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https://github.com/commonsense/conceptnet-numberbatch


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README 

        
![ConceptNet Numberbatch](conceptnet-numberbatch.png)



## The best pre-computed word embeddings you can use



ConceptNet Numberbatch is a set of semantic vectors (also known as word embeddings)

that can be used directly as a representation of word meanings or as a starting point

for further machine learning.



ConceptNet Numberbatch is part of the [ConceptNet](http://conceptnet.io) open

data project. ConceptNet is a knowledge graph that provides lots of ways to

compute with word meanings, one of which is word embeddings, while ConceptNet

Numberbatch is a snapshot of just the word embeddings.



These embeddings benefit from the fact that they have semi-structured, common

sense knowledge from ConceptNet, giving them a way to learn about words that

isn't _just_ observing them in context.



Numberbatch is built using an ensemble that combines data from ConceptNet, word2vec,

GloVe, and OpenSubtitles 2016, using a variation on retrofitting. It is

described in the paper [ConceptNet 5.5: An Open Multilingual Graph of General

Knowledge][cn55-paper], presented at AAAI 2017.



Unlike most embeddings, ConceptNet Numberbatch is **multilingual** from the

ground up.  Words in different languages share a common semantic space, and

that semantic space is informed by all of the languages.



### Evaluation and publications



ConceptNet Numberbatch can be seen as a replacement for other precomputed

embeddings, such as word2vec and GloVe, that do not include the graph-style

knowledge in ConceptNet. Numberbatch outperforms these datasets on benchmarks

of word similarity.



ConceptNet Numberbatch took first place in both subtasks at SemEval 2017 task

2, "[Multilingual and Cross-lingual Semantic Word Similarity][semeval17-2]".

Within that task, it was also the first-place system in each of English,

German, Italian, and Spanish. The result is described in our ACL 2017 SemEval

paper, "[Extending Word Embeddings with Multilingual Relational Knowledge][semeval-paper]".



[cn55-paper]: https://arxiv.org/abs/1612.03975

[semeval17-2]: http://alt.qcri.org/semeval2017/task2/

[semeval-paper]: https://arxiv.org/abs/1704.03560



The code and papers were created as a research project of [Luminoso

Technologies, Inc.][luminoso], by Robyn Speer, Joshua Chin, Catherine Havasi, and

Joanna Lowry-Duda.



![Graph of performance on English evaluations](eval-graph.png)



### Now with more fairness



Word embeddings are prone to learn human-like stereotypes and prejudices.

ConceptNet Numberbatch 17.04 and later counteract this as part of the build

process, leading to word vectors that are less prejudiced than competitors such

as word2vec and GloVe. See [our blog post on reducing

bias](https://blog.conceptnet.io/2017/04/24/conceptnet-numberbatch-17-04-better-less-stereotyped-word-vectors/).



![Graph of biases](bias-graph.png)



A paper by Chris Sweeney and Maryam Najafian, ["A Transparent Framework for

Evaluating Unintended Demographic Bias in Word Embeddings"][sweeney-paper],

independently evaluates bias in precomputed word embeddings, and finds that

ConceptNet Numberbatch is less likely than GloVe to inherently lead to

demographic discrimination.



[sweeney-paper]: (https://www.aclweb.org/anthology/P19-1162)



## Code



Since 2016, the code for building ConceptNet Numberbatch is part of the [ConceptNet

code base][conceptnet5], in the `conceptnet5.vectors` package.



The only code contained in _this_ repository is `text_to_uri.py`, which

normalizes natural-language text into the ConceptNet URI representation,

allowing you to look up rows in these tables without requiring the entire

ConceptNet codebase. For all other purposes, please refer to the [ConceptNet

code][conceptnet5].



[conceptnet5]: https://github.com/commonsense/conceptnet5



## Out-of-vocabulary strategy



ConceptNet Numberbatch is evaluated with an out-of-vocabulary strategy that

helps its performance in the presence of unfamiliar words. The strategy is

implemented in the [ConceptNet code base][conceptnet5]. It can be summarized

as follows:



- Given an unknown word whose language is not English, try looking up the

  equivalently-spelled word in the English embeddings (because English words

  tend to end up in text of all languages).

- Given an unknown word, remove a letter from the end, and see if that is

  a prefix of known words. If so, average the embeddings of those known words.

- If the prefix is still unknown, continue removing letters from the end until

  a known prefix is found. Give up when a single character remains.



## Downloads



[ConceptNet Numberbatch 19.08][nb1908-main] is the current recommended download.



This table lists the downloads and formats available for multiple recent versions:



| Version  | Multilingual                            | English-only                              | HDF5                         |

| -------- | --------------------------------------- | ----------------------------------------- | ---------------------------- |

| **19.08**| [numberbatch-19.08.txt.gz][nb1908-main] | [numberbatch-en-19.08.txt.gz][nb1908-en]  | [19.08/mini.h5][nb1908-mini] |

| 17.06    | [numberbatch-17.06.txt.gz][nb1706-main] | [numberbatch-en-17.06.txt.gz][nb1706-en]  | [17.06/mini.h5][nb1706-mini] |

| 17.04    | [numberbatch-17.04.txt.gz][nb1704-main] | [numberbatch-en-17.04b.txt.gz][nb1704-en] | [17.05/mini.h5][nb1704-mini] |

| 17.02    | [numberbatch-17.02.txt.gz][nb1704-main] | [numberbatch-en-17.02.txt.gz][nb1702-en]  |                              |

| 16.09    |                                         |                                           | [16.09/numberbatch.h5][nb1609-h5] |



The 16.09 version was the version published at AAAI 2017. You can reproduce its results using a Docker snapshot of the conceptnet5 repository.

See the instructions on the [ConceptNet wiki](https://github.com/commonsense/conceptnet5/wiki/Running-your-own-copy#reproducing-the-word-embedding-evaluation).



[nb1908-main]: https://conceptnet.s3.amazonaws.com/downloads/2019/numberbatch/numberbatch-19.08.txt.gz

[nb1908-en]: https://conceptnet.s3.amazonaws.com/downloads/2019/numberbatch/numberbatch-en-19.08.txt.gz

[nb1908-mini]: http://conceptnet.s3.amazonaws.com/precomputed-data/2016/numberbatch/19.08/mini.h5



[nb1706-main]: https://conceptnet.s3.amazonaws.com/downloads/2017/numberbatch/numberbatch-17.06.txt.gz

[nb1706-en]: https://conceptnet.s3.amazonaws.com/downloads/2017/numberbatch/numberbatch-en-17.06.txt.gz

[nb1706-mini]: http://conceptnet.s3.amazonaws.com/precomputed-data/2016/numberbatch/17.06/mini.h5



[nb1704-main]: https://conceptnet.s3.amazonaws.com/downloads/2017/numberbatch/numberbatch-17.04.txt.gz

[nb1704-en]: https://conceptnet.s3.amazonaws.com/downloads/2017/numberbatch/numberbatch-en-17.04b.txt.gz

[nb1704-mini]: http://conceptnet.s3.amazonaws.com/precomputed-data/2016/numberbatch/17.05/mini.h5



[nb1702-main]: http://conceptnet.s3.amazonaws.com/downloads/2017/numberbatch/numberbatch-17.02.txt.gz

[nb1702-en]: http://conceptnet.s3.amazonaws.com/downloads/2017/numberbatch/numberbatch-en-17.02.txt.gz



[nb1609-h5]: http://conceptnet.s3.amazonaws.com/precomputed-data/2016/numberbatch/16.09/numberbatch.h5



The .txt.gz files of term vectors are in the text format used by word2vec, GloVe, and fastText.



The first line of the file contains the dimensions of the matrix:



    9161912 300



Each line contains a term label followed by 300 floating-point numbers,

separated by spaces:



    /c/en/absolute_value -0.0847 -0.1316 -0.0800 -0.0708 -0.2514 -0.1687 -...

    /c/en/absolute_zero 0.0056 -0.0051 0.0332 -0.1525 -0.0955 -0.0902 0.07...

    /c/en/absoluteless 0.2740 0.0718 0.1548 0.1118 -0.1669 -0.0216 -0.0508...

    /c/en/absolutely 0.0065 -0.1813 0.0335 0.0991 -0.1123 0.0060 -0.0009 0...

    /c/en/absolutely_convergent 0.3752 0.1087 -0.1299 -0.0796 -0.2753 -0.1...



The HDF5 files are the format that ConceptNet uses internally. They are data

tables that can be loaded into Python using a library such as `pandas` or

`pytables`.



The "mini.h5" files trade off a little bit of accuracy for a lot of

memory savings, taking up less than 150 MB in RAM, and are used to power the

[ConceptNet API](https://github.com/commonsense/conceptnet5/wiki/API).



## License and attribution



These vectors are distributed under the [CC-By-SA 4.0][cc-by-sa] license. In

short, if you distribute a transformed or modified version of these vectors,

you must release them under a compatible Share-Alike license and give due

credit to [Luminoso][luminoso].



Some suggested text:



    This data contains semantic vectors from ConceptNet Numberbatch, by

    Luminoso Technologies, Inc. You may redistribute or modify the

    data under the terms of the CC-By-SA 4.0 license.



[cc-by-sa]: https://creativecommons.org/licenses/by-sa/4.0/

[luminoso]: http://luminoso.com



If you build on this data, you should cite it. Here is a straightforward citation:



> Robyn Speer, Joshua Chin, and Catherine Havasi (2017). "ConceptNet 5.5: An Open Multilingual Graph of General Knowledge." In proceedings of AAAI 2017.



In BibTeX form, the citation is:



    @inproceedings{speer2017conceptnet,

        title = {{ConceptNet} 5.5: An Open Multilingual Graph of General Knowledge},

        url = {http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14972},

        author = {Speer, Robyn and Chin, Joshua and Havasi, Catherine},

        year = {2017},

        pages = {4444--4451}

    }



This data is itself built on:



  - [ConceptNet 5.7][conceptnet], which contains data from Wiktionary,

    WordNet, and many contributors to Open Mind Common Sense projects,

    edited by Robyn Speer



  - [GloVe][glove], by Jeffrey Pennington, Richard Socher, and Christopher

    Manning



  - [word2vec][], by Tomas Mikolov and Google Research



  - Parallel text from [OpenSubtitles 2016][opensubtitles], by Pierre Lison

    and Jörg Tiedemann, analyzed using [fastText][], by Piotr Bojanowski,

    Edouard Grave, Armand Joulin, and Tomas Mikolov



[conceptnet]: http://conceptnet.io/

[glove]: http://nlp.stanford.edu/projects/glove/

[word2vec]: https://code.google.com/archive/p/word2vec/

[opensubtitles]: http://opus.lingfil.uu.se/OpenSubtitles2016.php

[fastText]: https://github.com/facebookresearch/fastText



## Language statistics



The multilingual data in ConceptNet Numberbatch represents 78 different language

codes, though some have vocabularies with much more coverage than others. The following

table lists the languages and their vocabulary size.



You may notice a focus on even the smaller and historical languages of Europe,

and under-representation of widely-spoken languages from outside Europe, which

is an effect of the availability of linguistic resources for these languages.

We would like to change this, but it requires finding good source data for

ConceptNet in these under-represented languages.



Because Numberbatch contains word forms, inflected languages end up with larger

vocabularies.



These vocabulary sizes were updated for ConceptNet Numberbatch 19.08.



| code | language                       | vocab size |

|:-----|:-------------------------------|-----------:|

|   fr | French                         |    1388686 |

|   la | Latin                          |     855294 |

|   es | Spanish                        |     651859 |

|   de | German                         |     594456 |

|   it | Italian                        |     557743 |

|   en | English                        |     516782 |

|   ru | Russian                        |     455325 |

|   zh | Chinese                        |     307441 |

|   fi | Finnish                        |     267307 |

|   pt | Portuguese                     |     262904 |

|   ja | Japanese                       |     256648 |

|   nl | Dutch                          |     190221 |

|   bg | Bulgarian                      |     178508 |

|   sv | Swedish                        |     167321 |

|   pl | Polish                         |     152949 |

|   no | Norwegian Bokmål               |     105689 |

|   eo | Esperanto                      |      96255 |

|   th | Thai                           |      95342 |

|   sl | Slovenian                      |      91134 |

|   ms | Malay                          |      90554 |

|   cs | Czech                          |      88613 |

|   ca | Catalan                        |      87508 |

|   ar | Arabic                         |      85325 |

|   hu | Hungarian                      |      74384 |

|   se | Northern Sami                  |      67601 |

|   sh | Serbian                        |      66746 |

|   el | Greek                          |      65905 |

|   gl | Galician                       |      59006 |

|   da | Danish                         |      57119 |

|   fa | Persian                        |      53984 |

|   ro | Romanian                       |      51437 |

|   tr | Turkish                        |      51308 |

|   is | Icelandic                      |      48639 |

|   eu | Basque                         |      44151 |

|   ko | Korean                         |      42106 |

|   vi | Vietnamese                     |      39802 |

|   ga | Irish                          |      36988 |

|  grc | Ancient Greek                  |      36977 |

|   uk | Ukrainian                      |      36851 |

|   lv | Latvian                        |      36333 |

|   he | Hebrew                         |      33435 |

|   mk | Macedonian                     |      33370 |

|   ka | Georgian                       |      32338 |

|   hy | Armenian                       |      29844 |

|   sk | Slovak                         |      29376 |

|   lt | Lithuanian                     |      28826 |

|  ast | Asturian                       |      28401 |

|   mg | Malagasy                       |      26865 |

|   et | Estonian                       |      26525 |

|   oc | Occitan                        |      26095 |

|  fil | Filipino                       |      25088 |

|   io | Ido                            |      25004 |

|  hsb | Upper Sorbian                  |      24852 |

|   hi | Hindi                          |      23538 |

|   te | Telugu                         |      22173 |

|   be | Belarusian                     |      22117 |

|  fro | Old French                     |      21249 |

|   sq | Albanian                       |      20493 |

|  mul | (Multilingual, such as emoji)  |      19376 |

|   cy | Welsh                          |      18721 |

|  xcl | Classical Armenian             |      18420 |

|   az | Azerbaijani                    |      17184 |

|   kk | Kazakh                         |      16979 |

|   gd | Scottish Gaelic                |      16827 |

|   af | Afrikaans                      |      16132 |

|   fo | Faroese                        |      15973 |

|  ang | Old English                    |      15700 |

|   ku | Kurdish                        |      13804 |

|   vo | Volapük                        |      12731 |

|   ta | Tamil                          |      12690 |

|   ur | Urdu                           |      12006 |

|   sw | Swahili                        |      11150 |

|   sa | Sanskrit                       |      11081 |

|  nrf | Norman French                  |      10048 |

|  non | Old Norse                      |       8536 |

|   gv | Manx                           |       8425 |

|   nv | Navajo                         |       8232 |

|  rup | Aromanian                      |       5107 |



## Referred here from an old version?



An unpublished paper of ours described the "ConceptNet Vector Ensemble", and refers to

a repository that now redirects here, and an attached store of data that is no

longer hosted. We apologize, but we're not supporting the unpublished paper.

Please use a newer version and use the currently supported

[ConceptNet build process](https://github.com/commonsense/conceptnet5/wiki/Build-process).



## Image credit



The otter logo was designed by [Christy

Presler](https://thenounproject.com/cnpresler/) for The Noun Project, and is

used under a Creative Commons Attribution license.