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DS4AI-UPB/bert-base-uncased-misinfo-lora
bert-base-uncased-misinfo-lora is a text classification model from DS4AI-UPB. Use it when you need a label for a piece of text. It is set up for peft.
Authors: Andrei-Gabriel Radu, Ciprian-Octavian Truică, Elena-Simona Apostol
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From the Hugging Face model README
Authors: Andrei-Gabriel Radu, Ciprian-Octavian Truică, Elena-Simona Apostol
National University of Science and Technology POLITEHNICA Bucharest
LoRA adapter fine-tuned from bert-base-uncased for binary text-only misinformation classification on the FakeTT social-media video dataset.
This model accompanies the bachelor thesis Misinformation Detection in Social Media Videos.
| Dataset | Modality | Macro-F1 |
|---|---|---|
| FakeTT | Text-only | 0.8400 |
bert-base-uncasedr): 16query, value, key, denseimport torch
from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification
repo_id = "DS4AI-UPB/bert-base-uncased-misinfo-lora"
base_model_id = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
base_model = AutoModelForSequenceClassification.from_pretrained(base_model_id, num_labels=2)
model = PeftModel.from_pretrained(base_model, repo_id).eval()
text = "Example social media video description."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
logits = model(**inputs).logits
print(logits.argmax(dim=-1).item())
Use the class-to-label mapping from the original FakeTT training pipeline.
Research and benchmarking of English-language text-only misinformation detection for social-media video content.
This is a classification model, not a factual verification system. It cannot inspect the associated video and can degrade under domain shift.
@thesis{radu2026misinformation,
author = {Radu, Andrei-Gabriel and Truică, Ciprian-Octavian and Apostol, Elena-Simona},
title = {Misinformation Detection in Social Media Videos},
school = {National University of Science and Technology POLITEHNICA Bucharest},
year = {2026}
}