robertuito-base-uncased

RoBERTuito

用于西班牙语社交媒体文本的预训练语言模型

PAPER Github Repository

RoBERTuito是针对西班牙语用户生成内容的预训练语言模型，根据RoBERTa的指导原则，使用5亿条推文进行了训练。RoBERTuito有三种变体：大小写敏感、大小写不敏感和去除重音的大小写不敏感。

我们在涉及西班牙语用户生成文本的基准任务上测试了RoBERTuito。它在 Hate Speech Detection（使用SemEval 2019 Task 5，HatEval数据集）、情感和情绪分析（使用TASS 2020数据集）以及讽刺检测（使用IrosVa 2019数据集）等任务中优于其他预训练语言模型，如BETO、BERTin和RoBERTa-BNE。

我们在huggingface模型中心发布了预训练模型：

• RoBERTuito uncased
• RoBERTuito cased
• RoBERTuito deacc

掩码语言建模

测试掩码语言建模时，请注意空格已经在SentencePiece的标记中编码。因此，如果您要进行测试

Este es un día<mask>

请不要在 día 和 <mask> 之间加入空格

用法

重要 - 先阅读此内容

RoBERTuito尚未完全集成到huggingface/transformers中。要使用它，首先安装pysentimiento

pip install pysentimiento

并在将文本输入分词器之前，使用pysentimiento.preprocessing.preprocess_tweet对文本进行预处理

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>']

我们正在努力将此预处理步骤整合到transformers库中的Tokenizer中

在此笔记本中查看文本分类示例：

引用

如果您使用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.",
}