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--- |
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language: |
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- ca |
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license: apache-2.0 |
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tags: |
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- "catalan" |
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- "part of speech tagging" |
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- "pos" |
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- "CaText" |
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- "Catalan Textual Corpus" |
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datasets: |
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- "universal_dependencies" |
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metrics: |
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- f1 |
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inference: |
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parameters: |
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aggregation_strategy: "first" |
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model-index: |
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- name: roberta-base-ca-v2-cased-pos |
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results: |
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- task: |
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type: token-classification |
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dataset: |
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type: universal_dependencies |
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name: Ancora-ca-POS |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.9896 |
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widget: |
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- text: "Em dic Lluïsa i visc a Santa Maria del Camí." |
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- text: "L'Aina, la Berta i la Norma són molt amigues." |
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- text: "El Martí llegeix el Cavall Fort." |
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--- |
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# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Part-of-speech-tagging (POS) |
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## Table of Contents |
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- [Model Description](#model-description) |
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- [Intended Uses and Limitations](#intended-uses-and-limitations) |
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- [How to Use](#how-to-use) |
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- [Training](#training) |
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- [Training Data](#training-data) |
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- [Training Procedure](#training-procedure) |
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- [Evaluation](#evaluation) |
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- [Variable and Metrics](#variable-and-metrics) |
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- [Evaluation Results](#evaluation-results) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Funding](#funding) |
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- [Contributions](#contributions) |
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## Model description |
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The **roberta-base-ca-v2-cased-pos** is a Part-of-speech-tagging (POS) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details). |
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## Intended Uses and Limitations |
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**roberta-base-ca-v2-cased-pos** model can be used to Part-of-speech-tagging (POS) a text. The model is limited by its training dataset and may not generalize well for all use cases. |
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## How to Use |
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Here is how to use this model: |
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```python |
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from transformers import pipeline |
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from pprint import pprint |
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nlp = pipeline("token-classification", model="projecte-aina/roberta-base-ca-v2-cased-pos") |
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example = "Em dic Lluïsa i visc a Santa Maria del Camí." |
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pos_results = nlp(example) |
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pprint(pos_results) |
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``` |
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## Training |
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### Training data |
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We used the POS dataset in Catalan from the [Universal Dependencies Treebank](https://huggingface.co/datasets/universal_dependencies) we refer to _Ancora-ca-pos_ for training and evaluation. |
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### Training Procedure |
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The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. |
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## Evaluation |
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### Variable and Metrics |
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This model was finetuned maximizing F1 score. |
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## Evaluation results |
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We evaluated the _roberta-base-ca-v2-cased-pos_ on the Ancora-ca-ner test set against standard multilingual and monolingual baselines: |
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| Model | Ancora-ca-pos (F1) | |
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| ------------|:-------------| |
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| roberta-base-ca-v2-cased-pos | **98.96** | |
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| roberta-base-ca-cased-pos | **98.96** | |
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| mBERT | 98.83 | |
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| XLM-RoBERTa | 98.89 | |
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For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club). |
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## Licensing Information |
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[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
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## Citation Information |
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If you use any of these resources (datasets or models) in your work, please cite our latest paper: |
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```bibtex |
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@inproceedings{armengol-estape-etal-2021-multilingual, |
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title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", |
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author = "Armengol-Estap{\'e}, Jordi and |
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Carrino, Casimiro Pio and |
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Rodriguez-Penagos, Carlos and |
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de Gibert Bonet, Ona and |
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Armentano-Oller, Carme and |
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Gonzalez-Agirre, Aitor and |
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Melero, Maite and |
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Villegas, Marta", |
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booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", |
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month = aug, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.findings-acl.437", |
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doi = "10.18653/v1/2021.findings-acl.437", |
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pages = "4933--4946", |
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} |
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``` |
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### Funding |
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This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). |
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## Contributions |
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[N/A] |
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