# RoBERTuito: a pre-trained language model for social media text in Spanish arXiv: https://arxiv.org/abs/2111.09453 ยท HTML: https://mlsanity.com/paper/2111.09453/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Builds on 19 of its 38 references that are in the map. - First posted on arXiv 18 Nov 2021. ## Builds on (most cited first, 19 of 19) - [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) - [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) - [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019) - [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018) - [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018) - [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019) - [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016) - [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020) - [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020) - [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019) - [LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation](https://mlsanity.com/paper/2005.04322/) (2020) - [SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets](https://mlsanity.com/paper/2008.04277/) (2020) - [Are Multilingual Models Effective in Code-Switching?](https://mlsanity.com/paper/2103.13309/) (2021) - [What the [MASK]? Making Sense of Language-Specific BERT Models](https://mlsanity.com/paper/2003.02912/) (2020) - [Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality](https://mlsanity.com/paper/2010.12730/) (2020) - [Part of speech tagging for code switched data](https://mlsanity.com/paper/1909.13006/) (2019) ## Built on by (most cited first, 22 of 22) - [MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages](https://mlsanity.com/paper/2404.02037/) (2024) - [Multilingual and Explainable Text Detoxification with Parallel Corpora](https://mlsanity.com/paper/2412.11691/) (2024) - [ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing](https://mlsanity.com/paper/2310.11166/) (2023) - [Assessing the impact of contextual information in hate speech detection](https://mlsanity.com/paper/2210.00465/) (2022) - [CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages](https://mlsanity.com/paper/2507.18791/) (2025) - [Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages](https://mlsanity.com/paper/2210.11359/) (2022) - [Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats](https://mlsanity.com/paper/2211.00171/) (2022) - [ALBETO and DistilBETO: Lightweight Spanish Language Models](https://mlsanity.com/paper/2204.09145/) (2022) - [Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training](https://mlsanity.com/paper/2210.05287/) (2022) - [Measuring Sentiment Bias in Machine Translation](https://mlsanity.com/paper/2306.07152/) (2023) - [Common Ground, Diverse Roots: The Difficulty of Classifying Common Examples in Spanish Varieties](https://mlsanity.com/paper/2412.11750/) (2024) - [A Spanish dataset for Targeted Sentiment Analysis of political headlines](https://mlsanity.com/paper/2208.13947/) (2022) - [Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Data](https://mlsanity.com/paper/2306.03722/) (2023) - [Exploring Large Language Models for Hate Speech Detection in Rioplatense Spanish](https://mlsanity.com/paper/2410.12174/) (2024) - [RoBERTweet: A BERT Language Model for Romanian Tweets](https://mlsanity.com/paper/2306.06598/) (2023) - [A Cost-aware Study of Depression Language on Social Media using Topic and Affect Contextualization](https://mlsanity.com/paper/2306.17564/) (2023) - [Language Model Knowledge Distillation for Efficient Question Answering in Spanish](https://mlsanity.com/paper/2312.04193/) (2023) - [Identification of emotions on Twitter during the 2022 electoral process in Colombia](https://mlsanity.com/paper/2407.07258/) (2024) - [Experimenting with Affective Computing Models in Video Interviews with Spanish-speaking Older Adults](https://mlsanity.com/paper/2501.16870/) (2025) - [Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning](https://mlsanity.com/paper/2508.10040/) (2025) - [BTC-SAM: Leveraging LLMs for Generation of Bias Test Cases for Sentiment Analysis Models](https://mlsanity.com/paper/2509.24101/) (2025) - [Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts](https://mlsanity.com/paper/2607.06611/) (2026) ## Abstract (arXiv) Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for Natural Language Understanding tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks. However, for languages other than English such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model achieves top results for some English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and has also competitive performance against monolingual models in English tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.