View navec on Snyk Open Source Advisor

Package Health Score

45 / 100

security
Security review needed
popularity
Small
maintenance
Inactive
community
Sustainable

Popularity

Small

GitHub Stars: 190
Forks: 18
Contributors: 4

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package navec, we found that it has been starred 190 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Oct 29, 2020
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

We found a way for you to contribute to the project! Looks like navec is missing a security policy.

You can connect your project's repository to Snyk to stay up to date on security alerts and receive automatic fix pull requests.

Keep your project free of vulnerabilities with Snyk

Maintenance

Inactive

Commit Frequency

No Recent Commits

Open Issues: 5
Open PR: 0
Last Release: 4 years ago
Last Commit: 1 year ago

Further analysis of the maintenance status of navec based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for navec is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 4
Funding: No

This project has seen only 10 or less contributors.

We found a way for you to contribute to the project! Looks like navec is missing a Code of Conduct.

Embed Package Health Score Badge

Keep your project healthy

Check your requirements.txt

Ensure all the packages you're using are healthy and well-maintained

Snyk Vulnerability Scanner

Get health score & security insights directly in your IDE

Package

Python Versions Compatibility: ==3.*

Age: 6 years
Latest Release: 4 years ago
Dependencies: 1 Direct / 1 Total
Versions: 9
Maintainers: 0
Wheels: OS Independent

Readme

Navec is a library of pretrained word embeddings for russian language. It shows competitive or better results than RusVectores, loads ~10 times faster (~1 sec), takes ~10 times less space (~50 MB). See natasha.github.io article for more info.

> Navec = large russian text datasets + vanila GloVe + quantization

Downloads

How to read model filename:

navec_hudlit_v1_12B_500K_300d_100q.tar
                 |    |    |    |
                 |    |    |     ---- 100 dimentions after quantization
                 |    |     --------- original vectors have 300 dimentions
                 |     -------------- vocab size is 500 000 words + 2 for , 
                  ------------------- dataset of 12 billion tokens was used

Currently two models are published:

Model	Size	Description	Sources
navec_hudlit_v1_12B_500K_300d_100q.tar `#`	50MB	Should be used by default. Shows best results on intrinsic evaluations. Model was trained on large corpus of russian literature (~150GB).	`librusec`
navec_news_v1_1B_250K_300d_100q.tar `#`	25MB	Try to use this model to news texts. It is two times smaller than `hudlit` but covers same 98% of words in news articles.	`lenta` `ria` `taiga_fontanka` `buriy_news` `buriy_webhose` `ods_gazeta` `ods_interfax`

Installation

Navec supports Pyton 3.5+ and PyPy 3.

$ pip install navec

Usage

First download hudlit emdeddings (see the table above):

wget https://storage.yandexcloud.net/natasha-navec/packs/navec_hudlit_v1_12B_500K_300d_100q.tar

Load tar-archive with Navec.load, it takes ~1s and ~100MB of RAM:

&gt;&gt;&gt; from navec import Navec

&gt;&gt;&gt; path = 'hudlit_12B_500K_300d_100q.tar'
&gt;&gt;&gt; navec = Navec.load(path)

Then navec can be used as a dict object:

&gt;&gt;&gt; navec['навек']
array([ 0.3955571 ,  0.11600914,  0.24605067, -0.35206917, -0.08932345,
        0.3382279 , -0.5457616 ,  0.07472657, -0.4753835 , -0.3330848 ,
        ...

&gt;&gt;&gt; 'нааавееек' in navec
False

&gt;&gt;&gt; navec.get('нааавееек')
None

To get an index of word, use navec.vocab:

&gt;&gt;&gt; navec.vocab['навек']
225823

&gt;&gt;&gt; navec.vocab.get('наааавеeeк', navec.vocab.unk_id)
500000   # == navec.vocab['']

There are two special words in vocab, and:

&gt;&gt;&gt; navec['']
array([ 3.69125791e-02,  9.32818875e-02,  2.01917738e-02, ...

&gt;&gt;&gt; navec['']
array([0., 0., 0., 0., 0., 0., ...

To use Navec in PyTorch model there is a Slovnet module:

&gt;&gt;&gt; import torch
&gt;&gt;&gt; from slovnet.model.emb import NavecEmbedding

&gt;&gt;&gt; emb = NavecEmbedding(navec)
&gt;&gt;&gt; input = torch.tensor([1, 2, 0])
&gt;&gt;&gt; output = emb(input)

&gt;&gt;&gt; output.shape
torch.Size([3, 300])

&gt;&gt;&gt; output
tensor([[ 4.2000e-01,  3.6666e-01,  1.7728e-01, -3.8719e-01, -1.0762e-01,
          1.6954e-01, -4.6063e-01,  5.4519e-01, -2.1212e-01,  2.0965e-01,
          1.9658e-01,  2.7807e-01, -2.3802e-01,  3.5155e-01,  1.4491e-02,
		  ...

Evaluation

Let's compore Navec to top 5 RusVectores models (based on simlex and hj eval datasets). In each column top 3 results are highlighted.

init — time it takes to load model file to RAM. tayga_upos_skipgram_300_2_2019 word2vec binary file takes 5 seconds to load with gensim.KeyedVectors.load_word2vec_format. tayga_none_fasttextcbow_300_10_2019 fastText large ~2.7 GB file takes 8 seconds. Navec hudlit with vocab 2 times larger than previous two takes 1 second.
get — time is takes to query embedding vector for a single word. Word2vec models win here, to fetch a vector they basically do dict.get. FastText and Navec for every query do extra computation. FastText extracts and sums word ngrams, Navec unpacks vector from quantization table. In practice query to embeddings table is small compared to all other computation in network.
disk — model file size. It is convenient for deployment and distribution to have small models. Notice that hudlit model is 4-20 times smaller with vocab size 2 times bigger.
ram — space model takes in RAM after loading. It is convenient to have small memory footprint to fit more computation on single server.
vocab — number of words in vocab, number of embedding vectors. Since Navec vectors table takes less space we can have larger vocab. With 500K vocab hudlit model has ~2% OVV rate on average.

	type	init, s	get, µs	disk, mb	ram, mb	vocab
hudlit_12B_500K_300d_100q	navec	1.1	21.6	50.6	95.3	500K
news_1B_250K_300d_100q	navec	0.8	20.7	25.4	47.7	250K
ruscorpora_upos_cbow_300_20_2019	w2v	3.3	1.4	220.6	236.1	189K
ruwikiruscorpora_upos_skipgram_300_2_2019	w2v	5.0	1.5	290.0	309.4	248K
tayga_upos_skipgram_300_2_2019	w2v	5.2	1.4	290.7	310.9	249K
tayga_none_fasttextcbow_300_10_2019	fasttext	8.0	13.4	2741.9	2746.9	192K
araneum_none_fasttextcbow_300_5_2018	fasttext	16.4	10.6	2752.1	2754.7	195K

Now let's look at intrinsic evaluation scores. Navec hudlit model does not show best results on all datasets but it is usually in top 3. We'll use 6 datasets:

simlex965, hj — two small datasets (965 and 398 tests respectively) used by RusVectores, see the their paper for more info. Metric is spearman correlation, other datasets use average precision.
rt, ae, ae2 — large datasets (114066, 22919, 86772 tests) from RUSSE workshop, see project description for more.
lrwc — relatively new dataset by Yandex.Toloka, see their page.

	type	simlex	hj	rt	ae	ae2	lrwc
hudlit_12B_500K_300d_100q	navec	0.310	0.707	0.842	0.931	0.923	0.604
news_1B_250K_300d_100q	navec	0.230	0.590	0.784	0.866	0.861	0.589
ruscorpora_upos_cbow_300_20_2019	w2v	0.359	0.685	0.852	0.758	0.896	0.602
ruwikiruscorpora_upos_skipgram_300_2_2019	w2v	0.321	0.723	0.817	0.801	0.860	0.629
tayga_upos_skipgram_300_2_2019	w2v	0.429	0.749	0.871	0.771	0.899	0.639
tayga_none_fasttextcbow_300_10_2019	fasttext	0.369	0.639	0.793	0.682	0.813	0.536
araneum_none_fasttextcbow_300_5_2018	fasttext	0.349	0.671	0.801	0.706	0.793	0.579

Support

Chat — https://telegram.me/natural_language_processing
Issues — https://github.com/natasha/navec/issues
Commercial support — https://lab.alexkuk.ru

Development

Test

make test

Package

make version
git push
git push --tags

make clean wheel upload

Notice! All commands belows use code from navec/train, it is not under CI, it works only with Python 3, it is expected user is familiar with source code. We use Yandex Cloud Compute and Object Storage.

Create remote worker

To compute cooc (large HDD, 1Tb for librusec).

yc compute instance create \
    --name worker \
    --zone ru-central1-a \
    --network-interface subnet-name=default,nat-ip-version=ipv4 \
    --create-boot-disk image-folder-id=standard-images,image-family=ubuntu-1804,type=network-hdd,size=1000 \
    --memory 8 \
    --cores 2 \
    --core-fraction 100 \
    --ssh-key ~/.ssh/id_rsa.pub \
    --folder-name default \
    --preemptible  # in case computation takes &lt;24h

To fit embedings (multiple cores). HDD should be > cooc.bin * 3 (for suffle + tmp)

yc compute instance create \
    --name worker \
    --zone ru-central1-a \
    --network-interface subnet-name=default,nat-ip-version=ipv4 \
    --create-boot-disk image-folder-id=standard-images,image-family=ubuntu-1804,type=network-hdd,size=700 \
    --memory 16 \
    --cores 16 \
    --core-fraction 100 \
    --ssh-key ~/.ssh/id_rsa.pub  \
    --folder-name default \
    --preemptible

Setup machine

yc compute instance list --folder-name default
ssh yc-user@123.123.123.123

sudo locale-gen en_US.UTF-8
sudo timedatectl set-timezone Europe/Moscow
sudo apt-get update
sudo DEBIAN_FRONTEND=noninteractive apt-get install -y language-pack-ru python3-pip screen unzip git pv cmake

wget https://nlp.stanford.edu/software/GloVe-1.2.zip
unzip GloVe-1.2.zip
rm GloVe-1.2.zip
mv GloVe-1.2 glove
cd glove
make
cd ..

export GLOVE_DIR=~/glove/build

git clone https://github.com/natasha/navec.git
sudo -H pip3 install -e navec
sudo -H pip3 install -r navec/requirements/train.txt

screen
ctrl a d

Remove instance

yc compute instance list --folder-name default
yc compute instance delete xxxxxxxxx

Env, used by navec-train s3|vocab|cooc|emb

export S3_KEY=_XxXXXxxx_XXXxxxxXxxx
export S3_SECRET=XXxxx_XXXXXXxxxxxxXXXXxxXXx-XxxXXxxxX
export S3_BUCKET=XXXXXXX
export GLOVE_DIR=~/path/to/glove/build

Share text data (see corus)

navec-train s3 upload librusec_fb2.plain.gz sources/librusec.gz
navec-train s3 upload taiga/proza_ru.zip sources/taiga_proza.zip

navec-train s3 upload ruwiki-latest-pages-articles.xml.bz2 sources/wiki.xml.bz2

navec-train s3 upload lenta-ru-news.csv.gz sources/lenta.csv.gz
navec-train s3 upload ria.json.gz sources/ria.json.gz
navec-train s3 upload taiga/Fontanka.tar.gz sources/taiga_fontanka.tar.gz
navec-train s3 upload buriy/news-articles-2014.tar.bz2 sources/buriy_news1.tar.bz2
navec-train s3 upload buriy/news-articles-2015-part1.tar.bz2 sources/buriy_news2.tar.bz2
navec-train s3 upload buriy/news-articles-2015-part2.tar.bz2 sources/buriy_news3.tar.bz2
navec-train s3 upload buriy/webhose-2016.tar.bz2 sources/buriy_webhose.tar.bz2
navec-train s3 upload ods/gazeta_v1.csv.zip sources/ods_gazeta.csv.zip
navec-train s3 upload ods/interfax_v1.csv.zip sources/ods_interfax.csv.zip

navec-train s3 download sources/librusec.gz
navec-train s3 download sources/taiga_proza.zip

navec-train s3 download sources/wiki.xml.bz2

navec-train s3 download sources/lenta.csv.gz
navec-train s3 download sources/ria.json.gz
navec-train s3 download sources/taiga_fontanka.tar.gz
navec-train s3 download sources/buriy_news1.tar.bz2
navec-train s3 download sources/buriy_news2.tar.bz2
navec-train s3 download sources/buriy_news3.tar.bz2
navec-train s3 download sources/buriy_webhose.tar.bz2
navec-train s3 download sources/ods_gazeta.csv.zip
navec-train s3 download sources/ods_interfax.csv.zip

Text to tokens

navec-train corpus librusec librusec.gz | pv | navec-train tokenize &gt; tokens.txt  # ~12B words
navec-train corpus taiga_proza taiga_proza.zip | pv | navec-train tokenize &gt; tokens.txt  # ~3B

navec-train corpus wiki wiki.xml.bz2 | pv | navec-train tokenize &gt; tokens.txt  # ~0.5B

navec-train corpus lenta lenta.csv.gz | pv | navec-train tokenize &gt;&gt; tokens.txt
navec-train corpus ria ria.json.gz | pv | navec-train tokenize &gt;&gt; tokens.txt
navec-train corpus taiga_fontanka taiga_fontanka.tar.gz | pv | navec-train tokenize &gt;&gt; tokens.txt
navec-train corpus buriy_news buriy_news1.tar.bz2 | pv | navec-train tokenize &gt;&gt; tokens.txt
navec-train corpus buriy_news buriy_news2.tar.bz2 | pv | navec-train tokenize &gt;&gt; tokens.txt
navec-train corpus buriy_news buriy_news3.tar.bz2 | pv | navec-train tokenize &gt;&gt; tokens.txt
navec-train corpus buriy_webhose buriy_webhose.tar.bz2 | pv | navec-train tokenize &gt;&gt; tokens.txt
navec-train corpus ods_gazeta ods_gazeta.csv.zip | pv | navec-train tokenize &gt;&gt; tokens.txt
navec-train corpus ods_interfax ods_interfax.csv.zip | pv | navec-train tokenize &gt;&gt; tokens.txt  # ~1B

pv tokens.txt | gzip &gt; tokens.txt.gz
navec-train s3 upload tokens.txt.gz librusec_tokens.txt.gz

navec-train s3 upload tokens.txt taiga_proza_tokens.txt
navec-train s3 upload tokens.txt news_tokens.txt
navec-train s3 upload tokens.txt wiki_tokens.txt

Tokens to vocab

pv tokens.txt \
	| navec-train vocab count \
	&gt; full_vocab.txt

pv full_vocab.txt \
	| navec-train vocab quantile

# librusec
# ...
# 0.970      325 882
# 0.980      511 542
# 0.990    1 122 624
# 1.000   22 129 654

# taiga_proza
# ...
# 0.960      229 906
# 0.970      321 810
# 0.980      517 647
# 0.990    1 224 277
# 1.000   14 302 409

# wiki
# ...
# 0.950     380 134
# 0.960     519 817
# 0.970     757 561
# 0.980   1 223 201
# 0.990   2 422 265
# 1.000   6 664 630

# news
# ...
# 0.970    163 833
# 0.980    243 903
# 0.990    462 361
# 1.000  3 744 070

# threashold at ~0.98
# librusec 500000
# taiga_proza 500000
# wiki 750000
# news 250000

cat full_vocab.txt \
	| head -500000 \
	| LC_ALL=C sort \
	&gt; vocab.txt

navec-train s3 upload full_vocab.txt librusec_full_vocab.txt
navec-train s3 upload vocab.txt librusec_vocab.txt

navec-train s3 upload full_vocab.txt taiga_proza_full_vocab.txt
navec-train s3 upload vocab.txt taiga_proza_vocab.txt

navec-train s3 upload full_vocab.txt wiki_full_vocab.txt
navec-train s3 upload vocab.txt wiki_vocab.txt

navec-train s3 upload full_vocab.txt news_full_vocab.txt
navec-train s3 upload vocab.txt news_vocab.txt

Compute coocurence pairs

# Default limit on max number of open files is 1024, merge fails if
# number of chunks is large

ulimit -n  # 1024
sudo nano /etc/security/limits.conf

* soft     nofile         65535
* hard     nofile         65535

# relogin
ulimit -n  # 65535

pv tokens.txt \
	| navec-train cooc count vocab.txt --memory 7 --window 10 \
	&gt; cooc.bin

# Monitor
ls /tmp/cooc_*
tail -c 16 cooc.bin | navec-train cooc parse

navec-train s3 upload cooc.bin librusec_cooc.bin
navec-train s3 upload cooc.bin taiga_proza_cooc.bin
navec-train s3 upload cooc.bin wiki_cooc.bin
navec-train s3 upload cooc.bin news_cooc.bin

Merge (did not give much boost compared to plain librusec, so all_vocab.txt, all_cooc.bin not used below)

for i in librusec taiga_proza wiki news; do
	navec-train s3 download $i_vocab.txt;
	navec-train s3 download $i_cooc.bin;
done

navec-train merge vocabs \
	librusec_vocab.txt \
	taiga_proza_vocab.txt \
	wiki_vocab.txt \
	news_vocab.txt \
	| pv &gt; vocab.txt

navec-train merge coocs vocab.txt \
	librusec_cooc.bin:librusec_vocab.txt \
	taiga_proza_cooc.bin:taiga_proza_vocab.txt \
	wiki_cooc.bin:wiki_vocab.txt \
	news_cooc.bin:news_vocab.txt \
	| pv &gt; cooc.bin

navec-train s3 upload vocab.txt all_vocab.txt
navec-train s3 upload cooc.bin all_cooc.bin

Compute embedings

navec-train s3 download librusec_vocab.txt vocab.txt
navec-train s3 download librusec_cooc.bin cooc.bin

navec-train s3 download wiki_vocab.txt vocab.txt
navec-train s3 download wiki_cooc.bin cooc.bin

navec-train s3 download news_vocab.txt vocab.txt
navec-train s3 download news_cooc.bin cooc.bin

pv cooc.bin \
	| navec-train cooc shuffle --memory 15 \
	&gt; shuf_cooc.bin

# Search dim with best score
for i in 100 200 300 400 500 600;
	do navec-train emb shuf_cooc.bin vocab.txt emb_${i}d.txt --dim $i --threads 10 --iterations 2;
done

# 300 has ok score. 400, 500 are a bit better, but too heavy
navec-train emb shuf_cooc.bin vocab.txt emb.txt --dim 300 --threads 16 --iterations 15

navec-train s3 upload emb.txt librusec_emb.txt
navec-train s3 upload emb.txt wiki_emb.txt
navec-train s3 upload emb.txt news_emb.txt

Quantize

navec-train s3 download librusec_emb.txt emb.txt
navec-train s3 download wiki_emb.txt emb.txt
navec-train s3 download news_emb.txt emb.txt

# Search for best compression that has still ok score
for i in 150 100 75 60 50;
	do pv emb.txt | navec-train pq fit $i --sample 100000 --iterations 15 &gt; pq_${i}q.bin;
done

# 100 is &lt;1% worse on eval but much lighter
pv emb.txt | navec-train pq fit 100 --sample 100000 --iterations 20 &gt; pq.bin

navec-train pq pad &lt; pq.bin &gt; t; mv t pq.bin

navec-train s3 upload pq.bin librusec_pq.bin
navec-train s3 upload pq.bin wiki_pq.bin
navec-train s3 upload pq.bin news_pq.bin

Pack

navec-train s3 download librusec_pq.bin pq.bin
navec-train s3 download librusec_vocab.txt vocab.txt

navec-train s3 download news_pq.bin pq.bin
navec-train s3 download news_vocab.txt vocab.txt

navec-train vocab pack &lt; vocab.txt &gt; vocab.bin

navec-train pack vocab.bin pq.bin hudlit_v1_12B_500K_300d_100q
navec-train s3 upload navec_hudlit_v1_12B_500K_300d_100q.tar packs/navec_hudlit_v1_12B_500K_300d_100q.tar

navec-train pack vocab.bin pq.bin news_v1_1B_250K_300d_100q
navec-train s3 upload navec_news_v1_1B_250K_300d_100q.tar packs/navec_news_v1_1B_250K_300d_100q.tar

Publish

navec-train s3 download packs/navec_hudlit_v1_12B_500K_300d_100q.tar
navec-train s3 download packs/navec_news_v1_1B_250K_300d_100q.tar

navec dependencies

numpy

FAQs

What is navec?

Compact high quality word embeddings for russian language. Visit Snyk Advisor to see a full health score report for navec, including popularity, security, maintenance & community analysis.

Is navec popular?

The python package navec receives a total of 3,063 weekly downloads. As such, navec popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is navec well maintained?

We found indications that navec is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is navec safe to use?

While scanning the latest version of navec, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 26 December-2024, at 19:46 (UTC).

Build a secure application checklist

Select a recommended open source package

Minimize your risk by selecting secure & well maintained open source packages

DONE

Scan your app for vulnerabilities

Scan your application to find vulnerabilities in your: source code, open source dependencies, containers and configuration files

SCAN NOW

Fix identified vulnerabilities

Easily fix your code by leveraging automatically generated PRs

AUTO FIX

Monitor for new issues

New vulnerabilities are discovered every day. Get notified if your application is affected

MONITOR APP

navec

Compact high quality word embeddings for russian language For more information about how to use this package see README

Ensure you're using the healthiest python packages

Package Health Score

Popularity

Direct Usage Popularity

Security

Security and license risk for latest version

Maintenance

Commit Frequency

Community

Embed Package Health Score Badge

Keep your project healthy

Check your requirements.txt

Snyk Vulnerability Scanner

Package

Readme

Downloads

Installation

Usage

Evaluation

Support

Development

navec dependencies

FAQs

What is navec?

Is navec popular?

Is navec well maintained?

Is navec safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

Embed Package Health Score Badge

Keep your project healthy

Check your requirements.txt

Snyk Vulnerability Scanner

Readme

Downloads

Installation

Usage

Evaluation

Support

Development

navec dependencies

FAQs

What is navec?

Is navec popular?

Is navec well maintained?

Is navec safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

Monitor for new issues