Anglicisms Spanish Mbert

🚀 Anglicisms Spanish mBERT

This is a pretrained model for detecting unassimilated English lexical borrowings in Spanish newswire, labeling words of foreign origin used in Spanish.

🚀 Quick Start

This model is a fine - tuned version of multilingual BERT trained on the COALAS corpus for detecting lexical borrowings.

Model Labels

The model considers two labels:

• ENG: For English lexical borrowings (smartphone, online, podcast)
• OTHER: For lexical borrowings from any other language (boutique, anime, umami)

The model uses BIO encoding to account for multitoken borrowings.

⚠ Important Note This is not the best - performing model for this task. For the best - performing model (F1 = 85.76) see Flair model.

✨ Features

• Detects unassimilated English lexical borrowings in Spanish newswire.
• Labels words of foreign origin used in Spanish.
• Uses BIO encoding for multitoken borrowings.

📚 Documentation

Metrics (on the test set)

The following table summarizes the results obtained on the test set of the COALAS corpus.

Dataset

This model was trained on COALAS, a corpus of Spanish newswire annotated with unassimilated lexical borrowings. The corpus contains 370,000 tokens and includes various written media written in European Spanish. The test set was designed to be as difficult as possible: it covers sources and dates not seen in the training set, includes a high number of OOV words (92% of the borrowings in the test set are OOV) and is very borrowing - dense (20 borrowings per 1,000 tokens).

More info

More information about the dataset, model experimentation and error analysis can be found in the paper: Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling.

💻 Usage Examples

Basic Usage

from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("lirondos/anglicisms-spanish-mbert")
model = AutoModelForTokenClassification.from_pretrained("lirondos/anglicisms-spanish-mbert")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)

example = "Buscamos data scientist para proyecto de machine learning."

borrowings = nlp(example)
print(borrowings)

📄 License

This project is licensed under the CC - BY - 4.0 license.

📚 Citation

If you use this model, please cite the following reference:

@inproceedings{alvarez-mellado-lignos-2022-detecting,
    title = "Detecting Unassimilated Borrowings in {S}panish: {A}n Annotated Corpus and Approaches to Modeling",
    author = "{\'A}lvarez-Mellado, Elena  and
      Lignos, Constantine",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.268",
    pages = "3868--3888",
    abstract = "This work presents a new resource for borrowing identification and analyzes the performance and errors of several models on this task. We introduce a new annotated corpus of Spanish newswire rich in unassimilated lexical borrowings{---}words from one language that are introduced into another without orthographic adaptation{---}and use it to evaluate how several sequence labeling models (CRF, BiLSTM-CRF, and Transformer-based models) perform. The corpus contains 370,000 tokens and is larger, more borrowing-dense, OOV-rich, and topic-varied than previous corpora available for this task. Our results show that a BiLSTM-CRF model fed with subword embeddings along with either Transformer-based embeddings pretrained on codeswitched data or a combination of contextualized word embeddings outperforms results obtained by a multilingual BERT-based model.",
}