SpaCy vs Stanza

Overview

SpaCy

Stacks220

Followers301

Votes14

GitHub Stars32.8K

Forks4.6K

Stanza

Stacks9

Followers34

Votes0

GitHub Stars7.6K

Forks926

SpaCy vs Stanza: What are the differences?

Licensing: SpaCy and Stanza have different licensing models. SpaCy is released under the MIT license, which allows users to freely use, modify, and distribute the software. On the other hand, Stanza is released under the Apache 2.0 license, which also permits users to use, modify, and distribute the software, but it includes additional requirements such as giving credit to the original authors and including the license agreement.
Language Support: Both SpaCy and Stanza support a wide range of languages. However, SpaCy initially focused on English language processing and has gradually added support for other languages. Stanza, on the other hand, is built with multilingual support in mind from the start, offering a larger number of languages out-of-the-box.
Dependency Parsing: SpaCy and Stanza provide different approaches to dependency parsing. SpaCy uses a statistical model based on transition-based parsing algorithms, which are fast and accurate but can struggle with certain linguistic phenomena. Stanza, in contrast, uses a neural network-based method called graph-based parsing, which captures long-range dependencies and performs well on various languages and sentence structures.
Pretrained Models: SpaCy and Stanza offer pretrained models for various NLP tasks. However, SpaCy's pretrained models are generally smaller in size and faster to load compared to Stanza's models. This makes SpaCy a good choice for cases where efficiency is crucial, while Stanza's larger models can be advantageous for tasks that require more advanced linguistic analysis.
Integration with Other Libraries: SpaCy and Stanza provide different levels of integration with other libraries and frameworks. SpaCy has a more extensive ecosystem of compatible libraries, such as scikit-learn and TensorFlow, making it easier to integrate with existing machine learning workflows. Stanza, on the other hand, offers seamless integration with the PyTorch ecosystem, which can be beneficial for users already working with PyTorch-based models and frameworks.
Extension and Customization: Both SpaCy and Stanza allow users to extend and customize their functionalities. However, SpaCy has a more mature and user-friendly API for creating custom components, pipelines, and annotations. Stanza, on the other hand, provides more flexibility in terms of customization by allowing users to define their own neural network architectures and training procedures.

In Summary, SpaCy and Stanza differ in their licensing models, language support, dependency parsing approaches, pretrained models, integration with other libraries, and extension/customization capabilities.

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Detailed Comparison

SpaCy	Stanza
It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.	It is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism.
-	Native Python implementation requiring minimal efforts to set up; Full neural network pipeline for robust text analytics, including tokenization, multi-word token (MWT) expansion, lemmatization, part-of-speech (POS) and morphological features tagging, dependency parsing, and named entity recognition; Pretrained neural models supporting 66 (human) languages; A stable, officially maintained Python interface to CoreNLP
Statistics
GitHub Stars 32.8K	GitHub Stars 7.6K
GitHub Forks 4.6K	GitHub Forks 926
Stacks 220	Stacks 9
Followers 301	Followers 34
Votes 14	Votes 0
Pros & Cons
Pros 12 Speed 2 No vendor lock-in Cons 1 Requires creating a training set and managing training	No community feedback yet
Integrations
No integrations available	Python PyTorch

What are some alternatives to SpaCy, Stanza?

rasa NLU

rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.

Speechly

It can be used to complement any regular touch user interface with a real time voice user interface. It offers real time feedback for faster and more intuitive experience that enables end user to recover from possible errors quickly and with no interruptions.

MonkeyLearn

Turn emails, tweets, surveys or any text into actionable data. Automate business workflows and saveExtract and classify information from text. Integrate with your App within minutes. Get started for free.

Jina

It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.

Sentence Transformers

It provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various tasks.

FastText

It is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices.

CoreNLP

It provides a set of natural language analysis tools written in Java. It can take raw human language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize and interpret dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases or word dependencies, and indicate which noun phrases refer to the same entities.

Flair

Flair allows you to apply our state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and classification.

Transformers

It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.

Gensim

It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.

Related Comparisons

SpaCy vs Stanza: What are the differences?

Licensing: SpaCy and Stanza have different licensing models. SpaCy is released under the MIT license, which allows users to freely use, modify, and distribute the software. On the other hand, Stanza is released under the Apache 2.0 license, which also permits users to use, modify, and distribute the software, but it includes additional requirements such as giving credit to the original authors and including the license agreement.
Language Support: Both SpaCy and Stanza support a wide range of languages. However, SpaCy initially focused on English language processing and has gradually added support for other languages. Stanza, on the other hand, is built with multilingual support in mind from the start, offering a larger number of languages out-of-the-box.
Dependency Parsing: SpaCy and Stanza provide different approaches to dependency parsing. SpaCy uses a statistical model based on transition-based parsing algorithms, which are fast and accurate but can struggle with certain linguistic phenomena. Stanza, in contrast, uses a neural network-based method called graph-based parsing, which captures long-range dependencies and performs well on various languages and sentence structures.
Pretrained Models: SpaCy and Stanza offer pretrained models for various NLP tasks. However, SpaCy's pretrained models are generally smaller in size and faster to load compared to Stanza's models. This makes SpaCy a good choice for cases where efficiency is crucial, while Stanza's larger models can be advantageous for tasks that require more advanced linguistic analysis.
Integration with Other Libraries: SpaCy and Stanza provide different levels of integration with other libraries and frameworks. SpaCy has a more extensive ecosystem of compatible libraries, such as scikit-learn and TensorFlow, making it easier to integrate with existing machine learning workflows. Stanza, on the other hand, offers seamless integration with the PyTorch ecosystem, which can be beneficial for users already working with PyTorch-based models and frameworks.
Extension and Customization: Both SpaCy and Stanza allow users to extend and customize their functionalities. However, SpaCy has a more mature and user-friendly API for creating custom components, pipelines, and annotations. Stanza, on the other hand, provides more flexibility in terms of customization by allowing users to define their own neural network architectures and training procedures.

SpaCy vs Stanza

Overview