Instructions to use google/bert_uncased_L-2_H-128_A-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/bert_uncased_L-2_H-128_A-2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/bert_uncased_L-2_H-128_A-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/bert_uncased_L-2_H-128_A-2: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/google/bert_uncased_L-2_H-128_A-2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/bert_uncased_L-2_H-128_A-2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/bert_uncased_L-2_H-128_A-2/resolve/main/flax_model.msgpack
17.5 MB
- Xet hash:
- 95e252539faeef22e6f35cef4516dcea40a66c7c077b8aad5598e3a6d50fd7c3
- Size of remote file:
- 17.5 MB
- SHA256:
- d1f885a549870769ba8b61c96b051ed6a98b68573062f03fd806fcaa9712eeb4
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