Pre-trained Word Vector Models of 30+ Languages
This project has two purposes. First of all, I'd like to share some of my experience in nlp tasks such as segmentation or word vectors. The other, which is more important, is that probably some people are searching for pre-trained word vector models for non-English languages. Alas! English has gained much more attention than any other languages has done. Check this to see how easily you can get a variety of pre-trained English word vectors without efforts. I think it's time to turn our eyes to a multi language version of this.
Required Python 3.
See requirements
If using below language, you also need additional library.
- Chinese:
jieba - Japanese:
mecab - Korean:
konlpy - Thai:
pythai - Vietnamese:
pyvi
If training your own model, you have to install below library.
- fastText: fasttext
- Word2Vec:
gensim
- Check this to know what word embedding is.
- Check this to quickly get a picture of Word2vec.
- Check this to install fastText.
- Watch this to really understand what's happening under the hood of Word2vec.
- Go get various English word vectors here if needed.
- STEP 1-1. Download the wikipedia database backup dumps of the language you want (For example, for english wiki go to https://dumps.wikimedia.org/enwiki/, click the latest timestamp, and download the
enwiki-YYYYMMDD-pages-articles-multistream.xml.bz2file). - STEP 1-2. Install requirements packages.
- STEP 2. Extract running texts to
./datadirectory. - STEP 3. Run
./src/build_corpus.py.
python build_corpus.py --lcode=ko- STEP 4-1. Run
./src/train_word2vec.pyto get Word2Vec word vectors:
python train_word2vec.py --lcode=en --vector_size=300 --window_size=5 --vocab_size=50000 --num_negative=5- STEP 4-2. Run
./script/fasttext.shto get fastText word vectors.
Two types of pre-trained models are provided. w and f represent word2vec and fastText respectively.
Check language code here.
| Language | ISO 639-1 | Vector Size | Corpus Size | Vocabulary Size |
|---|---|---|---|---|
| Bengali(w) & Bengali(f) | bn | 300 | 147M | 10059 |
| Catalan(w) & Catalan(f) | ca | 300 | 967M | 50013 |
| Chinese(w) & Chinese(f) | zh | 300 | 1G | 50101 |
| Danish(w) & Danish(f) | da | 300 | 295M | 30134 |
| Dutch(w) & Dutch(f) | nl | 300 | 1G | 50160 |
| Esperanto(w) & Esperanto(f) | eo | 300 | 1G | 50597 |
| Finnish(w) & Finnish(f) | fi | 300 | 467M | 30029 |
| French(w) & French(f) | fr | 300 | 1G | 50130 |
| German(w) & German(f) | de | 300 | 1G | 50006 |
| Hindi(w) & Hindi(f) | hi | 300 | 323M | 30393 |
| Hungarian(w) & Hungarian(f) | hu | 300 | 692M | 40122 |
| Indonesian(w) & Indonesian(f) | id | 300 | 402M | 30048 |
| Italian(w) & Italian(f) | it | 300 | 1G | 50031 |
| Japanese(w) & Japanese(f) | ja | 300 | 1G | 50108 |
| Javanese(w) & Javanese(f) | jv | 100 | 31M | 10019 |
| Korean(w) & Korean(f) | ko | 200 | 339M | 30185 |
| Malay(w) & Malay(f) | ms | 100 | 173M | 10010 |
| Norwegian(w) & Norwegian(f) | no | 300 | 1G | 50209 |
| Norwegian Nynorsk(w) & Norwegian Nynorsk(f) | nn | 100 | 114M | 10036 |
| Polish(w) & Polish(f) | pl | 300 | 1G | 50035 |
| Portuguese(w) & Portuguese(f) | pt | 300 | 1G | 50246 |
| Russian(w) & Russian(f) | ru | 300 | 1G | 50102 |
| Spanish(w) & Spanish(f) | es | 300 | 1G | 50003 |
| Swahili(w) & Swahili(f) | sw | 100 | 24M | 10222 |
| Swedish(w) & Swedish(f) | sv | 300 | 1G | 50052 |
| Tagalog(w) & Tagalog(f) | tl | 100 | 38M | 10068 |
| Thai(w) & Thai(f) | th | 300 | 696M | 30225 |
| Turkish(w) & Turkish(f) | tr | 200 | 370M | 30036 |
| Vietnamese(w) & Vietnamese(f) | vi | 100 | 74M | 10087 |
WordVectorModels
Copyright (c) 2018 Kyubyong Park, Astro
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WordVectorModels is forked from wordvectors.