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--- |
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language: en |
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widget: |
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- text: Still unemployed... |
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datasets: |
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- worldbank/twitter-labor-market |
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--- |
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# Detection of employment status disclosures on Twitter |
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## Model main characteristics: |
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- class: Is Unemployed (1), else (0) |
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- country: US |
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- language: English |
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- architecture: BERT base |
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## Model description |
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This model is a version of `DeepPavlov/bert-base-cased-conversational` finetuned by [@manueltonneau](https://huggingface.co/manueltonneau) to recognize English tweets where a user mentions that she is unemployed. It was trained on English tweets from US-based users. The task is framed as a binary classification problem with: |
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- the positive class referring to tweets mentioning that a user is currently unemployed (label=1) |
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- the negative class referring to all other tweets (label=0) |
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## Resources |
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The dataset of English tweets on which this classifier was trained is open-sourced [here](https://huggingface.co/datasets/worldbank/twitter-labor-market). |
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Details on the performance can be found in our [ACL 2022 paper](https://aclanthology.org/2022.acl-long.453/). |
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## Citation |
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If you find this model useful, please cite our paper: |
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``` |
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@inproceedings{tonneau-etal-2022-multilingual, |
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title = "Multilingual Detection of Personal Employment Status on {T}witter", |
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author = "Tonneau, Manuel and |
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Adjodah, Dhaval and |
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Palotti, Joao and |
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Grinberg, Nir and |
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Fraiberger, Samuel", |
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booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
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month = may, |
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year = "2022", |
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address = "Dublin, Ireland", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.acl-long.453", |
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doi = "10.18653/v1/2022.acl-long.453", |
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pages = "6564--6587", |
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} |
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``` |
