# RigoBERTa: A State-of-the-Art Language Model For Spanish

arXiv: https://arxiv.org/abs/2205.10233 · HTML: https://mlsanity.com/paper/2205.10233/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 47 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [PAWS: Paraphrase Adversaries from Word Scrambling](https://mlsanity.com/paper/1904.01130/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://mlsanity.com/paper/1908.11828/) (2019)
- [FlauBERT: Unsupervised Language Model Pre-training for French](https://mlsanity.com/paper/1912.05372/) (2019)
- [German's Next Language Model](https://mlsanity.com/paper/2010.10906/) (2020)
- [A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages](https://mlsanity.com/paper/2006.06202/) (2020)
- [RobBERT: a Dutch RoBERTa-based Language Model](https://mlsanity.com/paper/2001.06286/) (2020)
- [Macro F1 and Macro F1](https://mlsanity.com/paper/1911.03347/) (2019)
- [GottBERT: a pure German Language Model](https://mlsanity.com/paper/2012.02110/) (2020)
- [A Corpus for Multilingual Document Classification in Eight Languages](https://mlsanity.com/paper/1805.09821/) (2018)
- [Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering](https://mlsanity.com/paper/1912.05200/) (2019)
- [Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario](https://mlsanity.com/paper/2109.03570/) (2021)
- [Correction of "A Comparative Study to Benchmark Cross-project Defect Prediction Approaches"](https://mlsanity.com/paper/1707.09281/) (2017)
- [Overview of CAPITEL Shared Tasks at IberLEF 2020: Named Entity Recognition and Universal Dependencies Parsing](https://mlsanity.com/paper/2011.05932/) (2020)

## Built on by (most cited first, 5 of 5)

- [Leveraging Large Language Models for Topic Classification in the Domain of Public Affairs](https://mlsanity.com/paper/2306.02864/) (2023)
- [An evaluation of LLM code generation capabilities through graded exercises](https://mlsanity.com/paper/2410.16292/) (2024)
- [Sequence-to-Sequence Spanish Pre-trained Language Models](https://mlsanity.com/paper/2309.11259/) (2023)
- [A Survey of Spanish Clinical Language Models](https://mlsanity.com/paper/2308.02199/) (2023)
- [MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation](https://mlsanity.com/paper/2602.21379/) (2026)

## Abstract (arXiv)

This paper presents RigoBERTa, a State-of-the-Art Language Model for Spanish. RigoBERTa is trained over a well-curated corpus formed up from different subcorpora with key features. It follows the DeBERTa architecture, which has several advantages over other architectures of similar size as BERT or RoBERTa. RigoBERTa performance is assessed over 13 NLU tasks in comparison with other available Spanish language models, namely, MarIA, BERTIN and BETO. RigoBERTa outperformed the three models in 10 out of the 13 tasks, achieving new "State-of-the-Art" results.