Create README.md

README.md

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