Text Classification
Transformers
Safetensors
bert
politics
twitter
tweets
issues
text-embeddings-inference
Instructions to use z-dickson/issue_classification_tweets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use z-dickson/issue_classification_tweets with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="z-dickson/issue_classification_tweets")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("z-dickson/issue_classification_tweets") model = AutoModelForSequenceClassification.from_pretrained("z-dickson/issue_classification_tweets") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -14,4 +14,15 @@ This model classified politicians' tweets in English according to nine issues:
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- Defense
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- Environment
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The model was used to classify Twitter messages to study responsiveness to public issue salience
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- Defense
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- Environment
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The model was used to classify Twitter messages to study responsiveness to public issue salience in the following article: https://doi.org/10.1017/S153759272400104X.
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If you find the model useful and would like to cite it, it would be greatly if you could cite the following:
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@article{dickson2024gender,
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title={The Gender Gap in Elite-Voter Responsiveness Online},
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author={Dickson, Zachary P},
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journal={Perspectives on Politics},
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pages={1--17},
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year={2024},
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publisher={Cambridge University Press}
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}
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