README.md

---
language:

- ca

license: apache-2.0

tags:

- "catalan"

- "masked-lm"

- "RoBERTa-large-ca"

- "CaText"

- "Catalan Textual Corpus"

widget:
- text: "El Català és una llengua molt <mask>."
- text: "Salvador Dalí va viure a <mask>."
- text: "La Costa Brava té les millors <mask> d'Espanya."
- text: "El cacaolat és un batut de <mask>."
- text: "<mask> és la capital de la Garrotxa."
- text: "Vaig al <mask> a buscar bolets."
- text: "Antoni Gaudí vas ser un <mask> molt important per la ciutat."
- text: "Catalunya és una referència en <mask> a nivell europeu."

---

# Catalan BERTa (roberta-large-ca) large model

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

- [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)
   - [CLUB Benchmark](#club-benchmark)
   - [Evaluation Results](#evaluation-results)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Funding](#funding)
- [Contributions](#contributions)

</details>

## Model description

The **roberta-large-ca** is a transformer-based masked language model for the Catalan language. 
It is based on the [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) large model 
and has been trained on a medium-size corpus collected from publicly available corpora and crawlers.

## Intended Uses and Limitations

**roberta-large-ca** model is ready-to-use only for masked language modeling to perform the Fill Mask task (try the inference API or read the next section).
However, it is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification, or Named Entity Recognition.

## How to Use

Here is how to use this model:

```python
from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer, FillMaskPipeline
from pprint import pprint
tokenizer_hf = AutoTokenizer.from_pretrained('projecte-aina/roberta-large-ca')
model = AutoModelForMaskedLM.from_pretrained('projecte-aina/roberta-large-ca')
model.eval()
pipeline = FillMaskPipeline(model, tokenizer_hf)
text = f"Em dic <mask>."
res_hf = pipeline(text)
pprint([r['token_str'] for r in res_hf])
```

## Training

### Training data

The training corpus consists of several corpora gathered from web crawling and public corpora.


| Corpus                  | Size in GB |
|-------------------------|------------|
| Catalan Crawling        | 13.00      |
| Wikipedia               | 1.10       |
| DOGC                    | 0.78       |
| Catalan Open Subtitles  | 0.02       |
| Catalan Oscar           | 4.00       |
| CaWaC                   | 3.60       |
| Cat. General Crawling   | 2.50       |
| Cat. Goverment Crawling | 0.24       |
| ACN                     | 0.42       |
| Padicat                 | 0.63       |
| RacoCatalá              | 8.10       |
| Nació Digital           | 0.42       |
| Vilaweb                 | 0.06       |
| Tweets                  | 0.02       |

### Training Procedure

The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2)
used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens. 
The RoBERTa-large pretraining consists of a masked language model training that follows the approach employed for the RoBERTa large model
with the same hyperparameters as in the original work.
The training lasted a total of 96 hours with 32 NVIDIA V100 GPUs of 16GB DDRAM.


## Evaluation

### CLUB Benchmark

The BERTa-large model has been fine-tuned on the downstream tasks of the Catalan Language Understanding Evaluation benchmark (CLUB),
that has been created along with the model.

It contains the following tasks and their related datasets:

 1. Named Entity Recognition (NER)

    
    **[NER (AnCora)](https://zenodo.org/record/4762031#.YKaFjqGxWUk)**: extracted named entities from the original [Ancora](https://doi.org/10.5281/zenodo.4762030) version,
    filtering out some unconventional ones, like book titles, and transcribed them into a standard CONLL-IOB format


 2. Part-of-Speech Tagging (POS)
    
    **[POS (AnCora)](https://zenodo.org/record/4762031#.YKaFjqGxWUk)**: from the [Universal Dependencies treebank](https://github.com/UniversalDependencies/UD_Catalan-AnCora) of the well-known Ancora corpus.

 3. Text Classification (TC)
     
    **[TeCla](https://huggingface.co/datasets/projecte-aina/tecla)**: consisting of 137k news pieces from the Catalan News Agency ([ACN](https://www.acn.cat/)) corpus, with 30 labels.

 4. Textual Entailment (TE)
     
    **[TE-ca](https://huggingface.co/datasets/projecte-aina/teca)**: consisting of 21,163 pairs of premises and hypotheses, annotated according to the inference relation they have (implication, contradiction, or neutral), extracted from the [Catalan Textual Corpus](https://huggingface.co/datasets/projecte-aina/catalan_textual_corpus).

 5. Semantic Textual Similarity (STS)
    
    **[STS-ca](https://huggingface.co/datasets/projecte-aina/sts-ca)**: consisting of more than 3000 sentence pairs, annotated with the semantic similarity between them, scraped from the [Catalan Textual Corpus](https://huggingface.co/datasets/projecte-aina/catalan_textual_corpus).

 6. Question Answering (QA):
    
    **[VilaQuAD](https://huggingface.co/datasets/projecte-aina/vilaquad)**: contains 6,282 pairs of questions and answers, outsourced from 2095 Catalan language articles from VilaWeb newswire text.
    
    **[ViquiQuAD](https://huggingface.co/datasets/projecte-aina/viquiquad)**: consisting of more than 15,000 questions outsourced from Catalan Wikipedia randomly chosen from a set of 596 articles that were originally written in Catalan.
   
    **[CatalanQA](https://huggingface.co/datasets/projecte-aina/catalanqa)**: an aggregation of 2 previous datasets (VilaQuAD and ViquiQuAD), 21,427 pairs of Q/A balanced by type of question, containing one question and one answer per context, although the contexts can repeat multiple times.
    
    **[XQuAD-ca](https://huggingface.co/datasets/projecte-aina/xquad-ca)**: the Catalan translation of XQuAD, a multilingual collection of manual translations of 1,190 question-answer pairs from English Wikipedia used only as a _test set_.
    
Here are the train/dev/test splits of the datasets:

| Task (Dataset) | Total | Train | Dev  | Test |
|:--|:--|:--|:--|:--|
| NER (Ancora)  |13,581 | 10,628 | 1,427 | 1,526 |
| POS (Ancora)| 16,678 | 13,123 | 1,709 | 1,846 |
| STS (STS-ca)         | 3,073 | 2,073 | 500 | 500 |
| TC (TeCla) |  137,775 | 110,203 | 13,786 |  13,786|
| TE (TE-ca) |  21,163 | 16,930 | 2,116 | 2,117
| QA (VilaQuAD) | 6,282  | 3,882  | 1,200  | 1,200 |
| QA (ViquiQuAD) | 14,239  | 11,255  | 1,492  | 1,429 |
| QA (CatalanQA) | 21,427  | 17,135  | 2,157  | 2,135 |

### Evaluation Results

| Task        | NER (F1)      | POS (F1)   | STS-ca (Comb)   | TeCla (Acc.) | TEca (Acc.) | VilaQuAD (F1/EM)| ViquiQuAD (F1/EM) | CatalanQA (F1/EM) | XQuAD-ca <sup>1</sup> (F1/EM) | 
| ------------|:-------------:| -----:|:------|:------|:-------|:------|:----|:----|:----|
| RoBERTa-large-ca        | **89.82** | **99.02** | **83.41** | **75.46** | **83.61** | **89.34**/75.50 | **89.20**/75.77 | **90.72/79.06** | **73.79**/55.34 |
| RoBERTa-base-ca-v2      | 89.29 | 98.96 | 79.07 | 74.26 | 83.14 | 87.74/72.58 | 88.72/**75.91** | 89.50/76.63 | 73.64/**55.42** |
| BERTa                   | 89.76 | 98.96 | 80.19 | 73.65 | 79.26 | 85.93/70.58 | 87.12/73.11 | 89.17/77.14 | 69.20/51.47 |
| mBERT                   | 86.87 | 98.83 | 74.26 | 69.90 | 74.63 | 82.78/67.33 | 86.89/73.53 | 86.90/74.19 | 68.79/50.80 |
| XLM-RoBERTa             | 86.31 | 98.89 | 61.61 | 70.14 | 33.30 | 86.29/71.83 | 86.88/73.11 | 88.17/75.93 | 72.55/54.16 |

<sup>1</sup> : Trained on CatalanQA, tested on XQuAD-ca.

## 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]