Instructions to use hung200504/bert-9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hung200504/bert-9 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="hung200504/bert-9")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hung200504/bert-9") model = AutoModelForQuestionAnswering.from_pretrained("hung200504/bert-9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hung200504/bert-9: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/hung200504/bert-9/resolve/main/training_args.bin
- Command line
-
hf download hf://hung200504/bert-9/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hung200504/bert-9/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- b9e9f57cbbe330973fdd6638e1d38e98d197dd9d14b9593da45f672dc5336d2d
- Size of remote file:
- 4.09 kB
- SHA256:
- 7421849fdf82028adbbcbd32ebf0cb31067b9f39d86deda4c91fd6e2564e5898
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