NILC Portuguese Word Embeddings β FastText Skip-Gram 300d
This repository contains the FastText Skip-Gram 300d model in safetensors format.
About
NILC-Embeddings is a repository for storing and sharing word embeddings for the Portuguese language. The goal is to provide ready-to-use vector resources for Natural Language Processing (NLP) and Machine Learning tasks.
The embeddings were trained on a large Portuguese corpus (Brazilian + European), composed of 17 corpora (~1.39B tokens). Training was carried out with the following algorithms: Word2Vec, FastText, Wang2Vec, and GloVe.
π Files
- embeddings.safetensorsβ embedding matrix (- [vocab_size, 300])
- vocab.txtβ vocabulary (one token per line, aligned with rows)
π Usage
from huggingface_hub import hf_hub_download
from safetensors.numpy import load_file
path = hf_hub_download(repo_id="nilc-nlp/fasttext-skip-gram-300d",
                       filename="embeddings.safetensors")
data = load_file(path)
vectors = data["embeddings"]
vocab_path = hf_hub_download(repo_id="nilc-nlp/fasttext-skip-gram-300d",
                             filename="vocab.txt")
with open(vocab_path) as f:
    vocab = [w.strip() for w in f]
print(vectors.shape)
Or in PyTorch:
from safetensors.torch import load_file
tensors = load_file("embeddings.safetensors")
vectors = tensors["embeddings"]  # torch.Tensor
π Corpus
The embeddings were trained on a combination of 17 corpora (~1.39B tokens):
| Corpus | Tokens | Types | Genre | Description | 
|---|---|---|---|---|
| LX-Corpus [Rodrigues et al. 2016] | 714,286,638 | 2,605,393 | Mixed genres | Large collection of texts from 19 sources, mostly European Portuguese | 
| Wikipedia | 219,293,003 | 1,758,191 | Encyclopedic | Wikipedia dump (2016-10-20) | 
| GoogleNews | 160,396,456 | 664,320 | Informative | News crawled from Google News | 
| SubIMDB-PT | 129,975,149 | 500,302 | Spoken | Movie subtitles from IMDb | 
| G1 | 105,341,070 | 392,635 | Informative | News from G1 portal (2014β2015) | 
| PLN-Br [Bruckschen et al. 2008] | 31,196,395 | 259,762 | Informative | Corpus of PLN-BR project (1994β2005) | 
| DomΓnio PΓΊblico | 23,750,521 | 381,697 | Prose | 138,268 literary works | 
| Lacio-Web [AluΓsio et al. 2003] | 8,962,718 | 196,077 | Mixed | Literary, informative, scientific, law, didactic texts | 
| Literatura Brasileira | 1,299,008 | 66,706 | Prose | Classical Brazilian fiction e-books | 
| Mundo Estranho | 1,047,108 | 55,000 | Informative | Texts from Mundo Estranho magazine | 
| CHC | 941,032 | 36,522 | Informative | Texts from CiΓͺncia Hoje das CrianΓ§as | 
| FAPESP | 499,008 | 31,746 | Science communication | Texts from Pesquisa FAPESP magazine | 
| Textbooks | 96,209 | 11,597 | Didactic | Elementary school textbooks | 
| Folhinha | 73,575 | 9,207 | Informative | Childrenβs news from Folhinha (Folha de SΓ£o Paulo) | 
| NILC subcorpus | 32,868 | 4,064 | Informative | Childrenβs texts (3rdβ4th grade) | 
| Para Seu Filho Ler | 21,224 | 3,942 | Informative | Childrenβs news from Zero Hora | 
| SARESP | 13,308 | 3,293 | Didactic | School evaluation texts | 
| Total | 1,395,926,282 | 3,827,725 | β | β | 
π Paper
Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks
Hartmann, N. et al. (2017), STIL 2017.
ArXiv Paper
BibTeX
@inproceedings{hartmann-etal-2017-portuguese,
  title        = {{P}ortuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks},
  author       = {Hartmann, Nathan  and Fonseca, Erick  and Shulby, Christopher  and Treviso, Marcos  and Silva, J{'e}ssica  and Alu{'i}sio, Sandra},
  year         = 2017,
  month        = oct,
  booktitle    = {Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology},
  publisher    = {Sociedade Brasileira de Computa{\c{c}}{\~a}o},
  address      = {Uberl{\^a}ndia, Brazil},
  pages        = {122--131},
  url          = {https://aclanthology.org/W17-6615/},
  editor       = {Paetzold, Gustavo Henrique  and Pinheiro, Vl{'a}dia}
}
π License
Creative Commons Attribution 4.0 International (CC BY 4.0)
