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 flax_model.msgpack from google-bert/bert-large-uncased-whole-word-masking: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/google-bert/bert-large-uncased-whole-word-masking/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google-bert/bert-large-uncased-whole-word-masking/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google-bert/bert-large-uncased-whole-word-masking/resolve/main/flax_model.msgpack
1.34 GB
- Xet hash:
- 84425258e3fc4b5c968bd31815e07d99eb2adfa161409724765672609a79f154
- Size of remote file:
- 1.34 GB
- SHA256:
- abdcbd43555c4a2d4f4c57db1c833cdcf7d1570ed42920c3bf0200d5cf42a8c1
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