Text Classification
Transformers
PyTorch
English
roberta
text
stance
Eval Results (legacy)
text-embeddings-inference
Instructions to use eevvgg/Stance-Tw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eevvgg/Stance-Tw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eevvgg/Stance-Tw")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eevvgg/Stance-Tw") model = AutoModelForSequenceClassification.from_pretrained("eevvgg/Stance-Tw", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43417ba403e892add1201f6ddd863347ffc7bebee858010cd8e812b02fabe9eb
- Size of remote file:
- 1.42 GB
- SHA256:
- 83acf6af0c2a24af6696f21e3e78f82b67d0000f3504c903f78bccee37d7329d
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