Instructions to use hung200504/bert-7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hung200504/bert-7 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-7")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hung200504/bert-7") model = AutoModelForQuestionAnswering.from_pretrained("hung200504/bert-7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hung200504/bert-7: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/hung200504/bert-7/resolve/main/training_args.bin
- Command line
-
hf download hf://hung200504/bert-7/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hung200504/bert-7/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 022d429b8039b9ee851c13996cb3242ef195a98461e722a2aac3eb8e14113208
- Size of remote file:
- 4.09 kB
- SHA256:
- 0699f73b55dda927f535c64d4554ab90f8dc45043865c98de25e9dbec77c3916
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.