Instructions to use Contents/bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Contents/bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Contents/bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Contents/bert-base-uncased") model = AutoModel.from_pretrained("Contents/bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Contents/bert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Contents/bert-base-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Contents/bert-base-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Contents/bert-base-uncased/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 3654afebc8102b591b856cf9aa18cd3f8fbbacafa3e256f62ca12b88914f2be8
- Size of remote file:
- 438 MB
- SHA256:
- ff8e66bc015553d84e94959a0b84aa7386b59a83ae74b49189f735b107b01afc
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