Instructions to use ModelTC/roberta-base-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/roberta-base-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/roberta-base-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/roberta-base-sst2") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/roberta-base-sst2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 025c6b795307ee7b36cfbc97340842cabe3cdc44cd42374afcf7a43ece2060fe
- Size of remote file:
- 499 MB
- SHA256:
- b9afe4a71de317b22919517eb9c369786dad608a31a7e5f9c5faedf7592b2b77
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.