
# 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 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://github.com/manueltonneau/twitter-unemployment).
Details on the performance can be found in our [ACL 2022 paper](https://arxiv.org/abs/2203.09178).

## 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",
}
```
{"language": "en", "widget": [{"text": "Still unemployed..."}]}
transformers pytorch bert text-classification en arxiv:2203.09178 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us 


# 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 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.
Details on the performance can be found in our ACL 2022 paper.

If you find this model useful, please cite our paper:


# 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 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.
Details on the performance can be found in our ACL 2022 paper.

If you find this model useful, please cite our paper:
TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us 
 # 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 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.
Details on the performance can be found in our ACL 2022 paper.

If you find this model useful, please cite our paper:
passage: TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us 
# 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 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.
Details on the performance can be found in our ACL 2022 paper.

If you find this model useful, please cite our paper: