![]() ![]() This will be particularly useful if you need to deploy this to a cloud service and forget to download the model manually via the CLI ( like me). You may also notice that we are using the subprocess module mentioned earlier to programmatically call the Spacy CLI inside the application. They provide simple, minimal examples of how to get up and running.īoth endpoints receive POST requests and thus arguments are passed to each endpoint via the request body. If you are new to Flask I recommend checking out their docs quickstart guides. For the gist below make sure to either import the fuzzy matcher and keyword extraction service or declare them in app.py itself. The function then returns a list of all the unique words that ended up in the results variable.įuzzy Matching Implementation Flask ApplicationĪlmost there, all that’s left to do now is to wrap everything up into 2 very simple Flask endpoints. Custom stop words can be appended to this list if needed. Finally, we iterate over all the individual tokens and add those tokens that are in the desired pos_tag set, and are not part of the language models’ default stop words list.Iterating over the docs’ noun chunks and adding a noun chunk to the result if all the chunks’ tokens are in the desired pos_tag list.Adding the special tokens to the final result if they appear in the sequence.Creating the doc object by passing the string sequence through the language model.The code snippet below shows how the function works by: This parameter allows the user to specify a special list of words/phrases that are to be added by default to the output if they exists in the sequence. and an optional list of strings special_tags.sequence the string of words we want to extract keywords from.The keyword extraction function takes 3 arguments: Our language model nlp will be passed as an argument to the extract_keywords() function below to generate the doc object. With the model now downloaded you can load it and create the nlp object: import spacy nlp = spacy.load("en_core_web_sm”) But for now, we can do this in the command line. When we build the flask API we will use python’s inbuilt subprocess package to run this command within the app itself once the service spins up. To download the language model using Spacy’s CLI run the following command in your terminal: Depending on where/how you deploy this model you may be able to use the large model. I chose the small model as I had issues with the size of the large model in memory for Heroku deployment. I will be using the small version of the English Core model. As of today Spacy’s current version 2.2.4 has language models for 10 different languages, all in varying sizes. With Spacy we must first download the language model we would like to use. So with the creation of a document object created via the model we are given access to a number of very useful (and powerful) NLP derived attributes and functions including part-of-speech tags and noun chunks which will be central to the functionality of the keyword extractor. ![]() Can be used out-of-the-box and fine-tuned on more specific data.¹Ī container for accessing linguistic annotations…(and) is an array of token structs² General-purpose pretrained models to predict named entities, part-of-speech tags and syntactic dependencies. Keyword Extraction with Spacyįor the keyword extraction function, we will use two of Spacy’s central ideas- the core language model and document object. You can of course also build any of Spacy’s numerous NLP functions into this API using the same general structure.īefore we start, make sure to run: pip install flask flask-cors spacy fuzzywuzzy to install all the required packages. This lightweight API is intended to be a general purpose keyword service for a number of use cases. how to wrap both of these functions up into REST API endpoints with Flask.how to handle spelling mistakes and find fuzzy matches for a given keyword (token) using fuzzyWuzzy.how to build a simple and robust keyword extraction tool using Spacy.This task is known as keyword extraction and thanks to production grade NLP tools like Spacy it can be achieved in just a couple of lines of Python. If the input text is natural language you most likely don’t want to query your database with every single word - instead, you probably want to choose a set of unique keywords from your input and perform an efficient search using those words or word phrases. Often when dealing with long sequences of text you’ll want to break those sequences up and extract individual keywords to perform a search, or query a database. ![]()
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