Instructions to use ScriptEdgeAI/MarathiSentiment-Bloom-560m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ScriptEdgeAI/MarathiSentiment-Bloom-560m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ScriptEdgeAI/MarathiSentiment-Bloom-560m")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ScriptEdgeAI/MarathiSentiment-Bloom-560m") model = AutoModelForSequenceClassification.from_pretrained("ScriptEdgeAI/MarathiSentiment-Bloom-560m") - Notebooks
- Google Colab
- Kaggle
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README.md
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78% on test data.
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@article{joshi2022l3cube,
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title={L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library},
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author={Joshi, Raviraj},
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78% on test data.
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Citation to L3CubePune by the dataset usage.
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@article{joshi2022l3cube,
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title={L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library},
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author={Joshi, Raviraj},
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