XGBRegressor-text-filter

📣 UPDATE: If you like/use Tucanos or GigaVerbo for anything, know that version 2 is already out. Go to our new organization (Polyglot) to check out all the improvements the Tucano 2 series brings!

XGBRegressor-text-filter is a text-quality filter built on top of the xgboost library. It uses the embeddings generated by sentence-transformers/LaBSE as a feature vector.

This repository has the source code used to train this model.

Usage

Here's an example of how to use the XGBRegressor-text-filter:

from transformers import AutoTokenizer, AutoModel
from xgboost import XGBRegressor
import torch.nn.functional as F
import torch

def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/LaBSE")
embedding_model = AutoModel.from_pretrained("sentence-transformers/LaBSE")
device = ("cuda" if torch.cuda.is_available() else "cpu")
embedding_model.to(device)

bst_r = XGBRegressor({'device': device})
bst_r.load_model('/path/to/XGBRegressor-text-classifier.json')

def score_text(text, model):

    encoded_input = tokenizer(text, padding=True, truncation=True, return_tensors='pt').to(device)

    with torch.no_grad():
        model_output = embedding_model(**encoded_input)

    sentence_embedding = mean_pooling(model_output, encoded_input['attention_mask'])

    embedding = F.normalize(sentence_embedding, p=2, dim=1).numpy()
    score = model.predict(embedding)[0]

    return score

score_text("Os tucanos são aves que correspondem à família Ramphastidae, vivem nas florestas tropicais da América Central e América do Sul. A família inclui cinco gêneros e mais de quarenta espécies diferentes. Possuem bicos notavelmente grandes e coloridos, que possuem a função de termorregulação para as muitas espécies que passam muito tempo na copa da floresta exposta ao sol tropical quente.", bst_r)

Cite as 🤗

@misc{correa2024tucanoadvancingneuraltext,
      title={{Tucano: Advancing Neural Text Generation for Portuguese}}, 
      author={Corr{\^e}a, Nicholas Kluge and Sen, Aniket and Falk, Sophia and Fatimah, Shiza},
      year={2024},
      eprint={2411.07854},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.07854}, 
}

@article{correa2025tucanoadvancingneuraltext,
    title={{Tucano: Advancing Neural Text Generation for Portuguese}},
    author={Corr{\^e}a, Nicholas Kluge and Sen, Aniket and Falk, Sophia and Fatimah, Shiza},
    journal={Patterns},
    publisher={Elsevier},
    year={2025},
    doi={10.1016/j.patter.2025.101325},
    url={https://doi.org/10.1016/j.patter.2025.101325},
    issn={2666-3899}
}

Aknowlegments

We gratefully acknowledge the granted access to the Marvin cluster hosted by University of Bonn along with the support provided by its High Performance Computing & Analytics Lab.

License

XGBRegressor-text-filter is licensed under the Apache License, Version 2.0. For more details, see the LICENSE file.