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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## MarathiSentiment
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** An updated version of this model covering multiple domains is shared here: <a href="https://huggingface.co/l3cube-pune/marathi-sentiment-md"> marathi-sentiment-md </a> ** <br>
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MarathiSentiment is an IndicBERT(ai4bharat/indic-bert) model fine-tuned on L3CubeMahaSent - a Marathi tweet-based sentiment analysis dataset.
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[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
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## MarathiSentiment
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** An updated and better version of this model covering multiple domains is shared here: <a href="https://huggingface.co/l3cube-pune/marathi-sentiment-md"> marathi-sentiment-md </a> ** <br>
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MarathiSentiment is an IndicBERT(ai4bharat/indic-bert) model fine-tuned on L3CubeMahaSent - a Marathi tweet-based sentiment analysis dataset.
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[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
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