---
language: pt
tags:
- word-embeddings
- static
- portuguese
- wang2vec
- cbow
- 600d
license: cc-by-4.0
library_name: safetensors
pipeline_tag: feature-extraction
---

# NILC Portuguese Word Embeddings — Wang2Vec CBOW 600d

This repository contains the **Wang2Vec CBOW 600d** 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**.  



---

## 📂 Files
- `embeddings.safetensors` → embedding matrix (`[vocab_size, 600]`)
- `vocab.txt` → vocabulary (one token per line, aligned with rows)

---

## 🚀 Usage

```python
from huggingface_hub import hf_hub_download
from safetensors.numpy import load_file

path = hf_hub_download(repo_id="nilc-nlp/wang2vec-cbow-600d",
                       filename="embeddings.safetensors")

data = load_file(path)
vectors = data["embeddings"]

vocab_path = hf_hub_download(repo_id="nilc-nlp/wang2vec-cbow-600d",
                             filename="vocab.txt")
with open(vocab_path) as f:
    vocab = [w.strip() for w in f]

print(vectors.shape)
```

Or in PyTorch:

```python
from safetensors.torch import load_file
tensors = load_file("embeddings.safetensors")
vectors = tensors["embeddings"]  # torch.Tensor
```

---

## 📊 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 (2014–2015) |
| PLN-Br [Bruckschen et al. 2008] | 31,196,395 | 259,762 | Informative | Corpus of PLN-BR project (1994–2005) |
| Domínio Público | 23,750,521 | 381,697 | Prose | 138,268 literary works |
| Lacio-Web [Aluísio 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 Ciência Hoje das Crianças |
| 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 | Children’s news from Folhinha (Folha de São Paulo) |
| NILC subcorpus | 32,868 | 4,064 | Informative | Children’s texts (3rd–4th grade) |
| Para Seu Filho Ler | 21,224 | 3,942 | Informative | Children’s news from Zero Hora |
| SARESP | 13,308 | 3,293 | Didactic | School evaluation texts |
| **Total** | **1,395,926,282** | **3,827,725** | — | —

---

## 📖 Paper

**Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks**  
Hartmann, N. et al. (2017), STIL 2017.  
[ArXiv Paper](https://arxiv.org/abs/1708.06025)

### BibTeX
```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}
}
```

---

## 📜 License
Creative Commons Attribution 4.0 International (CC BY 4.0)