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 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) used in the original 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:
Part-of-Speech Tagging (POS)
Catalan-Ancora: from the Universal Dependencies treebank of the well-known Ancora corpus
Named Entity Recognition (NER)
AnCora Catalan 2.0.0: extracted named entities from the original Ancora version, filtering out some unconventional ones, like book titles, and transcribed them into a standard CONLL-IOB format
Text Classification (TC)
TeCla: consisting of 137k news pieces from the Catalan News Agency (ACN) corpus
Semantic Textual Similarity (STS)
Catalan semantic textual similarity: consisting of more than 3000 sentence pairs, annotated with the semantic similarity between them, scraped from the Catalan Textual Corpus
Question Answering (QA):
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.
XQuAD: 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.