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---
language: "ca"
tags:
- masked-lm
- RoBERTa-base-ca-v2
- catalan
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."
license: apache-2.0
---
## Model description
RoBERTa-ca-v2 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) base model
and has been trained on a medium-size corpus collected from publicly available corpora and crawlers.
## Tokenization and pretraining
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-ca-v2 pretraining consists of a masked language model training that follows the approach employed for the RoBERTa base model
with the same hyperparameters as in the original work.
The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM.
## Training corpora and preprocessing
The training corpus consists of several corpora gathered from web crawling and public corpora.
| Corpus | Size in GB |
|-------------------------|------------|
| BNE-ca | 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 |
## Evaluation
### CLUB benchmark
The BERTa 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. Part-of-Speech Tagging (POS)
Catalan-Ancora: from the [Universal Dependencies treebank](https://github.com/UniversalDependencies/UD_Catalan-AnCora) of the well-known Ancora corpus
2. Named Entity Recognition (NER)
**[AnCora Catalan 2.0.0](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
3. Text Classification (TC)
**[TeCla](https://doi.org/10.5281/zenodo.4627197)**: consisting of 137k news pieces from the Catalan News Agency ([ACN](https://www.acn.cat/)) corpus
4. Semantic Textual Similarity (STS)
**[Catalan semantic textual similarity](https://doi.org/10.5281/zenodo.4529183)**: consisting of more than 3000 sentence pairs, annotated with the semantic similarity between them,
scraped from the [Catalan Textual Corpus](https://doi.org/10.5281/zenodo.4519349)
5. Question Answering (QA):
**[ViquiQuAD](https://doi.org/10.5281/zenodo.4562344)**: 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.
**[XQuAD](https://doi.org/10.5281/zenodo.4526223)**: 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 | 3,073 | 2,073 | 500 | 500 |
| TC (TeCla) | 137,775 | 110,203 | 13,786 | 13,786|
| QA (ViquiQuAD) | 14,239 | 11,255 | 1,492 | 1,429 |
### Results
| Task | NER (F1) | POS (F1) | STS (Pearson) | TC (accuracy) | QA (ViquiQuAD) (F1/EM) | QA (XQuAD) (F1/EM) |
| ------------|:-------------:| -----:|:------|:-------|:------|:----|
| RoBERTa-base-ca-v2 | **89.84** | **99.07** | **79.98** | **83.41** | **88.04/74.65** | **71.50/53.41** |
| BERTa | 88.13 | 98.97 | 79.73 | 74.16 | 86.97/72.29 | 68.89/48.87 |
| mBERT | 86.38 | 98.82 | 76.34 | 70.56 | 86.97/72.22 | 67.15/46.51 |
| XLM-RoBERTa | 87.66 | 98.89 | 75.40 | 71.68 | 85.50/70.47 | 67.10/46.42 |
| WikiBERT-ca | 77.66 | 97.60 | 77.18 | 73.22 | 85.45/70.75 | 65.21/36.60 |
## Intended uses & limitations
The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section)
However, the is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification or Named Entity Recognition.
## Funding
This work was funded by the Generalitat de Catalunya within the framework of the AINA language technologies plan.
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