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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DeepWokLab/bert-tiny") model = AutoModel.from_pretrained("DeepWokLab/bert-tiny") - Notebooks
- Google Colab
- Kaggle
File size: 484 Bytes
6253c70 d6fcd6e 6253c70 d6fcd6e 6253c70 d6fcd6e 6253c70 d6fcd6e 6253c70 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ---
library_name: transformers
tags: []
---
# BERT-Tiny
<!-- Bert-tiny checkpoint from Google's BERT repo. -->
This checkpoint was originally from [Google's BERT repository](https://github.com/google-research/bert),
and was converted to HuggingFace format by [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny).
We forces contiguous tensors in the `state_dict` and uploaded here for [MASE]((https://deepwok.github.io/mase/)) and Advanced Deep Learning System Lab.
|