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
up model card stance
Browse files
README.md
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# BEtMan-Tw
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This model is a fine-tuned version of [j-hartmann/sentiment-roberta-large-english-3-classes](https://huggingface.co/j-hartmann/sentiment-roberta-large-english-3-classes) to predict 3 categories.
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- training procedure available in [Colab notebook](https://colab.research.google.com/drive/12DsO5dNaQI3kFO7ohOHZn4EWNewFy2jm?usp=sharing)
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# BEtMan-Tw
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This model is a fine-tuned version of [j-hartmann/sentiment-roberta-large-english-3-classes](https://huggingface.co/j-hartmann/sentiment-roberta-large-english-3-classes) to predict 3 categories of author stance (attack, support, neutral) towards an entity mentioned in the text.
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- training procedure available in [Colab notebook](https://colab.research.google.com/drive/12DsO5dNaQI3kFO7ohOHZn4EWNewFy2jm?usp=sharing)
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