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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@@ -16,9 +16,12 @@ This model classified politicians' tweets in English according to nine issues:
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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
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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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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 it!
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- APA: Dickson, Z. P. (2024). The Gender Gap in Elite-Voter Responsiveness Online. Perspectives on Politics, 1-17.
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- BibTeX: @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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