Instructions to use l3cube-pune/marathi-sentiment-md with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-sentiment-md with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/marathi-sentiment-md")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-sentiment-md") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/marathi-sentiment-md") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -19,6 +19,14 @@ The MahaSent-MD dataset contains domains like movie reviews, generic tweets, sub
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More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2306.13888)
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<br>
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Citing:
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```
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@article{joshi2022l3cube,
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More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2306.13888)
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<br>
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Citing:
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```
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@article{pingle2023l3cube,
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title={L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models},
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author={Pingle, Aabha and Vyawahare, Aditya and Joshi, Isha and Tangsali, Rahul and Joshi, Raviraj},
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journal={arXiv preprint arXiv:2306.13888},
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year={2023}
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}
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```
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```
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@article{joshi2022l3cube,
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