The purpose of the NLPL repository of word vectors/embeddings (which can comprise both ‘classic’, count-based and ‘modern’, dense models) is to make available a large and carefully curated collection of large-scale distributional semantic models for many languages. For general background, please see Fares et al. (2017).
For interactive exploration and download of the repository, there is an on-line explorer. The underlying data is stored in the NLPL project directory below /projects/nlpl/data/vectors/ (on Abel) and /proj/nlpl/data/vectors/ (on Taito). The repository is versioned, in the sense of assigning release numbers to different stages of repository construction. Each repository entry, thus, is assigned a unique and persistent identifier; once published, a repository entry will never change (to aid replicability). The initial release (providing some two dozen models) was published in May 2017 as version 1.0. In March 2018, version 1.1 supersedes this initial release, adding a large number of models and languages (including those from the UD parsing task) and re-packaging the models from the original release in a more standardized format (see below).
The on-line browser dynamically presents parts of the information encoded for programmatic access in the repository catalogue, which is represented as a JSON file in the top-level repository directory, with catalogue names corresponding to each repository version, e.g. /projects/nlpl/data/vectors/10.json (on Abel) for the initial repository release.
The catalogue contains three top-level sections, one each for corpora (data sources), algorithms (model creation tools), and models (resulting sets of word vectors). NLPL users with access to Abel and Taito can read the catalogue file directly from the project directory, for example when executing a series of experiments that make use of different pre-trained sets of word vectors.
Each repository entry (i.e. set of word vectors, or ‘model’) is packaged in the form of a .zip archive, with uniform conventions for file naming inside the file, using the model.txt and model.bin entries for the actual vectors. Each archive includes the relevant excerpts from the catalogue as a file meta.json to help identify the specific contents; a README file included with each model entry provides a life-time unique identifier, e.g. http://vectors.nlpl.eu/repository/11/3.zip for model #3 in the 1.1 release of the repository.
Using NLPL Models In-Situ
To avoid data duplication, it is recommended to load models from the NLPL repository directly from the NLPL project directory, when working on Abel or Taito. Repository entries are uniformly packaged as .zip compressed archives, but the uniform naming scheming makes it possible to directly read one or more of the model files from the archive.
In Python, for example, something along the following lines should work to iterate over all of the entries in the model
import zipfile import gensim repository = "/projects/nlpl/data/vectors/11" with zipfile.ZipFile(repository + "/30.zip", "r") as archive: stream = archive.open("model.txt") for line in stream: ...
Alternatively, if working in a framework like gensim
model = gensim.models.KeyedVectors.load_word2vec_format(stream, binary=False, unicode_errors='replace')
Binary fastText models (stored as parameters.bin files) should be first extracted from the .zip archive, and then loaded with
model = gensim.models.fasttext.load_facebook_vectors("parameters.bin")
- The life-time handle for each model should be included in the JSON catalogue (in addition to being listed in the README file).
- Each corpus should be listed as a separate entry; corpus combinations go into the array-valued corpus property on models (DONE).
- Where applicable, there should be an array-valued documentation field (of string, typically URLs) on corpora and models.
- The maintainers property may be over-promising, as often third-party models are in practice unmaintained; maybe rename to creator?
- Document (and possibly re-design) the metadata scheme; maybe invent a fourth category for a process applied prior model training.