Text Classification
Transformers
Keras
Safetensors
English
Bengali
bert
indian-sign-language
multilingual
Instructions to use rahul2025/isl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rahul2025/isl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rahul2025/isl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rahul2025/isl") model = AutoModelForSequenceClassification.from_pretrained("rahul2025/isl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rahul2025/isl: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/rahul2025/isl/resolve/main/training_args.bin
- Command line
-
hf download hf://rahul2025/isl/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rahul2025/isl/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- b1a9d7d1b651a13f9211528ffdc4cca4a88e025f2f73498a428cfb277f49a8a0
- Size of remote file:
- 5.37 kB
- SHA256:
- d967bf496ecf407adf4efdaa7963c8a3daa03b5ea1b8105d006d0b12d5abfffd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.