Instructions to use l3cube-pune/hate-bert-hasoc-marathi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hate-bert-hasoc-marathi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/hate-bert-hasoc-marathi")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hate-bert-hasoc-marathi") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/hate-bert-hasoc-marathi") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -17,6 +17,10 @@ The label mappings are 0 -> None, 1 -> Hate.
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More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2110.12200)
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```
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@article{velankar2021hate,
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title={Hate and Offensive Speech Detection in Hindi and Marathi},
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More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2110.12200)
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A new version of Marathi Hate Speech Detection models can be found here: <br>
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binary: https://huggingface.co/l3cube-pune/mahahate-bert <br>
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multi label: https://huggingface.co/l3cube-pune/mahahate-multi-roberta <br>
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```
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@article{velankar2021hate,
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title={Hate and Offensive Speech Detection in Hindi and Marathi},
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