https://github.com/smappnyu/smaberta
Wrapper for stable version of RoBERTa language models
https://github.com/smappnyu/smaberta
huggingface nlp roberta transfer-learning
Last synced: 6 months ago
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Wrapper for stable version of RoBERTa language models
- Host: GitHub
- URL: https://github.com/smappnyu/smaberta
- Owner: SMAPPNYU
- License: mit
- Created: 2019-12-19T21:14:02.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-06-12T21:28:11.000Z (over 2 years ago)
- Last Synced: 2025-04-10T23:43:11.950Z (6 months ago)
- Topics: huggingface, nlp, roberta, transfer-learning
- Language: Python
- Homepage: https://csmapnyu.org
- Size: 173 KB
- Stars: 10
- Watchers: 13
- Forks: 9
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SMaBERTa
This repository contains the code for SMaBERTa, a wrapper for the huggingface transformer libraries.
It was developed by Zhanna Terechshenko and Vishakh Padmakumar through research at the Center for
Social Media and Politics at NYU.
## Setup
To install using pip, run
```
pip install smaberta
```
To install from the source, first download the repository by running
```
git clone https://github.com/SMAPPNYU/SMaBERTa.git
```
Then, install the dependencies for this repo and setup by running
```
cd SMaBERTa
pip install -r requirements.txt
python setup.py install
```
## Using the package
Basic use:
```
from smaberta import TransformerModel
epochs = 3
lr = 4e-6
training_sample = ['Today is a great day', 'Today is a terrible day']
training_labels = [1, 0]
model = TransformerModel('roberta', 'roberta-base', num_labels=25, reprocess_input_data=True,
num_train_epochs=epochs, learning_rate=lr, output_dir='./saved_model/',
overwrite_output_dir=True, fp16=False)
model.train(training_sample, training_labels)
```
For further details, see `Tutorial.ipynb` in the [examples](https://github.com/SMAPPNYU/SMaBERTa/tree/master/examples) directory.
# Acknowledgements
Code for this project was adapted from version 0.6 of https://github.com/ThilinaRajapakse/simpletransformers
Vishakh Padmakumar and Zhanna Terechshenko contributed to the software writing, implementation, and testing.
Megan Brown contributed to documentation and publication.
If you use this software in your research please cite it as:
```
@misc{padmakumar_terechshenko,
author = {Vishakh Padmakumar and Zhanna Terechshenko},
title = {SMAPPNYU/SMaBERTa},
month = dec,
year = 2020,
doi = {10.5281/zenodo.5090728},
url = {https://doi.org/10.5281/zenodo.5090728}
}
```