--- language: "en" thumbnail: "https://pbs.twimg.com/profile_images/1092721745994440704/d6R-AHzj_400x400.jpg" tags: - propaganda - bert license: "MIT" datasets: - metrics: - --- Propaganda Techniques Analysis BERT ---- This model is a BERT based model to make predictions of propaganda techniques in news articles in English. The model is described in [this paper](https://propaganda.qcri.org/papers/EMNLP_2019__Fine_Grained_Propaganda_Detection.pdf). ## Model description Please find propaganda definition here: https://propaganda.qcri.org/annotations/definitions.html You can also try the model in action here: https://www.tanbih.org/prta ### How to use ```python >>> from transformers import BertTokenizerFast >>> from .model import BertForTokenAndSequenceJointClassification >>> >>> tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased') >>> model = BertForTokenAndSequenceJointClassification.from_pretrained( >>> "QCRI/PropagandaTechniquesAnalysis-en-BERT", >>> revision="v0.1.0", >>> ) >>> >>> inputs = tokenizer.encode_plus("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> sequence_class_index = torch.argmax(outputs.sequence_logits, dim=-1) >>> sequence_class = model.sequence_tags[sequence_class_index[0]] >>> token_class_index = torch.argmax(outputs.token_logits, dim=-1) >>> tokens = tokenizer.convert_ids_to_tokens(inputs.input_ids[0][1:-1]) >>> tags = [model.token_tags[i] for i in token_class_index[0].tolist()[1:-1]] ``` ### BibTeX entry and citation info ```bibtex @inproceedings{da-san-martino-etal-2019-fine, title = "Fine-Grained Analysis of Propaganda in News Article", author = "Da San Martino, Giovanni and Yu, Seunghak and Barr{\'o}n-Cede{\~n}o, Alberto and Petrov, Rostislav and Nakov, Preslav", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1565", doi = "10.18653/v1/D19-1565", pages = "5636--5646", abstract = "Propaganda aims at influencing people{'}s mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at fragment level with eighteen propaganda techniques and propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.", } ```
