pytorch roberta token-classification ca dataset:universal_dependencies arxiv:1907.11692 transformers catalan part of speech tagging pos CaText Catalan Textual Corpus license:apache-2.0 model-index autotrain_compatible
---

language:

- ca

license: apache-2.0

tags:

- "catalan"

- "part of speech tagging"

- "pos"

- "CaText"

- "Catalan Textual Corpus"

datasets:

- "universal_dependencies"

metrics:

- f1

inference:
  parameters:
    aggregation_strategy: "first"
    
model-index:
- name: roberta-base-ca-v2-cased-pos
  results:
  - task: 
      type: token-classification 
    dataset:
      type:   universal_dependencies
      name: Ancora-ca-POS
    metrics:
      - name: F1
        type: f1
        value: 0.9909

widget:

- text: "Em dic Lluïsa i visc a Santa Maria del Camí." 

- text: "L'Aina, la Berta i la Norma són molt amigues."

- text: "El Martí llegeix el Cavall Fort."

---

# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Part-of-speech-tagging (POS)

## Table of Contents
- [Model Description](#model-description)
- [Intended Uses and Limitations](#intended-uses-and-limitations)
- [How to Use](#how-to-use)
- [Training](#training)
  - [Training Data](#training-data)
  - [Training Procedure](#training-procedure)
- [Evaluation](#evaluation)
   - [Variable and Metrics](#variable-and-metrics)
   - [Evaluation Results](#evaluation-results)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Funding](#funding)
- [Contributions](#contributions)

## Model description

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).

## Intended Uses and Limitations

**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.

## How to Use

Here is how to use this model:

```python
from transformers import pipeline
from pprint import pprint

nlp = pipeline("token-classification", model="projecte-aina/roberta-base-ca-v2-cased-pos")
example = "Em dic Lluïsa i visc a Santa Maria del Camí."

pos_results = nlp(example)
pprint(pos_results)
```
## Training

### Training data
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.

### Training Procedure
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.

## Evaluation

### Variable and Metrics

This model was finetuned maximizing F1 score.

## Evaluation results
We evaluated the _roberta-base-ca-v2-cased-pos_ on the Ancora-ca-ner test set against standard multilingual and monolingual baselines:

| Model        | Ancora-ca-pos (F1)   | 
| ------------|:-------------|
| roberta-base-ca-v2-cased-pos |99.09 |
| roberta-base-ca-cased-pos | **99.10** |
| mBERT       | 98.98 |
| XLM-RoBERTa | 99.03 | 

For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).

## Licensing Information

[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)

## Citation Information  
If you use any of these resources (datasets or models) in your work, please cite our latest paper:
```bibtex
@inproceedings{armengol-estape-etal-2021-multilingual,
    title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
    author = "Armengol-Estap{\'e}, Jordi  and
      Carrino, Casimiro Pio  and
      Rodriguez-Penagos, Carlos  and
      de Gibert Bonet, Ona  and
      Armentano-Oller, Carme  and
      Gonzalez-Agirre, Aitor  and
      Melero, Maite  and
      Villegas, Marta",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.437",
    doi = "10.18653/v1/2021.findings-acl.437",
    pages = "4933--4946",
}
```

### Funding

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).



## Contributions

[N/A]
