thomasrenault/topic

A multi-label political topic classifier fine-tuned on US tweets, campaign speeches and congressional speeches. Built on distilbert-base-uncased with GPT-4o-mini annotation via the OpenAI Batch API.

Labels

The model predicts 7 independent topic indicators (sigmoid, threshold 0.5).
A document can belong to zero or multiple topics simultaneously.

Label	Description
abortion	Abortion rights and reproductive policy
democracy	Elections, voting rights, democratic institutions
gender equality	Gender rights, feminism, LGBTQ+ issues
gun control	Firearms regulation, Second Amendment
immigration	Immigration policy, border control, citizenship
tax and inequality	Tax policy, economic inequality, redistribution
trade	Trade policy, tariffs, international commerce

Documents that match none of the above are implicitly classified as other topic.

Training

Setting	Value
Base model	distilbert-base-uncased
Architecture	DistilBertForSequenceClassification (multi-label)
Problem type	multi_label_classification
Training data	~200,000 labeled documents
Annotation	GPT-4o-mini (temperature=0) via OpenAI Batch API
Epochs	4
Learning rate	2e-5
Batch size	16
Max length	512 tokens
Classification threshold	0.5
Domain	US tweets about policy, campaign speeches and congressional floor speeches

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "thomasrenault/topic"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model     = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

TOPICS    = ["abortion", "democracy", "gender equality", "gun control",
             "immigration", "tax and inequality", "trade"]
THRESHOLD = 0.5

def predict(text):
    enc = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        probs = torch.sigmoid(model(**enc).logits).squeeze().tolist()
    matched = [t for t, p in zip(TOPICS, probs) if p >= THRESHOLD]
    return matched or ["other topic"]

print(predict("We need stronger border security and immigration reform."))
# ["immigration"]

print(predict("Tax cuts for the wealthy only increase inequality in America."))
# ["tax and inequality"]

Intended Use

• Academic research on political agenda-setting and issue salience
• Topic trend analysis across congressional speeches and social media
• Cross-platform comparison of elite vs. citizen political communication

Limitations

• Trained on US English political text — may not generalise to other political systems or languages
• Annotation by GPT-4o-mini introduces model-specific biases in topic boundaries
• Topics reflect the specific research agenda of the parent project; other salient topics (healthcare, climate, etc.) are out of scope

Citation

If you use this model, please cite:

@misc{renault2025topic,
  author    = {Renault, Thomas},
  title     = {thomasrenault/topic: Multi-label political topic classifier for US political text},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/thomasrenault/topic}
}