Instructions to use google-bert/bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-uncased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download rust_model.ot from google-bert/bert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 534 MB
-
https://huggingface.co/google-bert/bert-base-uncased/resolve/refs%2Fpr%2F28/rust_model.ot
- Command line
-
hf download hf://google-bert/bert-base-uncased@refs/pr/28/rust_model.ot
-
curl -L -o rust_model.ot https://huggingface.co/google-bert/bert-base-uncased/resolve/refs%2Fpr%2F28/rust_model.ot
534 MB
- Xet hash:
- 33ffdfe1fe90b9aaa2e79168c0141c72645c865bf1990b7a0975929862c34b45
- Size of remote file:
- 534 MB
- SHA256:
- afd9aa425fd45c5655d3d43a0d041f9b76729bf475d6c017a0e9304a38f89972
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.