Instructions to use l3cube-pune/marathi-topic-long-doc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-topic-long-doc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/marathi-topic-long-doc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-topic-long-doc") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/marathi-topic-long-doc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a291a861e11a6672e51cc6b5ffb0bfab1347faa9859f4c3ed333a182b12e9af6
- Size of remote file:
- 950 MB
- SHA256:
- 77df2dd375fe57751b2a1c9eb7bc9206ee0eb1c728959b2652bf3b5b57110517
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