Instructions to use harshil10/birt_tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshil10/birt_tiny with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("harshil10/birt_tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from harshil10/birt_tiny: direct link, hf CLI and curl.
- Browser
- Download file 1.27 kB
-
https://huggingface.co/harshil10/birt_tiny/resolve/main/README.md
- Command line
-
hf download hf://harshil10/birt_tiny/README.md
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curl -L -o README.md https://huggingface.co/harshil10/birt_tiny/resolve/main/README.md
1.27 kB
metadata
language:
- en
license:
- mit
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository.
This is one of the smaller pre-trained BERT variants, together with bert-mini bert-small and bert-medium. They were introduced in the study Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (arxiv), and ported to HF for the study Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics (arXiv). These models are supposed to be trained on a downstream task.
If you use the model, please consider citing both the papers:
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
primaryClass={cs.CL}
}