Instructions to use october-sd/MuRIL_relevance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use october-sd/MuRIL_relevance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="october-sd/MuRIL_relevance")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("october-sd/MuRIL_relevance") model = AutoModelForSequenceClassification.from_pretrained("october-sd/MuRIL_relevance") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1a39fe804ae24a022273a7ee237a315cf898cf206fee78d77ab09a295795c7a
- Size of remote file:
- 4.86 kB
- SHA256:
- 756e150409d985d3d6372f6aa65df359467f47ae25ef9eb843b5f3fe56ac74ef
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