README.md

---
license: gpl-3.0
language:
- es
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- classification
- pytorch
- language varieties
---
## Model Description

<!-- Provide a longer summary of what this model is. -->

- **Model type:** Multi-class classifier on top of a transformer
- **Language(s) (NLP):** Spanish varieties: Argentinian (ar), Chilean (cl), Mexican (mx), Spanish (es), and the rest (mix)
- **License:** GPL-3.0
- **Finetuned from:** XLM-RoBERTa large
- **Preprocessing and tokenisation:** the same as XLM-RoBERTa 

We provide models for a 3-class (es, mx, mix), a 4-class (cl, es, mx, mix) and a 5-class problem (ar, cl, es, mx, mix). For each case, models with 3 different seeds and the versions with one and two splits of the training documents are included. 
See the documentation of [docTransformer](https://github.com/cristinae/docTransformer) for more detailed information.


## Model Sources

- **Repository:** https://github.com/CEREAL-es/CEREAL
- **Paper:** [Elote, Choclo and Mazorca: on the Varieties of Spanish](https://aclanthology.org/2024.naacl-long.204.pdf) (NAACL 2024)
- **Data:** Find the corpora at Zenodo [https://zenodo.org/records/11390829]

## Use

Use the CEREAL classification models with [docTransformer](https://github.com/cristinae/docTransformer).


## Example Usage

Use these models for evaluation, classification or explanation using integrated gradients:

### Slurm 

#### Evaluation (gold label available)

```srun --ntasks 1 --gpus-per-task 1 python -u docClassifier.py --task evaluation -f trainedModel -o C4_cereal2splits_seed1.bin -b2 --sentence_batch_size 2 --split_documents True --test_dataset data/multivariant3all.test --plotConfusionFileName modelSplit2Seed3test.png```

#### Classification (gold label unavailable)

```srun --ntasks 1 --gpus-per-task 1 python -u docClassifier.py --task classification -f trainedModel -o C4_cereal2splits_seed1.bin -b1 --sentence_batch_size 2 --split_documents True --test_dataset ../es/es_meta_part_1.jsonl.unk```

#### Explanation

```srun --ntasks 1 --gpus-per-task 1 python -u docClassifier.py --task explanation -t data/testExample.mx -f trainedModel -o C4_cereal1split_seed1.bin -b1 --split_documents False  --xai_threshold_percentile 90```


## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@inproceedings{espana-bonet-barron-cedeno-2024-elote,
    title = "Elote, Choclo and Mazorca: on the Varieties of {S}panish",
    author = "Espa{\~n}a-Bonet, Cristina  and
      Barr{\'o}n-Cede{\~n}o, Alberto",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-long.204",
    pages = "3689--3711"
}
```

**APA:**

España-Bonet, Cristina and Barrón-Cedeño, Alberto. (2024, June). Elote, Choclo and Mazorca: on the Varieties of Spanish. In Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics: NAACL 2024 (pp. 3689-3711).