spaCy Reviews

spaCy supports well, in a modular way, all low levels of text analysis, making easy to add support for new languages.

NER functionality, which understandably is of poor quality, should be kept off the main pipeline.

It provides me with basic support in implementing higher level text analysis functions, for an educational platform addressing multiple languages.

Spacy is basically used for Natural Language Processing(NLP) tasks in Machine Learning. We can optimise our tasks with this library in Python by using pre trained models of Part of Speech(PoS) tagging, Text Summarization and for Named Entity Recognition(NER) model. It also has the capabilities to do tokenization in which sentences can be divided into words and punctuation marks. All in all, It is a very useful library of python to use NLP tasks in multiple domains.

Spacy's library context are somewhat difficult to learn and it may have steep learning curve as the current functions have some much dependency on the previous functions used. Even for custom model training, it is very complex task which may require labeled and annotated data for processing.

As a dats scientist, I build my NLP models with Spacy library in Python which requires tokenization(splitting of words) , Text PreProcessing, Model Building,Model validating and testing. This all processes are covered with Spacy and some other extensive libraries clubbed together.

SpaCy continues to be open source and publicly accessible even with the most modern algorithms. Modern named entity recognition performs flawlessly and tags words into their correct parts of speech quickly and accurately. Over twenty languages can be used with its extensive library.

Setting up SpaCy may present some challenges if you are unfamiliar with Python. This may restrict some of your options. However, that small limitation of personalization shouldn't bother someone who is sincerely interested in research or teaching tools.

It does quick text extraction and enables us to create our own enterprise solution and manage original NLP techniques across a diverse range of languages. It can finally provide you with the resources you need to train your program human language with all of its richness and depth. It is a powerhouse for every deep learning algorithm you could possibly require.

1. Information extraction related to Locations, Names, Nouns, Verbs, etc. from the English text.

2. Pre-trained model which helps in building faster and smoother.

1. Support for languages other than English is not that great.

2. Need to be tech-savvy to extract/perform more complex operations.

Ease of usage is one the most liked things about Spacy. I have been using Spacy for about 2 years as a backend for my data science software & processing natural language queries. The community support is fantastic.

Spacy used to lack transformers pipelines but with version 3.0+, this has been added & as a result it diminishes the things I dislike about spacy.

I have been using Spacy to support my data science apps in order to process natural language more efficiently. As a result, this brings down the cost of human intervention very low.

I like the sentence parser and the quality of sentences I generates. I like the object based approach so it is very easy for making a flow. SpaCy also solve many problems with its trained models that other libraries can't.

Sentence parser is slow and it can be improved.

I am using spaCy for relation extraction, sentiment analysis, Stemming and nearly all NLP problems.

All the functions of the library give near to SOTA performance. And yet, it gives very performance. The API is very easy to use.

The documentation can use some more examples. Also, it's a little inconvenient to use it in tandem with other nlp libraries. It also takes some work to integrate it into a pipeline.

I have used spaCy for many NLP projects. It's fast and decent performance is very helpful.

While Stanford NLP library is good to start with for tackling NLP problems, Spacy gives a nudge to your projects with advanced capabilities which otherwise are too tricky and difficult to master. Spacy exposes methods and APIs which abstracts out all the complexities like Training for custom Named Entities. Or extracting phrases from text. Spacy has proved for us to be very fruitful. To add to that, its blazingly fast when compared to other libraries.

Sometimes taking away complexities from the problem makes the problem less interesting. Just kidding. It has been an amazing experience till now.

Give it a shot for Custom NER. Its open source.

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I loved how easy it was to work the interface. SpaCy is the coolest open-source software library I've used!

I disliked the fact that it took me a while to figure out how to change the font size so that I could see everything a bit better

Honeslty, a go-to product!

SpaCy makes advanced NLP languages like Python and Cython easy to work with, which solves my business issue of collaborating between coders.

Its a very handy library when you want to prepare data for deep learning purposes. Integrates very well with python and is useful in NLP tasks such as POS tagging

The very idea of not having a web interface

Currently, in the learning phase and using it to solve