Question Answering
Transformers
PyTorch
Catalan
roberta
catalan
qa
Eval Results
Inference Endpoints
File size: 8,668 Bytes
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---
language:

- ca

license: apache-2.0

tags:

- "catalan"

- "qa"

datasets:

- "projecte-aina/catalanqa"

- "projecte-aina/xquad-ca"

model-index:
- name: roberta-base-ca-v2-cased-qa
  results:
  - task: 
      type: question-answering 
    dataset:
      type: projecte-aina/catalanqa
      name: CatalanQA
    metrics:
      - name: F1
        type: f1
        value: 0.8950
  - task: 
      type: question-answering 
    dataset:
      type: projecte-aina/xquad-ca
      name: XQuAD-Ca
    metrics:
      - name: F1
        type: f1
        value: 0.7364

metrics:

- "f1"

- "exact match"

widget:

- text: "Quan va començar el Super3?"
  context: "El Super3 o Club Super3 és un univers infantil català creat a partir d'un programa emès per Televisió de Catalunya des del 1991. Està format per un canal de televisió, la revista Súpers!, la Festa dels Súpers i un club que té un milió i mig de socis."
  
- text: "Quants eren els germans Marx?"
  context: "Els germans Marx van ser un grup de còmics dels Estats Units que originàriament estava compost per cinc germans (entre parèntesis els noms artístics): Leonard (Chico), Adolph (Harpo), Julius (Groucho), Milton (Gummo) i Herbert (Zeppo)."
  
- text: "On van ser els Jocs Olímpics de 1992?"
  context: "Els Jocs Olímpics d'estiu de 1992, oficialment Jocs Olímpics de la XXV Olimpíada, es van celebrar a la ciutat de Barcelona entre els dies 25 de juliol i 9 d'agost de 1992. Hi participaren 9.356 atletes (6.652 homes i 2.704 dones) de 169 comitès nacionals, que competiren en 32 esports i 286 especialitats."
  
- text: "Qui va dissenyar la Sagrada Família?"
  context: "El Temple Expiatori de la Sagrada Família, conegut habitualment com la Sagrada Família, és una basílica catòlica situada a la ciutat de Barcelona. És un dels exemples més coneguts del modernisme català i un edifici únic al món, que ha esdevingut tot un símbol de la ciutat. Obra inacabada de l'arquitecte català Antoni Gaudí, és al barri de la Sagrada Família, al districte de l'Eixample de la ciutat."
  
- text: "Quin és el tercer volcà més gran de la Terra?"
  context: "El Teide (o Pic del Teide) és un estratovolcà i muntanya de Tenerife, Illes Canàries (28.27 N, 16.6 O). Amb una altitud de 3718 m sobre el nivell del mar i amb aproximadament uns 7000 m sobre el llit marí adjacent, és la muntanya més alta d'Espanya, la muntanya més alta de totes les illes atlàntiques i el tercer volcà més gran de la Terra."


---

# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Question Answering.

## Table of Contents
<details>
<summary>Click to expand</summary>


- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
  - [Training data](#training-data)
  - [Training procedure](#training-procedure)
- [Evaluation](#evaluation)
   - [Variable and metrics](#variable-and-metrics)
   - [Evaluation results](#evaluation-results)
- [Additional information](#additional-information)
  - [Author](#author)
  - [Contact information](#contact-information)
  - [Copyright](#copyright)
  - [Licensing information](#licensing-information)
  - [Funding](#funding)
  - [Citing information](#citing-information)
  - [Disclaimer](#disclaimer)
</details>

## Model description
The **roberta-base-ca-v2-cased-qa** is a Question Answering (QA) 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-qa** model can be used for extractive question answering. 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

nlp = pipeline("question-answering", model="projecte-aina/roberta-base-ca-v2-cased-qa")
text = "Quan va començar el Super3?"
context = "El Super3 o Club Super3 és un univers infantil català creat a partir d'un programa emès per Televisió de Catalunya des del 1991. Està format per un canal de televisió, la revista Súpers!, la Festa dels Súpers i un club que té un milió i mig de socis."
  
qa_results = nlp(text, context)
print(qa_results)
```

## Limitations and bias
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.

## Training

### Training data
We used the QA dataset in Catalan called [CatalanQA](https://huggingface.co./datasets/projecte-aina/catalanqa) for training and evaluation, and the [XQuAD-ca](https://huggingface.co./datasets/projecte-aina/xquad-ca) test set for 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-qa_ on the CatalanQA and XQuAD-ca test sets against standard multilingual and monolingual baselines:


| Model        | CatalanQA (F1/EM)  | XQuAD-Ca (F1/EM) | 
| ------------|:-------------:| -----:|
| roberta-base-ca-v2-cased-qa | **89.50**/76.63 | **73.64/55.42** |
| roberta-base-ca-cased-qa | 89.17/**77.14** | 69.20/51.47  |
| mBERT       | 86.90/74.19 | 68.79/50.80 |
| XLM-RoBERTa | 88.17/75.93 | 72.55/54.16 |

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

## Additional information

### Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])

### Contact information
For further information, send an email to [email protected]

### Copyright
Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center 

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

### 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).

## 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",
}
```
## Disclaimer

<details>
<summary>Click to expand</summary>

The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.