Instructions to use hung200504/bert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hung200504/bert-large 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-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hung200504/bert-large") model = AutoModelForQuestionAnswering.from_pretrained("hung200504/bert-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hung200504/bert-large: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/hung200504/bert-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hung200504/bert-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hung200504/bert-large/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- f9102ee6750595171c0aeea1b0e2c1a4aab01ef28154e41dd8e9a6416ebe2f21
- Size of remote file:
- 1.34 GB
- SHA256:
- 993952f558070effd071e167702edc38c951a16259041a3868fb7b4da1282269
路
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