Instructions to use eevvgg/StanceBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eevvgg/StanceBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eevvgg/StanceBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eevvgg/StanceBERTa") model = AutoModelForSequenceClassification.from_pretrained("eevvgg/StanceBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Browse files
README.md
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<!-- Provide a quick summary of what the model is/does. -->
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This model is a fine-tuned version of **distilroberta-base** model to predict 3 categories of stance (negative, positive, neutral) towards some entity mentioned in the text.
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Fine-tuned on a larger and more balanced data sample compared with the previous version [eevvgg/
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- **Developed by:** Ewelina Gajewska
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<!-- Provide a quick summary of what the model is/does. -->
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This model is a fine-tuned version of **distilroberta-base** model to predict 3 categories of stance (negative, positive, neutral) towards some entity mentioned in the text.
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Fine-tuned on a larger and more balanced data sample compared with the previous version [eevvgg/Stance-Tw](https://huggingface.co/eevvgg/Stance-Tw).
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- **Developed by:** Ewelina Gajewska
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