spaCy Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

spaCy supports well, in a modular way, all low levels of text analysis, making easy to add support for new languages. Review collected by and hosted on G2.com.

NER functionality, which understandably is of poor quality, should be kept off the main pipeline. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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

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

3. Good Hardware requirement. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Sentence parser is slow and it can be improved. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Sometimes taking away complexities from the problem makes the problem less interesting. Just kidding. It has been an amazing experience till now. Review collected by and hosted on G2.com.

- The very first thing I would like to mention in the best things about spaCy, it's open source.

- This library provides a huge collection of various categories of NLP algorithms which are industry ready which can be trusted and directly brought into picture.

- spaCy supports over 28 languages and handles them very efficiently. Review collected by and hosted on G2.com.

No dislikes for spaCy as I have been using it for a long time and for different purposes. Still have never faced any significant problem. Review collected by and hosted on G2.com.

All the functions of the library give near to SOTA performance. And yet, it gives very performance. The API is very easy to use. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The best thing about spaCy is that it is open-source still gives industry-ready state-of-art algorithms. Also their documentation is sound enough to let a learner independently understand the whole library with any experts advice. Even they provide tons of examples for a newbie to practice. Review collected by and hosted on G2.com.

I have been using this library form a long time and I am truly satisfied by it. Have never faced any crucial issues, yes but I had some minor errors which I was easily able to solve using the public forums. Review collected by and hosted on G2.com.