Instructions to use Hate-speech-CNERG/urdu-codemixed-abusive-MuRIL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hate-speech-CNERG/urdu-codemixed-abusive-MuRIL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hate-speech-CNERG/urdu-codemixed-abusive-MuRIL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hate-speech-CNERG/urdu-codemixed-abusive-MuRIL") model = AutoModelForSequenceClassification.from_pretrained("Hate-speech-CNERG/urdu-codemixed-abusive-MuRIL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Hate-speech-CNERG/urdu-codemixed-abusive-MuRIL: direct link, hf CLI and curl.
- Browser
- Download file 979 Bytes
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https://huggingface.co/Hate-speech-CNERG/urdu-codemixed-abusive-MuRIL/resolve/main/README.md
- Command line
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hf download hf://Hate-speech-CNERG/urdu-codemixed-abusive-MuRIL/README.md
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curl -L -o README.md https://huggingface.co/Hate-speech-CNERG/urdu-codemixed-abusive-MuRIL/resolve/main/README.md
979 Bytes
metadata
language: ur-en
license: afl-3.0
This model is used detecting abusive speech in Code-Mixed Urdu. It is finetuned on MuRIL model using code-mixed Urdu abusive speech dataset. The model is trained with learning rates of 2e-5. Training code can be found at this url
LABEL_0 :-> Normal LABEL_1 :-> Abusive
For more details about our paper
Mithun Das, Somnath Banerjee and Animesh Mukherjee. "Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages". Accepted at ACM HT 2022.
Please cite our paper in any published work that uses any of these resources.
@article{das2022data,
title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages},
author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2204.12543},
year={2022}
}