Instructions to use subbareddyoota/ltrc-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subbareddyoota/ltrc-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subbareddyoota/ltrc-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subbareddyoota/ltrc-roberta") model = AutoModelForMaskedLM.from_pretrained("subbareddyoota/ltrc-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from subbareddyoota/ltrc-roberta: direct link, hf CLI and curl.
- Browser
- Download file 998 MB
-
https://huggingface.co/subbareddyoota/ltrc-roberta/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://subbareddyoota/ltrc-roberta@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/subbareddyoota/ltrc-roberta/resolve/refs%2Fpr%2F1/optimizer.pt
998 MB
- Xet hash:
- 41e1348c423dcd3f61b17cb7ec9ec9e6c67a91ec2b3d5a09e06c3e69c5a7053e
- Size of remote file:
- 998 MB
- SHA256:
- 307928d9bd8c28dd824d3803a4672a344e6f25b3ac7f42d97db3b9c6606a98e2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.