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 training_args.bin from IIIT-L/indic-bert-finetuned-ours-DS: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/IIIT-L/indic-bert-finetuned-ours-DS/resolve/main/training_args.bin
- Command line
-
hf download hf://IIIT-L/indic-bert-finetuned-ours-DS/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/IIIT-L/indic-bert-finetuned-ours-DS/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 277ebcdc7e78cea7905f04f010c7090ee94395db86132e9cd12ba2d81ffea023
- Size of remote file:
- 3.31 kB
- SHA256:
- 6383fcf2fa08e75d21e5871b7b26fdb424ed6c3cdddfc9eddf1df2e5e229ae0e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.