Instructions to use alex2awesome/stance-detection-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alex2awesome/stance-detection-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alex2awesome/stance-detection-classification-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alex2awesome/stance-detection-classification-model") model = AutoModelForSequenceClassification.from_pretrained("alex2awesome/stance-detection-classification-model") - Notebooks
- Google Colab
- Kaggle
Stance detection model distilled from a news dataset label by a larger model. The larger model was trained on a combination of stance datasets in the literature:
FNC-1 (Pomerleau and Rao, 2017), Perspectrum (Chen et al., 2019), ARC (Habernal et al., 2017), Emergent (Ferreira and Vlachos, 2016) NewsClaims (Reddy et al., 2021)7.
Acheives this score on distilled data. Hand-checked data to come, but similar models with similar scores achieved a 0.57.
"eval_samples": 6482,
"test_f1": 0.43183643890513856,
"test_loss": 2.9971516132354736,
"test_runtime": 46.1838,
"test_samples_per_second": 140.352,
"test_steps_per_second": 8.791
}```
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