Instructions to use crashx/telugu-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crashx/telugu-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="crashx/telugu-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("crashx/telugu-roberta-base") model = AutoModelForMaskedLM.from_pretrained("crashx/telugu-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from crashx/telugu-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/crashx/telugu-roberta-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://crashx/telugu-roberta-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/crashx/telugu-roberta-base/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 0d4e8eecf6c6f7991363c51c7bcb1ce01f9dcabdbec03be98dae2f10c762ee03
- Size of remote file:
- 499 MB
- SHA256:
- fe966512d72d627347210aa8c4c6d27c1f5fa228f9f65cb607ec3052cdb25582
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