The Python Flair JSON-NLP package
Flair JSON-NLP Wrapper
Brought to you by the NLP-Lab.org!
We provide a
FlairPipeline class, with the following parameters for ease of use:
lang: defaults to
en. Different languages support different models, see the Flair Docs for details.
use_ontonotes: defaults to
False. Whether or not to use 4-class (True) or 12-class (False) NER tagging.
fast: defaults to
True. Whether or not to use the smaller, faster, but slightly less accurate versions of the models.
use_embeddings: defaults to ''. Passing
defaultwill map to
glove,multi-forward,multi-backward, the recommended stacked-embedding configuration.
char_embeddings: defaults to
False. Whether or not to include character-level embeddings.
bpe_size: defaults to 0. If you want to include Byte-Pair Encodings, set this value to 50, 100, 200, or 300. See more at Flair Embeddings Docs.
pos: defaults to
True. Whether or not to include language-specific part-of-speech tags.
sentinment: defaults to
True. Whether or not to include sentiment analysis, if it is available for the given language.
Tagging and Embedding models are downloaded automatically the first time they are called. This may take a while depending on your internet connection.
server.py extends the Flask app, a WSGI file would contain:
from flairjsonnlp.server import app as application
The microservice exposes the following URIs:
These URIs are shortcuts to disable the other components of the parse. In all cases,
tokenList will be included in the
JSON-NLP output. An example url is:
http://localhost:5000/expressions?text=I am a sentence
Text is provided to the microservice with the
text parameter, via either
POST. If you pass
url as a parameter, the microservice will scrape that url and process the text of the website.
The additional Flair parameters can be passed as parameters as well.
Here is an example
http://localhost:5000?lang=de&constituents=0&text=Ich bin ein Berliner.
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