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--- |
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language: "en" |
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thumbnail: "https://pbs.twimg.com/profile_images/1092721745994440704/d6R-AHzj_400x400.jpg" |
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tags: |
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- propaganda |
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- bert |
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license: "MIT" |
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datasets: |
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- |
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metrics: |
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- |
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--- |
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Propaganda Techniques Analysis BERT |
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---- |
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This model is a BERT based model to make predictions of propaganda techniques in |
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news articles in English. The model is described in |
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[this paper](https://propaganda.qcri.org/papers/EMNLP_2019__Fine_Grained_Propaganda_Detection.pdf). |
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## Model description |
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Please find propaganda definition here: |
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https://propaganda.qcri.org/annotations/definitions.html |
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You can also try the model in action here: https://www.tanbih.org/prta |
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### How to use |
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```python |
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>>> from transformers import BertTokenizerFast |
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>>> from .model import BertForTokenAndSequenceJointClassification |
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>>> |
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>>> tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased') |
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>>> model = BertForTokenAndSequenceJointClassification.from_pretrained( |
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>>> "QCRI/PropagandaTechniquesAnalysis-en-BERT", |
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>>> revision="v0.1.0", |
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>>> ) |
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>>> |
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>>> inputs = tokenizer.encode_plus("Hello, my dog is cute", return_tensors="pt") |
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>>> outputs = model(**inputs) |
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>>> sequence_class_index = torch.argmax(outputs.sequence_logits, dim=-1) |
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>>> sequence_class = model.sequence_tags[sequence_class_index[0]] |
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>>> token_class_index = torch.argmax(outputs.token_logits, dim=-1) |
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>>> tokens = tokenizer.convert_ids_to_tokens(inputs.input_ids[0][1:-1]) |
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>>> tags = [model.token_tags[i] for i in token_class_index[0].tolist()[1:-1]] |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@inproceedings{da-san-martino-etal-2019-fine, |
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title = "Fine-Grained Analysis of Propaganda in News Article", |
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author = "Da San Martino, Giovanni and |
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Yu, Seunghak and |
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Barr{\'o}n-Cede{\~n}o, Alberto and |
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Petrov, Rostislav and |
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Nakov, Preslav", |
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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)", |
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month = nov, |
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year = "2019", |
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address = "Hong Kong, China", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/D19-1565", |
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doi = "10.18653/v1/D19-1565", |
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pages = "5636--5646", |
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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.", |
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} |
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``` |
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