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
MahaSent-MD
MahaSent-MD is a MahaBERT(l3cube-pune/marathi-bert-v2) model fine-tuned on full L3Cube-MahaSent-MD Corpus, a multi-domain Marathi sentiment analysis dataset.
The MahaSent-MD dataset contains domains like movie reviews, generic tweets, subtitles, and political tweets. This model is trained on all the domains.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2306.13888)
Citing:
@article{pingle2023l3cube,
title={L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models},
author={Pingle, Aabha and Vyawahare, Aditya and Joshi, Isha and Tangsali, Rahul and Joshi, Raviraj},
journal={arXiv preprint arXiv:2306.13888},
year={2023}
}
@article{joshi2022l3cube,
title={L3cube-mahanlp: Marathi natural language processing datasets, models, and library},
author={Joshi, Raviraj},
journal={arXiv preprint arXiv:2205.14728},
year={2022}
}
Other Marathi Sentiment models from MahaSent family are shared here:
MahaSent-MD (multi domain)
MahaSent-GT (generic tweets)
MahaSent-MR (movie reviews)
MahaSent-PT (political tweets)
MahaSent-ST (TV subtitles)
MahaSent v1 (political tweets)
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