Instructions to use DeepWokLab/bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepWokLab/bert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DeepWokLab/bert-tiny")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DeepWokLab/bert-tiny") model = AutoModel.from_pretrained("DeepWokLab/bert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from DeepWokLab/bert-tiny: direct link, hf CLI and curl.
- Browser
- Download file 484 Bytes
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https://huggingface.co/DeepWokLab/bert-tiny/resolve/main/README.md
- Command line
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hf download hf://DeepWokLab/bert-tiny/README.md
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curl -L -o README.md https://huggingface.co/DeepWokLab/bert-tiny/resolve/main/README.md
484 Bytes
metadata
library_name: transformers
tags: []
BERT-Tiny
This checkpoint was originally from Google's BERT repository, and was converted to HuggingFace format by prajjwal1/bert-tiny.
We forces contiguous tensors in the state_dict and uploaded here for MASE and Advanced Deep Learning System Lab.