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 training_args.bin from hung200504/bert-large: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/hung200504/bert-large/resolve/main/training_args.bin
- Command line
-
hf download hf://hung200504/bert-large/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hung200504/bert-large/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- b1eb563da90be087584b6f3b4d601a84cde9522f69b26865e19317b1cc964fc8
- Size of remote file:
- 4.09 kB
- SHA256:
- ca79f77d0143df64c6ba97d68127dec1cb05125e45c34404abd8b37ec0293a3f
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.