robertuito-base-deacc

RoBERTuito

面向西班牙语社交媒体文本的预训练语言模型

阅读完整论文
GitHub代码库

RoBERTuito 是一个针对西班牙语用户生成内容的预训练语言模型，遵循RoBERTa框架在5亿条推文上训练而成。该模型提供三种变体：区分大小写版、不区分大小写版和不区分大小写+去重音符号版。

我们在涉及西班牙语用户生成文本的任务基准测试中评估了RoBERTuito，其表现优于其他西班牙语预训练模型如BETO、BERTin和RoBERTa-BNE。四项评测任务包括：仇恨言论检测（使用SemEval 2019任务5的HatEval数据集）、情感分析（TASS 2020数据集）、情绪分析（同TASS 2020）以及反讽检测（IrosVa 2019数据集）。

预训练模型已发布在HuggingFace模型中心：

• RoBERTuito不区分大小写版
• RoBERTuito区分大小写版
• RoBERTuito去重音版

掩码语言模型

测试掩码语言模型时需注意：空格已编码在SentencePiece的分词中。例如测试

Este es un día<mask>  

时不要在"día"和""之间添加空格。

使用指南

重要提示——必读事项

RoBERTuito尚未完全集成至huggingface/transformers。使用时请先安装pysentimiento：

pip install pysentimiento  

在输入分词器前需用pysentimiento.preprocessing.preprocess_tweet预处理文本：

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>']  

我们正在将该预处理步骤集成至transformers库的分词器中。

文本分类示例参见Colab笔记本：

引用

若使用RoBERTuito，请引用我们的论文：

@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.",  
}