Bangla (Bengali / বাংলা) spaCy models

This repository contains releases of Bengali/Bangla language models for the spaCy NLP library.

⚠️ Important note: Because the models can be very large and consist mostly of binary data, we can't simply provide them as files in a GitHub repository. Instead, we've opted for adding them to releases as .tar.gz files. This allows us to still maintain a public release history.

Releases

spaCy v2.x

Date	Model	Version	Dep	Ent	Vec	Size	License
`2020-09-16`	`bn_core_news_sm`	0.1.0	✘	✔	✔	290 MB	MIT

Model naming conventions

In general, spaCy expects all model packages to follow the naming convention of [lang]_[name]. For our models, we also chose to divide the name into three components:

type: Model capabilities (e.g. core for general-purpose model with vocabulary, syntax, entities and word vectors, or depent for only vocab, syntax and entities)
genre: Type of text the model is trained on (e.g. web for web text, news for news text)
size: Model size indicator (sm, md or lg)

For example, en_depent_web_md is a medium-sized English model trained on written web text (blogs, news, comments), that includes vocabulary, syntax and entities.

Model versioning

Additionally, the model versioning reflects both the compatibility with spaCy, as well as the major and minor model version. A model version a.b.c translates to:

a: spaCy major version. For example, 2 for spaCy v2.x.
b: Model major version. Models with a different major version can't be loaded by the same code. For example, changing the width of the model, adding hidden layers or changing the activation changes the model major version.
c: Model minor version. Same model structure, but different parameter values, e.g. from being trained on different data, for different numbers of iterations, etc.

For a detailed compatibility overview, see the compatibility.json. This is also the source of spaCy's internal compatibility check, performed when you run the download command.

Downloading models

To increase transparency and make it easier to use spaCy with your own models, all data is now available as direct downloads, organised in individual releases. spaCy 1.7 also supports installing and loading models as Python packages. You can now choose how and where you want to keep the data files, and set up "shortcut links" to load models by name from within spaCy. For more info on this, see the new models documentation.

# pip install .tar.gz archive from path or URL
pip install /Users/you/bn_core_news_sm-2.0.0.tar.gz
pip install https://github.com/banglakit/spacy-models/releases/download/bn_core_news_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz

# set up shortcut link to load installed package as "en_default"
python -m spacy link en_core_web_sm en_default

# set up shortcut link to load local model as "my_amazing_model"
python -m spacy link /Users/you/data my_amazing_model

Loading and using models

import it directly and then call its load() method with no arguments.

⚠️ Important note: Because the the Bangla models use a custom SentenceSegmenter, it is dynamically loaded with model.load(). Thus the sentence segmenter will not work if you load with spacy.load('modelname').

import spacy
import bn_core_news_sm

nlp = bn_core_news_sm.load()
doc = nlp(u'আমি বাংলায় গান গাই। তুমি কি গাও?')

Manual download and installation

In some cases, you might prefer downloading the data manually, for example to place it into a custom directory. You can download the model via your browser from the latest releases, or configure your own download script using the URL of the archive file. The archive consists of a model directory that contains another directory with the model data.

└── bn_core_news_sm-2.0.0.tar.gz       # downloaded archive
    ├── meta.json                      # model meta data
    ├── setup.py                       # setup file for pip installation
    └── bn_core_news_sm                # model directory
        ├── __init__.py                # init for pip installation
        ├── meta.json                  # model meta data
        └── bn_core_news_sm-2.0.0      # model data

You can place the model data directory anywhere on your local file system. To use it with spaCy, simply assign it a name by createing a shortcut link for the data directory.

📖 For more info and examples, check out the models documentation.

Issues and bug reports

To report an issue with a model, please open an issue on the spaCy issue tracker. Please note that no model is perfect. Because models are statistical, their expected behaviour will always include some errors. However, particular errors can indicate deeper issues with the training feature extraction or optimisation code. If you come across patterns in the model's performance that seem suspicious, please do file a report.