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 training_args.bin from ModelTC/roberta-base-sst2: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/ModelTC/roberta-base-sst2/resolve/main/training_args.bin
- Command line
-
hf download hf://ModelTC/roberta-base-sst2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ModelTC/roberta-base-sst2/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- d810a64f09327bca69ef580a7895ea614287ce064e1c7817474f364563e9d728
- Size of remote file:
- 3.06 kB
- SHA256:
- c7e440150530807bf13dcb780c05b33c0df5ee5dc9ba1bcf062637a89e3b7523
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