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](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962). 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) | |
| [@prajjwal_1](https://twitter.com/prajjwal_1) | |