Instructions to use hung200504/bert-13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hung200504/bert-13 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-13")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hung200504/bert-13") model = AutoModelForQuestionAnswering.from_pretrained("hung200504/bert-13", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hung200504/bert-13: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/hung200504/bert-13/resolve/main/training_args.bin
- Command line
-
hf download hf://hung200504/bert-13/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hung200504/bert-13/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- dfc8faea2d5949ec34aef7070ba0be8c89b317f9560b2d938a82bd1c8bda4a6d
- Size of remote file:
- 4.03 kB
- SHA256:
- a5fa438248635011757ae6757bfd37e0fe271b0e7dc33cbeba3eb4cdd1fa953a
路
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