Instructions to use jenspt/roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jenspt/roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jenspt/roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jenspt/roberta") model = AutoModelForSequenceClassification.from_pretrained("jenspt/roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jenspt/roberta: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/jenspt/roberta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jenspt/roberta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jenspt/roberta/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 4fa54094cb31fcd9b952ac6d6d6d86690cbff97a265c0fcc240c512fe4dec409
- Size of remote file:
- 499 MB
- SHA256:
- d83452e0e7f57217f99889fff32c17fb3d74f91aabeb5b1696ace25931d2dd40
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.