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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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- [Training](#training)
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- [Training
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- [Training
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- [Evaluation](#evaluation)
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- [Variable and
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- [Evaluation
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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
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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
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Here is how to use this model:
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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
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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
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This model was finetuned maximizing F1 score.
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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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##
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[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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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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```
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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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## Disclaimer
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<details>
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<summary>Click to expand</summary>
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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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## Table of Contents
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<details>
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<summary>Click to expand</summary>
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- [Model description](#model-description)
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- [Intended uses and limitations](#intended-use)
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- [How to use](#how-to-use)
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- [Limitations and bias](#limitations-and-bias)
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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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- [Additional information](#additional-information)
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- [Author](#author)
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- [Contact information](#contact-information)
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- [Copyright](#copyright)
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- [Licensing information](#licensing-information)
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- [Funding](#funding)
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- [Citing information](#citing-information)
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- [Disclaimer](#disclaimer)
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</details>
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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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pos_results = nlp(example)
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pprint(pos_results)
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```
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## Limitations and bias
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At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
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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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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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## Additional information
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### Author
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Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
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### Contact information
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For further information, send an email to aina@bsc.es
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### Copyright
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Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center
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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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### 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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### 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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}
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```
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### Disclaimer
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<details>
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<summary>Click to expand</summary>
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