Instructions to use svenbl80/roberta-base_contracts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svenbl80/roberta-base_contracts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="svenbl80/roberta-base_contracts")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("svenbl80/roberta-base_contracts") model = AutoModelForSequenceClassification.from_pretrained("svenbl80/roberta-base_contracts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from svenbl80/roberta-base_contracts: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/svenbl80/roberta-base_contracts/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://svenbl80/roberta-base_contracts/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/svenbl80/roberta-base_contracts/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 4274a4b8c80604efa02341cc9455e45eb13a21c3b34fc51f9ca4555264a2f22e
- Size of remote file:
- 499 MB
- SHA256:
- 1f1b87b05172c5ee3e20a80e31311a45488c0fb381e0326160e50810b6cbf88a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.