Instructions to use IIIT-L/indic-bert-finetuned-ours-DS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIIT-L/indic-bert-finetuned-ours-DS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIIT-L/indic-bert-finetuned-ours-DS")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIIT-L/indic-bert-finetuned-ours-DS") model = AutoModelForSequenceClassification.from_pretrained("IIIT-L/indic-bert-finetuned-ours-DS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from IIIT-L/indic-bert-finetuned-ours-DS: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/IIIT-L/indic-bert-finetuned-ours-DS/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://IIIT-L/indic-bert-finetuned-ours-DS/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/IIIT-L/indic-bert-finetuned-ours-DS/resolve/main/pytorch_model.bin
134 MB
- Xet hash:
- 03a2c0c267d90213d8d99b63f85fc821f2fdd751bd26428f011462b59a5df5ad
- Size of remote file:
- 134 MB
- SHA256:
- f7f3ff7876ae03930c0bc9d318c440d6e554b85089c8ce81d44c0f2bfb1e721c
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