The vecshare python library for word embedding query, selection and download. The vecshare python library uses indexers to regularly poll the data.world datastore for uploaded embeddings, record associated metadata, and generate lightweight signatures representing each uploaded embedding. Users can select embeddings for use by specifying the name of the desired embedding or using provided methods to compare their corpus against indexed signatures and extracting the embedding most similar to the target corpus.

Read more about VecShare: https://bit.ly/VecShare.

Each indexed is evaluated and assigned a score on 10 word pair similarity tasks. The score is calculated by measuring the average Spearman correlation of the word vector cosine similarities and human-rated similarity for each word pair.

Highest Scoring Word Embeddings:

Word Pair Similarity Tasks:

• WS-353: Finkelstein et. al, 2002
• MC-30: Miller and Charles, 1991
• MEN: Bruni et. al, 2012
• MTurk-287: Radinsky et. al, 2011
• MTurk-771: Halawi and Dror, 2012
• Rare-Word: Luong et. al, 2013
• SimLex-999: Hill et. al, 2014
• SimVerb-3500: Gerz et. al, 2016
• Verb-144: Baker et. al, 2014

Install the VecShare Python library:

pip install vecshare

Before using the vecshare library, configure the datadotworld library with your API token. Your token is obtainable on data.world under Settings > Advanced

Set your data.world token using:

dw configure

or

export DW_AUTH_TOKEN=<DATA.WORLD_API_TOKEN>

To avoid resetting the token, add your token as a permanent environment variable to your bash profile. See Advanced Setup for details on creating new indexers or signature methods.

The VecShare Python library currently supports:

• check: See available embeddings
• format: Format an embedding for upload to the data store
• upload: Upload or update an embedding on the datastore
• query: Look up word vectors from a specific embedding
• extract: Download word vectors for only the vocabulary of a specific corpus
• download: Download an entire shared embedding

check(): Returns embeddings available with the current indexer as a queryable pandas.DataFrame.

The default indexer aggregates a set of embeddings by polling data.world hourly for datasets with the tag vecshare-small and vecshare-large. Currently indexed embeddings are viewable at: https://data.world/jaredfern/vecshare-large-indexer.

See Advanced Setup, if you would like to use a custom indexer.

For Example:

>>> from vecshare import vecshare as vs
>>> vs.check()
        embedding_name                              dataset_name contributor  \
0            reutersr8         jaredfern/reuters-word-embeddings   jaredfern   
1         reuters21578         jaredfern/reuters-word-embeddings   jaredfern   
2                brown                    jaredfern/brown-corpus   jaredfern   
3   glove_gigaword100d        jaredfern/gigaword-glove-embedding   jaredfern   
4         oanc_written            jaredfern/oanc-word-embeddings   jaredfern   


   case_sensitive  dimension embedding_type file_format vocab_size
0           False        100       word2vec         csv       7821
1           False        100       word2vec         csv      20203     
2           False        100       word2vec         csv      15062     
3           False        100          glove         csv     399922
4           False        100       word2vec         csv      73127        

Embeddings must be uploaded as a .csv file with a header in the format: ['text', 'd0', 'd1', ... 'd_n'], such that they can be properly indexed and accessed.

format(emb_path,vocab_size=None,dim=None,pca=False,precision=None,sep=","): Formats local embeddings for upload to the data store as needed:

1. A header will be prepended to the file (text, d1, d2, ..., dn)
2. Elements will be delimited with ","
3. Prefix line from plain text word2vec format: Remove "<vocab_size> "

• emb_path(str): Path to embedding
• vocab_size(int,opt): Number of words being retained
• dim(int,opt): Number of dimensions being retained, First dim dimensions preserved unless otherwise specified.
  • pca (bool, opt): If true, retained dimensions will be selected by sklearn-PCA. Calculations are memory intensive and require extensive computation time..
• precision(int,opt): Precision of word vector elements

upload(set_name, emb_path, metadata = {}, summary = None, sep=","): Create a new shared embedding on data.world. Converts embedding to a .csv and creates a header.

• set_name (str): Name of the new dataset on data.world in the form (data.world_username/dataset_name)
• emb_path (str): Path to embedding being uploaded
• metadata (dict, opt): Dictionary containing metadata fields and values as '{metadata_field: value}'
• summary (str, opt): Optional embedding description
• sep (str, opt): Embedding delimiter, defaults to .csv

Alternatively, new embeddings less than 1GB can be added directly to the framework by uploading the embedding as a .csv file to data.world, and tagging the dataset with the tag. Embeddings larger than 1GB must be added through the Python Library and will be tagged as . Large embeddings will be split into multiple appendices to ease upload and query times. The default indexer will add new embedding sets hourly.

Metadata associated with the embedding can be added in the datasets description in the following format, Field: Value

For example:

Embedding Type: word2vec
Token Count: 6000000
Case Sensitive: False

signatures.avgrank(inp_dir): Returns the shared embedding most similar to the user's target corpus, using the AvgRank method described in the VecShare paper. Note: Computation is performed locally. Users' corpora will not be shared with other users

• inp_dir (str): Path to the directory containing the target corpus. signatures.simscore(): Returns the embedding currently scoring highest on the word pair similarity task suite. signatures.maxtkn(): Returns the embedding trained on the most tokens.

>>> from vecshare import signatures as sigs
>>> sigs.avgrank('Test_Input')
u'reutersR8
>>> sig.simscore()
u'gnews_mod'
>>> sig.maxtkn()
u'gnews_mod'

Additional custom similarity and selection methods can be added. See 'Advanced Setup'.

query(words, emb_name, set_name = None, case_sensitive = False): Returns a pandas DataFrame, such that each row specifies a word vector from the query.

• words (list): List of word vectors being requested
• emb_name (str): Title of the embedding containing the requested word vectors
• set_name (str, opt): Specify if multiple embeddings exist with the same emb_name
• case_sensitive (bool): Set to True if word vectors must exactly case match those in words

For Example:

>>> from vecshare import vecshare as vs
>>> vs.query(['The', 'farm'], 'agriculture_40')
   text       d99       d98       d97       d96       d95   ...           d1      d0  
0   the -1.414755  0.414973  1.115698  0.034085  0.542921   ...   0.037287 -1.004704  
1  farm  0.349535 -0.379208 -0.189476  2.776809 -0.099886   ...   0.067443 -1.391604  
[2 rows x 101 columns]

def extract(emb_name, file_dir, set_name = None, download = False): Return a pandas DataFrame containing all available word vectors for the target corpora's vocabulary.

Parameters:

• emb_name (str): Title of the shared embedding
• file_dir (str): Directory containing the user's target corpora
• set_name (str,opt): Specify only if multiple embeddings exist with the same emb_name
• download (bool,opt): If True, the extracted embedding will be saved as a .csv
• case_sensitive (bool): Set to True if word vectors must exactly case match those in words

For example:

>>> from vecshare import vecshare as vs
>>> vs.extract('agriculture_40', 'Test_Input/reutersR8_all')
Embedding extraction begins.
100% (23584 of 23584) |################################| Elapsed Time: 0:01:04
Embedding successfully extracted.

              text       d99       d98       d97       d96       d95    ... \
0        designing -0.194328 -0.229856  0.455848  0.234053 -0.272354    ...
1       affiliated -0.446879 -0.519360  0.130626  0.034608  0.134680    ...
2    appropriately  0.106778  0.057186 -0.222296  0.101948  0.395122    ...
3       cincinnati -0.563716 -0.274534  0.120897  0.273457  0.383307    ...
4           choice  0.689276  1.586349  1.301351 -1.193058 -0.243053    ...
5              han -0.287583  0.237989 -0.141203  0.328414  0.401448    ...
6            begin  1.952841 -1.497073 -0.656650  2.443687  0.315941    ...
7        wednesday -1.591453 -1.419733 -0.758305  2.638620  0.323779    ...
8            wales -0.591623 -0.761353 -0.042557 -0.106776  0.004614    ...
9             much  1.971340 -2.316020  0.147194 -0.641963 -0.280868    ...

            d14       d13       d12       d11       d10        d1         d0
0      0.432226 -0.023887 -0.246207  0.429862  0.268280  0.283950   0.218664   
1      0.702217 -0.516346  0.273179  0.662874  0.106199 -0.011592   0.057832   
2     -0.174151 -0.069734 -0.255887  0.070181 -0.163013  0.093490   0.028913
3     -0.189739 -0.089899 -0.048192  0.569139  0.595834  0.421905  -0.241777
4     -1.085993 -0.054178  1.156616 -1.449286  0.267787  0.677337   2.148856  
5     -0.004664 -0.414933 -0.346377 -0.214976  0.201621  0.063539  -0.331673
6      1.587940 -0.258819  1.396479  0.637493 -1.476619 -0.487518   0.864765    
7      0.190376  0.881103  0.966915  1.543105  1.974099 -0.807656   0.800163  
8     -0.181255  0.005893 -0.718905  0.373082  0.784821  0.393715  -0.000517  
9      1.348299  0.180225  1.686486  0.535154 -2.005099 -1.424234  -2.677770    
[9320 rows x 101 columns]

download(emb_name, set_name=None): Returns the full embedding, containing all uploaded word vectors in the shared embedding and saves the embedding as a .csv file in the current directory. Merges appendices for embeddings.

• emb_name (str): Title of the shared embedding
• set_name (str, opt): Specify if multiple embeddings exist with the same emb_name

For example:

>>> from vecshare import vecshare as vs
>>> vs.download('agriculture_40')
              text        d0        d1        d2        d3        d4  \
0              the  1.477964  0.016078 -0.193995  1.113142  0.765398   
1               of -0.048878 -0.597735  0.196982  0.220966  1.463818   
2               to  1.932197  1.587676 -0.321938 -0.592603  0.137684   
3               in  0.294486  1.061131 -0.119670  0.611166  0.436337   
4             said -0.609932 -0.481854  0.028189  0.755433 -0.493351   
5                a  0.750953  0.342545 -0.758257  0.381944  0.824879   
6              and  0.991821 -0.252496  0.011951  0.384948  0.505785   
7              mln  0.215208  3.330005  0.458480  0.484309  1.128098   
8               vs  0.512198  3.565070 -1.698517  0.813855 -0.002396   
9             dlrs -0.026384  1.905773  1.313683  0.825797  1.981671

Additional signature methods can be included in the library by downloading the library and adding to the signatures.py file. To incorporate new signatures into future releases of the official VecShare library, fork and merge your changes with the github repository.

Custom indexers can be added by updating the indexer.py file.

INDEXER      = <NEW INDEXER DATASET ID>
INDEX_FILE   = <NAME OF THE INDEX FILE>
EMB_TAG      = <EMB TAG>

This research was supported in part by NSF grant IIS-1351029 and the Allen Institute for Artificial Intelligence.