Instructions to use l3cube-pune/marathi-topic-all-doc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-topic-all-doc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/marathi-topic-all-doc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-topic-all-doc") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/marathi-topic-all-doc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c1ee6457a5617412912107bdb18342aaeadf0b521663acf98f9b0b7659e18fb
- Size of remote file:
- 950 MB
- SHA256:
- 281d4fe230b74c17cee6de091b1b39cbf45a54c5bc39940d4b23414a6d49c3fe
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