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:
# 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 pytorch_model.bin from google/bert_uncased_L-2_H-128_A-2: direct link, hf CLI and curl.
- Browser
- Download file 17.7 MB
-
https://huggingface.co/google/bert_uncased_L-2_H-128_A-2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/bert_uncased_L-2_H-128_A-2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/bert_uncased_L-2_H-128_A-2/resolve/main/pytorch_model.bin
17.7 MB
- Xet hash:
- 04d884f739d7f8118e0b1f16e24e59d9b8b22f523061ea10e21bd8f12089d65d
- Size of remote file:
- 17.7 MB
- SHA256:
- dd152f8450c0579bd271ac0ccb4a88fa4f6a67d8035b7799dbf3a0fb7156d9d0
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