Instructions to use l3cube-pune/MarathiSentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/MarathiSentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/MarathiSentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/MarathiSentiment") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/MarathiSentiment") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -18,10 +18,11 @@ MarathiSentiment is an IndicBERT(ai4bharat/indic-bert) model fine-tuned on L3Cub
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More details on the dataset, models, and baseline results can be found in our [paper] (http://arxiv.org/abs/2103.11408)
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```
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@
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title={L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset},
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author={Kulkarni, Atharva and Mandhane, Meet and Likhitkar, Manali and Kshirsagar, Gayatri and Joshi, Raviraj},
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-
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year={2021}
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}
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```
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More details on the dataset, models, and baseline results can be found in our [paper] (http://arxiv.org/abs/2103.11408)
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```
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@inproceedings{kulkarni2021l3cubemahasent,
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title={L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset},
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author={Kulkarni, Atharva and Mandhane, Meet and Likhitkar, Manali and Kshirsagar, Gayatri and Joshi, Raviraj},
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booktitle={Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
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pages={213--220},
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year={2021}
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}
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```
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