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
- Xet hash:
- f9d60d3efda41dcacf6230473dfb0bc95b0ec87764ebe597d3cc5620784601ac
- Size of remote file:
- 950 MB
- SHA256:
- f6ebe76b10bc9e0403f46035b0e34dd0cc33c057681dc6d29f3c713422694c98
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