Instructions to use prajjwal1/bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/bert-tiny with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prajjwal1/bert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from prajjwal1/bert-tiny: direct link, hf CLI and curl.
- Browser
- Download file 635 Bytes
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https://huggingface.co/prajjwal1/bert-tiny/resolve/c1b89bbe21a3bc8ef093e4be5c4aad352d329b01/README.md
- Command line
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hf download hf://prajjwal1/bert-tiny@c1b89bbe21a3bc8ef093e4be5c4aad352d329b01/README.md
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curl -L -o README.md https://huggingface.co/prajjwal1/bert-tiny/resolve/c1b89bbe21a3bc8ef093e4be5c4aad352d329b01/README.md
635 Bytes
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository. These BERT variants were introduced in the paper Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. These models are supposed to be trained on a downstream task.
You can check out:
prajjwal1/bert-tiny(L=2, H=128)prajjwal1/bert-mini(L=4, H=256)prajjwal1/bert-small(L=4, H=512)prajjwal1/bert-medium(L=8, H=512)