Instructions to use google-bert/bert-large-uncased-whole-word-masking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-large-uncased-whole-word-masking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-large-uncased-whole-word-masking")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-large-uncased-whole-word-masking") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-large-uncased-whole-word-masking", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google-bert/bert-large-uncased-whole-word-masking: direct link, hf CLI and curl.
- Browser
- Download file 1.35 GB
-
https://huggingface.co/google-bert/bert-large-uncased-whole-word-masking/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google-bert/bert-large-uncased-whole-word-masking/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google-bert/bert-large-uncased-whole-word-masking/resolve/main/pytorch_model.bin
1.35 GB
- Xet hash:
- 0bbffacc291f7e7eb7bea3df06c20e517ead315e8634161d510044186b5a3c2b
- Size of remote file:
- 1.35 GB
- SHA256:
- 3b90346b46af8a18e92092413bb2388ceadcf5fce28984910695b984e61d7f4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.