Gensim vs Spark NLP

Overview

Gensim

Stacks75

Followers91

Votes0

Spark NLP

Stacks28

Followers38

Votes0

GitHub Stars4.1K

Forks733

Gensim vs Spark NLP: What are the differences?

Introduction

Gensim and Spark NLP are both popular libraries used for natural language processing (NLP) tasks. While they have similar goals of dealing with text data, there are several key differences between the two.

Ease of use: Gensim is known for its simplicity and ease of use. It provides a user-friendly interface and straightforward APIs, making it a popular choice for beginners in NLP. On the other hand, Spark NLP is a more complex and powerful library that requires working with distributed computing frameworks like Apache Spark. It is better suited for large-scale NLP tasks and data processing.
Performance and Scalability: Spark NLP, being built on top of Apache Spark, offers better performance and scalability compared to Gensim. Spark NLP leverages distributed computing capabilities, making it capable of handling large datasets and processing tasks in parallel. Gensim, although efficient for smaller datasets, may face performance limitations when dealing with massive amounts of text data.
Supported NLP Tasks: Gensim is primarily focused on topic modeling, document similarity, and word vector representations. It provides implementations of popular algorithms like Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). On the other hand, Spark NLP offers a wide range of NLP tasks, including tokenization, named entity recognition, part-of-speech tagging, sentiment analysis, and more. It provides a comprehensive suite of pre-trained models for these tasks.
Language Support: Gensim supports a wide range of languages, allowing users to perform NLP tasks on text data in various languages. It provides the flexibility to train models on specific language contexts. Spark NLP, on the other hand, primarily focuses on English language support. Although efforts have been made to expand language support, it may not be as comprehensive as Gensim for languages other than English.
Integration with Other Frameworks: Gensim provides seamless integration with other Python libraries like NumPy and SciPy, making it convenient for data pre-processing and analysis. It is also compatible with popular text processing tools like NLTK. Spark NLP, being built on top of Apache Spark, integrates well with the Spark ecosystem and allows for distributed data processing. It can be seamlessly integrated with other Spark-based tools and frameworks.
Community and Development: Gensim has a vibrant community of users and contributors, continually improving and expanding its functionalities. It also has extensive documentation and a range of online resources, including tutorials and sample code. Spark NLP, being a part of the larger Apache Spark community, benefits from the active development and adoption of the Spark ecosystem. It also has an active community that contributes to its development and provides support.

In Summary, Gensim and Spark NLP differ in terms of ease of use, performance, supported NLP tasks, language support, integration with other frameworks, and community and development.

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Detailed Comparison

Gensim	Spark NLP
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.	It is a Natural Language Processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines that scale easily in a distributed environment. It comes with 160+ pretrained pipelines and models in more than 20+ languages.
platform independent; converters & I/O formats	Tokenization; Stop Words Removal; Normalizer; Stemmer; Lemmatizer; NGrams; Regex Matching; Text Matching; Chunking; Date Matcher; Part-of-speech tagging; Sentence Detector; Dependency parsing (Labeled/unlabled); Sentiment Detection (ML models); Spell Checker (ML and DL models); Word Embeddings (GloVe and Word2Vec); BERT Embeddings; ELMO Embeddings; Universal Sentence Encoder Sentence Embeddings; Chunk Embeddings
Statistics
GitHub Stars -	GitHub Stars 4.1K
GitHub Forks -	GitHub Forks 733
Stacks 75	Stacks 28
Followers 91	Followers 38
Votes 0	Votes 0
Integrations
Python Windows macOS	Python Java Scala TensorFlow

What are some alternatives to Gensim, Spark NLP?

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.

SpaCy

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.

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.

Related Comparisons

Gensim vs Spark NLP: What are the differences?

Introduction

Ease of use: Gensim is known for its simplicity and ease of use. It provides a user-friendly interface and straightforward APIs, making it a popular choice for beginners in NLP. On the other hand, Spark NLP is a more complex and powerful library that requires working with distributed computing frameworks like Apache Spark. It is better suited for large-scale NLP tasks and data processing.
Performance and Scalability: Spark NLP, being built on top of Apache Spark, offers better performance and scalability compared to Gensim. Spark NLP leverages distributed computing capabilities, making it capable of handling large datasets and processing tasks in parallel. Gensim, although efficient for smaller datasets, may face performance limitations when dealing with massive amounts of text data.
Supported NLP Tasks: Gensim is primarily focused on topic modeling, document similarity, and word vector representations. It provides implementations of popular algorithms like Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). On the other hand, Spark NLP offers a wide range of NLP tasks, including tokenization, named entity recognition, part-of-speech tagging, sentiment analysis, and more. It provides a comprehensive suite of pre-trained models for these tasks.
Language Support: Gensim supports a wide range of languages, allowing users to perform NLP tasks on text data in various languages. It provides the flexibility to train models on specific language contexts. Spark NLP, on the other hand, primarily focuses on English language support. Although efforts have been made to expand language support, it may not be as comprehensive as Gensim for languages other than English.
Integration with Other Frameworks: Gensim provides seamless integration with other Python libraries like NumPy and SciPy, making it convenient for data pre-processing and analysis. It is also compatible with popular text processing tools like NLTK. Spark NLP, being built on top of Apache Spark, integrates well with the Spark ecosystem and allows for distributed data processing. It can be seamlessly integrated with other Spark-based tools and frameworks.
Community and Development: Gensim has a vibrant community of users and contributors, continually improving and expanding its functionalities. It also has extensive documentation and a range of online resources, including tutorials and sample code. Spark NLP, being a part of the larger Apache Spark community, benefits from the active development and adoption of the Spark ecosystem. It also has an active community that contributes to its development and provides support.

In Summary, Gensim and Spark NLP differ in terms of ease of use, performance, supported NLP tasks, language support, integration with other frameworks, and community and development.

Gensim vs Spark NLP

Overview

Gensim vs Spark NLP: What are the differences?