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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/roberta-base-sst2") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/roberta-base-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from ModelTC/roberta-base-sst2: direct link, hf CLI and curl.
- Browser
- Download file 997 MB
-
https://huggingface.co/ModelTC/roberta-base-sst2/resolve/main/optimizer.pt
- Command line
-
hf download hf://ModelTC/roberta-base-sst2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ModelTC/roberta-base-sst2/resolve/main/optimizer.pt
997 MB
- Xet hash:
- 2785c0e427f1a1dec2b80206c93e8040c90b13003a2ff8a17b76441ac7661fdf
- Size of remote file:
- 997 MB
- SHA256:
- e35fcab13ef84b0ef597e4f55ce4ad924be748df5a326624f8adc3d310db8d05
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