Instructions to use hung200504/bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hung200504/bert-base-uncased 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-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hung200504/bert-base-uncased") model = AutoModelForQuestionAnswering.from_pretrained("hung200504/bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hung200504/bert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/hung200504/bert-base-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hung200504/bert-base-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hung200504/bert-base-uncased/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- cfe494c1b48c128859c97fb2d317e32e6dea21d0d86adbdc7aa005266a4cae38
- Size of remote file:
- 436 MB
- SHA256:
- 40ee78709af067b2818403cc8c60b2d99be39d367ce56313acd7e273ccb38aac
路
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