# POS Tagging model for Spanish/English
## robertuito-pos

Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)


Model trained with the Spanish/English split of the [LinCE NER corpus](https://ritual.uh.edu/lince/), a code-switched benchmark . Base model is [RoBERTuito](https://github.com/pysentimiento/robertuito), a RoBERTa model trained in Spanish tweets.

## Usage

If you want to use this model, we suggest you use it directly from the `pysentimiento` library as it is not working properly with the pipeline due to tokenization issues

```python
from pysentimiento import create_analyzer

pos_analyzer = create_analyzer("pos", lang="es")

pos_analyzer.predict("Quiero que esto funcione correctamente! @perezjotaeme")
 
 
>[{'type': 'PROPN', 'text': 'Quiero', 'start': 0, 'end': 6},
> {'type': 'SCONJ', 'text': 'que', 'start': 7, 'end': 10},
> {'type': 'PRON', 'text': 'esto', 'start': 11, 'end': 15},
> {'type': 'VERB', 'text': 'funcione', 'start': 16, 'end': 24},
> {'type': 'ADV', 'text': 'correctamente', 'start': 25, 'end': 38},
> {'type': 'PUNCT', 'text': '!', 'start': 38, 'end': 39},
> {'type': 'NOUN', 'text': '@perezjotaeme', 'start': 40, 'end': 53}]
```


## Results

Results are taken from the LinCE leaderboard

| Model                  | Sentiment       | NER                | POS     |
|:-----------------------|:----------------|:-------------------|:--------|
| RoBERTuito             | **60.6**        | 68.5               | 97.2    |
| XLM Large              | --              | **69.5**           | **97.2**   |
| XLM Base               | --              | 64.9               | 97.0    |
| C2S mBERT              | 59.1            | 64.6               | 96.9    |
| mBERT                  | 56.4            | 64.0               | 97.1    |
| BERT                   | 58.4            | 61.1               | 96.9    |
| BETO                   | 56.5            | --                 | --      |



## Citation

If you use this model in your research, please cite pysentimiento, RoBERTuito and LinCE papers:

```
@misc{perez2021pysentimiento,
      title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
      author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
      year={2021},
      eprint={2106.09462},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@inproceedings{ortega2019overview,
  title={Overview of the task on irony detection in Spanish variants},
  author={Ortega-Bueno, Reynier and Rangel, Francisco and Hern{\'a}ndez Far{\i}as, D and Rosso, Paolo and Montes-y-G{\'o}mez, Manuel and Medina Pagola, Jos{\'e} E},
  booktitle={Proceedings of the Iberian languages evaluation forum (IberLEF 2019), co-located with 34th conference of the Spanish Society for natural language processing (SEPLN 2019). CEUR-WS. org},
  volume={2421},
  pages={229--256},
  year={2019}
}

@inproceedings{aguilar2020lince,
  title={LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation},
  author={Aguilar, Gustavo and Kar, Sudipta and Solorio, Thamar},
  booktitle={Proceedings of the 12th Language Resources and Evaluation Conference},
  pages={1803--1813},
  year={2020}
}
```
{"language": ["es"], "tags": ["twitter", "pos-tagging"]}
transformers pytorch safetensors roberta token-classification twitter pos-tagging es arxiv:2106.09462 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #twitter #pos-tagging #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #region-us 

POS Tagging model for Spanish/English
=====================================


robertuito-pos
--------------


Repository: URL


Model trained with the Spanish/English split of the LinCE NER corpus, a code-switched benchmark . Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets.


Usage
-----


If you want to use this model, we suggest you use it directly from the 'pysentimiento' library as it is not working properly with the pipeline due to tokenization issues


Results
-------


Results are taken from the LinCE leaderboard



If you use this model in your research, please cite pysentimiento, RoBERTuito and LinCE papers:



TAGS
#transformers #pytorch #safetensors #roberta #token-classification #twitter #pos-tagging #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #safetensors #roberta #token-classification #twitter #pos-tagging #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #region-us 
