Instructions to use rwillh11/mdeberta_groups_2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rwillh11/mdeberta_groups_2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rwillh11/mdeberta_groups_2.0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rwillh11/mdeberta_groups_2.0") model = AutoModelForSequenceClassification.from_pretrained("rwillh11/mdeberta_groups_2.0") - Notebooks
- Google Colab
- Kaggle
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# Model Card for Model ID
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## Model Details
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# Model Card for Model ID
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This model takes the various social groups that might be mentioned in political speech, and assigns them to different meaningful groups. It allows the same text string to belong to multiple social groups, for example "girls" are mapped to both "women" and "children"
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See Dolinsky et al (2025) for more information on social group categories and Horne et al (2025) for details on training, relation to other models, and use cases.
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📊 Evaluation Results: {'eval_loss': 0.024197660386562347, 'eval_accuracy': 0.994262946025139, 'eval_f1': 0.8552677029360967, 'eval_precision': 0.8657342657342657, 'eval_recall': 0.8450511945392492, 'eval_runtime': 5.9184, 'eval_samples_per_second': 232.831, 'eval_steps_per_second': 29.231, 'epoch': 29.914368650217707}
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## Model Details
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