Text Classification
Transformers
PyTorch
Safetensors
Marathi
English
multilingual
xlm-roberta
codemix
text-embeddings-inference
Instructions to use l3cube-pune/me-hate-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/me-hate-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/me-hate-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/me-hate-roberta") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/me-hate-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -18,6 +18,8 @@ MeHate-RoBERTa is a MeRoBERTa-Mixed model fine-tuned on L3Cube-MeHate, a codemix
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[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
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More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2306.14030)
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Other models from the MeBERT family: <br>
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[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
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LABEL_0 = Non-Hate, LABEL_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/2306.14030)
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Other models from the MeBERT family: <br>
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