot-qaner-s2-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JohnLei/5-shot-qaner-s2-v2 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="JohnLei/5-shot-qaner-s2-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("JohnLei/5-shot-qaner-s2-v2") model = AutoModelForQuestionAnswering.from_pretrained("JohnLei/5-shot-qaner-s2-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JohnLei/5-shot-qaner-s2-v2: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/JohnLei/5-shot-qaner-s2-v2/resolve/dfac2af3d22d2fe8d2083cbd8a9e00d49b971c93/training_args.bin
- Command line
-
hf download hf://JohnLei/5-shot-qaner-s2-v2@dfac2af3d22d2fe8d2083cbd8a9e00d49b971c93/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JohnLei/5-shot-qaner-s2-v2/resolve/dfac2af3d22d2fe8d2083cbd8a9e00d49b971c93/training_args.bin
5.3 kB
- Xet hash:
- 45db797ebe44233985877b15960eb48641d5ca2e63b2b74331bb929a76abb7e8
- Size of remote file:
- 5.3 kB
- SHA256:
- cb11d2a8ad42e954f32bf2b23a2739140534bd4b34b158c9bf85353305fa02dd
路
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