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--- |
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license: gpl-3.0 |
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language: |
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- es |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- classification |
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- pytorch |
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- language varieties |
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--- |
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## Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Model type:** Multi-class classifier on top of a transformer |
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- **Language(s) (NLP):** Spanish varieties: Argentinian (ar), Chilean (cl), Mexican (mx), Spanish (es), and the rest (mix) |
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- **License:** GPL-3.0 |
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- **Finetuned from:** XLM-RoBERTa large |
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- **Preprocessing and tokenisation:** the same as XLM-RoBERTa |
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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. |
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See the documentation of [docTransformer](https://github.com/cristinae/docTransformer) for more detailed information. |
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## Model Sources |
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- **Repository:** https://github.com/CEREAL-es/CEREAL |
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- **Paper:** [Elote, Choclo and Mazorca: on the Varieties of Spanish](https://aclanthology.org/2024.naacl-long.204.pdf) (NAACL 2024) |
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- **Data:** Find the corpora at Zenodo [https://zenodo.org/records/11390829] |
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## Use |
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Use the CEREAL classification models with [docTransformer](https://github.com/cristinae/docTransformer). |
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## Example Usage |
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Use these models for evaluation, classification or explanation using integrated gradients: |
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### Slurm |
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#### Evaluation (gold label available) |
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```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``` |
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#### Classification (gold label unavailable) |
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```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``` |
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#### Explanation |
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```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``` |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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``` |
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@inproceedings{espana-bonet-barron-cedeno-2024-elote, |
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title = "Elote, Choclo and Mazorca: on the Varieties of {S}panish", |
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author = "Espa{\~n}a-Bonet, Cristina and |
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Barr{\'o}n-Cede{\~n}o, Alberto", |
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editor = "Duh, Kevin and |
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Gomez, Helena and |
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Bethard, Steven", |
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booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)", |
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month = jun, |
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year = "2024", |
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address = "Mexico City, Mexico", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.naacl-long.204", |
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pages = "3689--3711" |
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} |
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``` |
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**APA:** |
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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). |
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