Instructions to use DS4AI-UPB/timesformer-misinfo-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DS4AI-UPB/timesformer-misinfo-lora with PEFT:
Task type is invalid.
- Transformers
How to use DS4AI-UPB/timesformer-misinfo-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="DS4AI-UPB/timesformer-misinfo-lora")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DS4AI-UPB/timesformer-misinfo-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
TimeSformer — Video-Only Misinformation Detection on FakeTT
Authors: Andrei-Gabriel Radu, Ciprian-Octavian Truică, Elena-Simona Apostol
National University of Science and Technology POLITEHNICA Bucharest
LoRA adapter fine-tuned from facebook/timesformer-base-finetuned-k600 for binary video-only misinformation classification on FakeTT.
This model accompanies the bachelor thesis Misinformation Detection in Social Media Videos.
Results
| Dataset | Modality | Macro-F1 |
|---|---|---|
| FakeTT | Video-only | 0.7983 |
Model
- Base model:
facebook/timesformer-base-finetuned-k600 - Task: Binary misinformation classification
- Modality: Video-only
- Fine-tuning: LoRA / PEFT
- Dataset: FakeTT
- Number of classes: 2
- Primary metric: Macro-F1
Training
- LoRA rank (
r): 8 - LoRA alpha: 32
- LoRA dropout: 0.05
- Target modules:
key,value,query,dense - Bias: none
Usage
from peft import PeftModel
from transformers import AutoImageProcessor, AutoModelForVideoClassification
repo_id = "DS4AI-UPB/timesformer-misinfo-lora"
base_model_id = "facebook/timesformer-base-finetuned-k600"
processor = AutoImageProcessor.from_pretrained(repo_id)
base_model = AutoModelForVideoClassification.from_pretrained(
base_model_id, num_labels=2, ignore_mismatched_sizes=True
)
model = PeftModel.from_pretrained(base_model, repo_id).eval()
Use the same frame sampling and preprocessing procedure as during training before passing pixel_values to the model.
Intended Use
Research and benchmarking of video-only misinformation detection on short social-media videos.
Limitations
The model does not use titles, descriptions or other textual metadata. It can miss linguistic misinformation cues and can degrade under domain shift or a different frame-sampling strategy.
Citation
@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}
}
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Model tree for DS4AI-UPB/timesformer-misinfo-lora
Base model
facebook/timesformer-base-finetuned-k600