Instructions to use hung200504/bert-12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hung200504/bert-12 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-12")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hung200504/bert-12") model = AutoModelForQuestionAnswering.from_pretrained("hung200504/bert-12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hung200504/bert-12: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/hung200504/bert-12/resolve/main/training_args.bin
- Command line
-
hf download hf://hung200504/bert-12/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hung200504/bert-12/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 465bc1d47cf32f59fd5b6aba3d5107d52ca7d89b5c02d0b68a3b9abd69b73979
- Size of remote file:
- 4.03 kB
- SHA256:
- 10279b95126dcb42fe65fe56b911e40a04cdc950dd3715c31965395c606927bf
路
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