Spark NLP vs rasa NLU

Overview

rasa NLU

Stacks120

Followers282

Votes25

Spark NLP

Stacks28

Followers38

Votes0

GitHub Stars4.1K

Forks733

Spark NLP vs rasa NLU: What are the differences?

Introduction

In this markdown, we will compare the key differences between Spark NLP and Rasa NLU, two popular natural language processing (NLP) frameworks. Spark NLP is a powerful library built on Apache Spark for processing text data, while Rasa NLU is an open-source library used for intent recognition and entity extraction in conversational AI systems.

Scalability: Spark NLP is designed to handle large-scale NLP tasks efficiently by leveraging the distributed computing capabilities of Apache Spark. It allows processing of massive amounts of text data in parallel, making it suitable for big data use cases. On the other hand, Rasa NLU is not specifically designed for distributed computing and may face scalability challenges when dealing with large volumes of text data.
Feature Set: Spark NLP provides a comprehensive set of pre-trained models and pipelines for various NLP tasks such as text classification, named entity recognition, sentiment analysis, and more. It also offers a wide range of pre-processing and transformation functions to clean and prepare text data for analysis. In contrast, Rasa NLU focuses more on intent recognition and entity extraction, providing a simpler set of features compared to Spark NLP.
Integration with Conversational AI Frameworks: Rasa NLU is primarily developed as a core component of the Rasa framework, which is a complete open-source platform for building AI assistants and chatbots. It seamlessly integrates with the Rasa stack, allowing easy development and deployment of conversational AI systems. Spark NLP, on the other hand, is a standalone library that can be integrated with various platforms but does not provide a complete conversational AI framework like Rasa.
Customization and Training: Both Spark NLP and Rasa NLU allow customization of their pre-trained models and training of new models with domain-specific data. However, Spark NLP provides a more extensive set of pre-trained models and allows fine-tuning of these models using transfer learning techniques. It offers more flexibility in model architecture and training options compared to Rasa NLU.
Development and Community Support: Spark NLP is backed by a large community of developers and researchers, as well as industry support from John Snow Labs. It has gained popularity in the industry and is actively maintained with regular updates and new features. Rasa NLU also has a strong community support, especially within the Rasa ecosystem, but may have a comparatively smaller community and fewer resources available.
Use Cases: Due to its scalability and feature-richness, Spark NLP is particularly well-suited for large-scale NLP use cases such as text analysis in big data environments, document classification, sentiment analysis on social media data, and more. Rasa NLU, on the other hand, is primarily designed for building conversational AI systems such as chatbots and virtual assistants, focusing on intent recognition and entity extraction in user queries.

In summary, Spark NLP excels in scalability, feature set, and integration capabilities with various platforms, making it suitable for big data NLP use cases, while Rasa NLU focuses on intent recognition and entity extraction for conversational AI systems within the Rasa framework.

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Detailed Comparison

rasa NLU	Spark NLP
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.	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.
Open source; NLP; Machine learning	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 120	Stacks 28
Followers 282	Followers 38
Votes 25	Votes 0
Pros & Cons
Pros 9 Open Source 6 Docker Image 6 Self Hosted 3 Comes with rasa_core 1 Enterprise Ready Cons 4 Wdfsdf 4 No interface provided	No community feedback yet
Integrations
Slack RocketChat Google Hangouts Chat Telegram Microsoft Bot Framework Twilio Mattermost	Python Java Scala TensorFlow

What are some alternatives to rasa NLU, Spark NLP?

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.

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

Spark NLP vs rasa NLU: What are the differences?

Introduction

Scalability: Spark NLP is designed to handle large-scale NLP tasks efficiently by leveraging the distributed computing capabilities of Apache Spark. It allows processing of massive amounts of text data in parallel, making it suitable for big data use cases. On the other hand, Rasa NLU is not specifically designed for distributed computing and may face scalability challenges when dealing with large volumes of text data.
Feature Set: Spark NLP provides a comprehensive set of pre-trained models and pipelines for various NLP tasks such as text classification, named entity recognition, sentiment analysis, and more. It also offers a wide range of pre-processing and transformation functions to clean and prepare text data for analysis. In contrast, Rasa NLU focuses more on intent recognition and entity extraction, providing a simpler set of features compared to Spark NLP.
Integration with Conversational AI Frameworks: Rasa NLU is primarily developed as a core component of the Rasa framework, which is a complete open-source platform for building AI assistants and chatbots. It seamlessly integrates with the Rasa stack, allowing easy development and deployment of conversational AI systems. Spark NLP, on the other hand, is a standalone library that can be integrated with various platforms but does not provide a complete conversational AI framework like Rasa.
Customization and Training: Both Spark NLP and Rasa NLU allow customization of their pre-trained models and training of new models with domain-specific data. However, Spark NLP provides a more extensive set of pre-trained models and allows fine-tuning of these models using transfer learning techniques. It offers more flexibility in model architecture and training options compared to Rasa NLU.
Development and Community Support: Spark NLP is backed by a large community of developers and researchers, as well as industry support from John Snow Labs. It has gained popularity in the industry and is actively maintained with regular updates and new features. Rasa NLU also has a strong community support, especially within the Rasa ecosystem, but may have a comparatively smaller community and fewer resources available.
Use Cases: Due to its scalability and feature-richness, Spark NLP is particularly well-suited for large-scale NLP use cases such as text analysis in big data environments, document classification, sentiment analysis on social media data, and more. Rasa NLU, on the other hand, is primarily designed for building conversational AI systems such as chatbots and virtual assistants, focusing on intent recognition and entity extraction in user queries.

Spark NLP vs rasa NLU

Overview

Spark NLP vs rasa NLU: What are the differences?