Instructions to use gustavecortal/roberta_comp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gustavecortal/roberta_comp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gustavecortal/roberta_comp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gustavecortal/roberta_comp") model = AutoModelForSequenceClassification.from_pretrained("gustavecortal/roberta_comp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gustavecortal/roberta_comp: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/gustavecortal/roberta_comp/resolve/main/training_args.bin
- Command line
-
hf download hf://gustavecortal/roberta_comp/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gustavecortal/roberta_comp/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- 4ffcc45d977b1479165b33664f881d9c166cfc3d6f1596fb470803f353e50ea5
- Size of remote file:
- 3.39 kB
- SHA256:
- cd8fa52d974da900afac624c7814068c237c1da5403ffe17f68448cce641884e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.