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--- |
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language: |
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- en |
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license: lgpl-3.0 |
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library_name: transformers |
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tags: |
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- text-classification |
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- transformers |
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- pytorch |
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- generated_from_keras_callback |
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datasets: |
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- m-newhauser/senator-tweets |
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metrics: |
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- accuracy |
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- f1 |
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widget: |
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- text: This pandemic has shown us clearly the vulgarity of our healthcare system. |
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Highest costs in the world, yet not enough nurses or doctors. Many millions uninsured, |
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while insurance company profits soar. The struggle continues. Healthcare is a |
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human right. Medicare for all. |
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example_title: Bernie Sanders (D) |
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- text: Team Biden would rather fund the Ayatollah's Death to America regime than |
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allow Americans to produce energy for our own domestic consumption. |
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example_title: Ted Cruz (R) |
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base_model: distilbert-base-uncased |
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--- |
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# distilbert-political-tweets 🗣 🇺🇸 |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [m-newhauser/senator-tweets](https://huggingface.co/datasets/m-newhauser/senator-tweets) dataset, which contains all tweets made by United States senators during the first year of the Biden Administration. |
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It achieves the following results on the evaluation set: |
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* Accuracy: 0.9076 |
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* F1: 0.9117 |
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## Model description |
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The goal of this model is to classify short pieces of text as having either Democratic or Republican sentiment. The model was fine-tuned on 99,693 tweets (51.6% Democrat, 48.4% Republican) made by US senators in 2021. |
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Model accuracy may not hold up on pieces of text longer than a tweet. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: Adam |
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- training_precision: float32 |
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- learning_rate = 5e-5 |
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- num_epochs = 5 |
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### Framework versions |
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- Transformers 4.16.2 |
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- TensorFlow 2.8.0 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.6 |
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