Instructions to use hung200504/bert-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hung200504/bert-10 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-10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hung200504/bert-10") model = AutoModelForQuestionAnswering.from_pretrained("hung200504/bert-10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hung200504/bert-10: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/hung200504/bert-10/resolve/main/training_args.bin
- Command line
-
hf download hf://hung200504/bert-10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hung200504/bert-10/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 5973b8032a91406575b51aeb49b9e054a9e3871123f56d126018ea46d3bc6f72
- Size of remote file:
- 4.03 kB
- SHA256:
- 56f0f70ea4084d32970f362255f80d2da96d6d332d36941bad8b4e9e02612f09
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