
# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for TeCla-based Text Classification.

## 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)
    - [Tokenization](#tokenization)
    - [Hyperparameters](#hyperparameters)
- [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-tc** is a Text Classification (TC) 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).

The previous version of this model, which was trained on the old TeCla dataset (v1), can still be accessed through the "v1" tag.

## Intended uses and limitations

**roberta-base-ca-v2-cased-tc** model can be used to classify texts. 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("text-classification", model="projecte-aina/roberta-base-ca-v2-cased-tc")
example = "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya."

tc_results = nlp(example)
pprint(tc_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 TC dataset in Catalan called [TeCla](https://huggingface.co/datasets/projecte-aina/tecla) for training and evaluation. Although TeCla includes a coarse-grained ('label1') and a fine-grained categorization ('label2'), only the last one, with 53 classes, was used for the training.

### 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 (weighted).

## Evaluation results
We evaluated the _roberta-base-ca-v2-cased-tc_ on the TeCla test set against standard multilingual and monolingual baselines. The results for 'label1' categories were obtained through a mapping from the fine-grained category ('label2') to the corresponding coarse-grained one ('label1').

| Model        | TeCla - label1 (Accuracy) |  TeCla - label2 (Accuracy) |
| ------------|:-------------|:-------------|
| roberta-base-ca-v2 | 96.31 | 80.34 |
| roberta-large-ca-v2 | **96.51** | **80.68** |
| mBERT       |  95.72 | 78.47 |
| XLM-RoBERTa | 95.66 | 78.01 |

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"], "tags": ["catalan", "text classification", "tecla", "CaText", "Catalan Textual Corpus"], "datasets": ["projecte-aina/tecla"], "metrics": ["accuracy"], "widget": [{"text": "Els Pets presenten el seu nou treball al Palau Sant Jordi."}, {"text": "Els barcelonins incrementen un 23% l\u2019\u00fas del cotxe des de l\u2019inici de la pand\u00e8mia."}, {"text": "Retards a quatre l\u00ednies de Rodalies per una avaria entre Sants i pla\u00e7a de Catalunya."}, {"text": "Majors de 60 anys i sanitaris comen\u00e7aran a rebre la tercera dosi de la vacuna covid els propers dies."}, {"text": "Els cinemes Verdi estrenen Verdi Classics, un nou canal de televisi\u00f3."}], "model-index": [{"name": "roberta-base-ca-v2-cased-tc", "results": [{"task": {"type": "text-classification"}, "dataset": {"name": "TeCla", "type": "projecte-aina/tecla"}, "metrics": [{"type": "accuracy", "value": 0.8034, "name": "Accuracy"}]}]}]}
transformers pytorch roberta text-classification catalan text classification tecla CaText Catalan Textual Corpus ca dataset:projecte-aina/tecla arxiv:1907.11692 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #catalan #text classification #tecla #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/tecla #arxiv-1907.11692 #model-index #autotrain_compatible #endpoints_compatible #region-us 

Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for TeCla-based Text Classification.
====================================================================================


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



Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
	+ Training data
	+ Training procedure
		- Tokenization
		- Hyperparameters
* 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-tc is a Text Classification (TC) 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).


The previous version of this model, which was trained on the old TeCla dataset (v1), can still be accessed through the "v1" tag.


Intended uses and limitations
-----------------------------


roberta-base-ca-v2-cased-tc model can be used to classify texts. 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 TC dataset in Catalan called TeCla for training and evaluation. Although TeCla includes a coarse-grained ('label1') and a fine-grained categorization ('label2'), only the last one, with 53 classes, was used for the training.


### 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 (weighted).


Evaluation results
------------------


We evaluated the *roberta-base-ca-v2-cased-tc* on the TeCla test set against standard multilingual and monolingual baselines. The results for 'label1' categories were obtained through a mapping from the fine-grained category ('label2') to the corresponding coarse-grained one ('label1').



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 TC dataset in Catalan called TeCla for training and evaluation. Although TeCla includes a coarse-grained ('label1') and a fine-grained categorization ('label2'), only the last one, with 53 classes, was used for the training. ### 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 (weighted).


Evaluation results
------------------


We evaluated the *roberta-base-ca-v2-cased-tc* on the TeCla test set against standard multilingual and monolingual baselines. The results for 'label1' categories were obtained through a mapping from the fine-grained category ('label2') to the corresponding coarse-grained one ('label1').



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 #text-classification #catalan #text classification #tecla #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/tecla #arxiv-1907.11692 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training data


We used the TC dataset in Catalan called TeCla for training and evaluation. Although TeCla includes a coarse-grained ('label1') and a fine-grained categorization ('label2'), only the last one, with 53 classes, was used for the training. ### 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 (weighted).


Evaluation results
------------------


We evaluated the *roberta-base-ca-v2-cased-tc* on the TeCla test set against standard multilingual and monolingual baselines. The results for 'label1' categories were obtained through a mapping from the fine-grained category ('label2') to the corresponding coarse-grained one ('label1').



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 #text-classification #catalan #text classification #tecla #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/tecla #arxiv-1907.11692 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training data


We used the TC dataset in Catalan called TeCla for training and evaluation. Although TeCla includes a coarse-grained ('label1') and a fine-grained categorization ('label2'), only the last one, with 53 classes, was used for the training.### 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 (weighted).


Evaluation results
------------------


We evaluated the *roberta-base-ca-v2-cased-tc* on the TeCla test set against standard multilingual and monolingual baselines. The results for 'label1' categories were obtained through a mapping from the fine-grained category ('label2') to the corresponding coarse-grained one ('label1').



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: