---
language: 
  - es
library_name: pysentimiento

tags:
  - twitter
  - RoBERTa

---

# robertuito-base-cased

# RoBERTuito
## A pre-trained language model for social media text in Spanish

[**READ THE FULL PAPER**](https://arxiv.org/abs/2111.09453)
[Github Repository](https://github.com/pysentimiento/robertuito)
[![Test it in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1WcubR0kbqT289XupSnN5-STe7HafyKpf#scrollTo=SF-n4IdjnoYk)

*RoBERTuito* is a pre-trained language model for user-generated content in Spanish, trained following RoBERTa guidelines on 500 million tweets. *RoBERTuito* comes in 3 flavors: cased, uncased, and uncased+deaccented.

We tested *RoBERTuito* on a benchmark of tasks involving user-generated text in Spanish. It outperforms other pre-trained language models for this language such as *BETO*, *BERTin* and *RoBERTa-BNE*. The 4 tasks selected for evaluation were: Hate Speech Detection (using SemEval 2019 Task 5, HatEval dataset), Sentiment and Emotion Analysis (using TASS 2020 datasets), and Irony detection (using IrosVa 2019 dataset).

| model              | hate speech     | sentiment analysis   | emotion analysis   | irony detection  |   score |
|:-------------------|:----------------|:---------------------|:-------------------|:-----------------|---------:|
| robertuito-uncased | 0.801 ± 0.010  | 0.707 ± 0.004       | 0.551 ± 0.011     | 0.736 ± 0.008   | 0.6987  |
| robertuito-deacc   | 0.798 ± 0.008  | 0.702 ± 0.004       | 0.543 ± 0.015     | 0.740 ± 0.006   | 0.6958  |
| robertuito-cased   | 0.790 ± 0.012  | 0.701 ± 0.012       | 0.519 ± 0.032     | 0.719 ± 0.023   | 0.6822  |
| roberta-bne        | 0.766 ± 0.015  | 0.669 ± 0.006       | 0.533 ± 0.011     | 0.723 ± 0.017   | 0.6726  |
| bertin             | 0.767 ± 0.005  | 0.665 ± 0.003       | 0.518 ± 0.012     | 0.716 ± 0.008   | 0.6666  |
| beto-cased         | 0.768 ± 0.012  | 0.665 ± 0.004       | 0.521 ± 0.012     | 0.706 ± 0.007   | 0.6651  |
| beto-uncased       | 0.757 ± 0.012  | 0.649 ± 0.005       | 0.521 ± 0.006     | 0.702 ± 0.008   | 0.6571  |


We release the pre-trained models on huggingface model hub:

- [RoBERTuito uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased)
- [RoBERTuito cased](https://huggingface.co/pysentimiento/robertuito-base-cased)
- [RoBERTuito deacc](https://huggingface.co/pysentimiento/robertuito-base-deacc)


## Masked LM

To test the masked LM, take into account that space is encoded inside SentencePiece's tokens. So, if you want to test

```
Este es un día<mask>
```

don't put a space between `día` and `<mask>`


## Usage

**IMPORTANT -- READ THIS FIRST**

*RoBERTuito* is not yet fully-integrated into `huggingface/transformers`. To use it, first install `pysentimiento`

```bash
pip install pysentimiento
```

and preprocess text using `pysentimiento.preprocessing.preprocess_tweet` before feeding it into the tokenizer

```python
from transformers import AutoTokenizer
from pysentimiento.preprocessing import preprocess_tweet

tokenizer = AutoTokenizer.from_pretrained('pysentimiento/robertuito-base-cased')

text = "Esto es un tweet estoy usando #Robertuito @pysentimiento 🤣"
preprocessed_text = preprocess_tweet(text, ha)

tokenizer.tokenize(preprocessed_text)
# ['<s>','▁Esto','▁es','▁un','▁tweet','▁estoy','▁usando','▁','▁hashtag','▁','▁ro','bert','uito','▁@usuario','▁','▁emoji','▁cara','▁revolviéndose','▁de','▁la','▁risa','▁emoji','</s>']
```

We are working on integrating this preprocessing step into a Tokenizer within `transformers` library.

You can check a text classification example in this notebook:
[![Test it in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1WcubR0kbqT289XupSnN5-STe7HafyKpf#scrollTo=SF-n4IdjnoYk)

## Citation

If you use *RoBERTuito*, please cite our paper:

```bibtex
@inproceedings{perez-etal-2022-robertuito,
    title = "{R}o{BERT}uito: a pre-trained language model for social media text in {S}panish",
    author = "P{\'e}rez, Juan Manuel  and
      Furman, Dami{\'a}n Ariel  and
      Alonso Alemany, Laura  and
      Luque, Franco M.",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.785",
    pages = "7235--7243",
    abstract = "Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for natural language processing 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 has some cross-lingual abilities, achieving top results for English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and also competitive performance against monolingual models in English Twitter tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.",
}
```