
# 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 (bsc-temu@bsc.es)

### Contact information
For further information, send an email to aina@bsc.es

### 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.

{"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "qa"], "datasets": ["projecte-aina/catalanqa", "projecte-aina/xquad-ca"], "metrics": ["f1", "exact match"], "widget": [{"text": "Quan va comen\u00e7ar el Super3?", "context": "El Super3 o Club Super3 \u00e9s un univers infantil catal\u00e0 creat a partir d'un programa em\u00e8s per Televisi\u00f3 de Catalunya des del 1991. Est\u00e0 format per un canal de televisi\u00f3, la revista S\u00fapers!, la Festa dels S\u00fapers i un club que t\u00e9 un mili\u00f3 i mig de socis."}, {"text": "Quants eren els germans Marx?", "context": "Els germans Marx van ser un grup de c\u00f2mics dels Estats Units que origin\u00e0riament estava compost per cinc germans (entre par\u00e8ntesis els noms art\u00edstics): Leonard (Chico), Adolph (Harpo), Julius (Groucho), Milton (Gummo) i Herbert (Zeppo)."}, {"text": "On van ser els Jocs Ol\u00edmpics de 1992?", "context": "Els Jocs Ol\u00edmpics d'estiu de 1992, oficialment Jocs Ol\u00edmpics de la XXV Olimp\u00edada, 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 comit\u00e8s nacionals, que competiren en 32 esports i 286 especialitats."}, {"text": "Qui va dissenyar la Sagrada Fam\u00edlia?", "context": "El Temple Expiatori de la Sagrada Fam\u00edlia, conegut habitualment com la Sagrada Fam\u00edlia, \u00e9s una bas\u00edlica cat\u00f2lica situada a la ciutat de Barcelona. \u00c9s un dels exemples m\u00e9s coneguts del modernisme catal\u00e0 i un edifici \u00fanic al m\u00f3n, que ha esdevingut tot un s\u00edmbol de la ciutat. Obra inacabada de l'arquitecte catal\u00e0 Antoni Gaud\u00ed, \u00e9s al barri de la Sagrada Fam\u00edlia, al districte de l'Eixample de la ciutat."}, {"text": "Quin \u00e9s el tercer volc\u00e0 m\u00e9s gran de la Terra?", "context": "El Teide (o Pic del Teide) \u00e9s un estratovolc\u00e0 i muntanya de Tenerife, Illes Can\u00e0ries (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 mar\u00ed adjacent, \u00e9s la muntanya m\u00e9s alta d'Espanya, la muntanya m\u00e9s alta de totes les illes atl\u00e0ntiques i el tercer volc\u00e0 m\u00e9s gran de la Terra."}], "model-index": [{"name": "roberta-base-ca-v2-cased-qa", "results": [{"task": {"type": "question-answering"}, "dataset": {"name": "CatalanQA", "type": "projecte-aina/catalanqa"}, "metrics": [{"type": "f1", "value": 0.895, "name": "F1"}]}, {"task": {"type": "question-answering"}, "dataset": {"name": "XQuAD-Ca", "type": "projecte-aina/xquad-ca"}, "metrics": [{"type": "f1", "value": 0.7364, "name": "F1"}]}]}]}
transformers pytorch roberta question-answering catalan qa ca dataset:projecte-aina/catalanqa dataset:projecte-aina/xquad-ca arxiv:1907.11692 license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #question-answering #catalan #qa #ca #dataset-projecte-aina/catalanqa #dataset-projecte-aina/xquad-ca #arxiv-1907.11692 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

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


Table of Contents
-----------------



Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
	+ Training data
	+ Training procedure
* Evaluation
	+ Variable and metrics
	+ Evaluation results
* Additional information
	+ Author
	+ Contact information
	+ Copyright
	+ Licensing information
	+ Funding
	+ Citing information
	+ Disclaimer



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 model, a RoBERTa 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:


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 for training and evaluation, and the 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:



For more details, check the fine-tuning and evaluation scripts in the official GitHub repository.


Additional information
----------------------


### Author


Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL)


### Contact information


For further information, send an email to aina@URL


### Copyright


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


### Licensing information


Apache License, Version 2.0


### Funding


This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.


If you use any of these resources (datasets or models) in your work, please cite our latest paper:


Disclaimer
----------



Click to expand
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.



### Training data


We used the QA dataset in Catalan called CatalanQA for training and evaluation, and the 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:



For more details, check the fine-tuning and evaluation scripts in the official GitHub repository.


Additional information
---------------------- ### Author


Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL) ### Contact information


For further information, send an email to aina@URL ### Copyright


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


Apache License, Version 2.0 ### Funding


This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.


If you use any of these resources (datasets or models) in your work, please cite our latest paper:


Disclaimer
----------



Click to expand
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.
TAGS
#transformers #pytorch #roberta #question-answering #catalan #qa #ca #dataset-projecte-aina/catalanqa #dataset-projecte-aina/xquad-ca #arxiv-1907.11692 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 ### Training data


We used the QA dataset in Catalan called CatalanQA for training and evaluation, and the 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:



For more details, check the fine-tuning and evaluation scripts in the official GitHub repository.


Additional information
---------------------- ### Author


Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL) ### Contact information


For further information, send an email to aina@URL ### Copyright


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


Apache License, Version 2.0 ### Funding


This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.


If you use any of these resources (datasets or models) in your work, please cite our latest paper:


Disclaimer
----------



Click to expand
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.
passage: TAGS
#transformers #pytorch #roberta #question-answering #catalan #qa #ca #dataset-projecte-aina/catalanqa #dataset-projecte-aina/xquad-ca #arxiv-1907.11692 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
### Training data


We used the QA dataset in Catalan called CatalanQA for training and evaluation, and the 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:



For more details, check the fine-tuning and evaluation scripts in the official GitHub repository.


Additional information
----------------------### Author


Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL)### Contact information


For further information, send an email to aina@URL### Copyright


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


Apache License, Version 2.0