Spark NLP vs Transformers

Overview

Transformers

Stacks251

Followers64

Votes0

GitHub Stars152.1K

Forks31.0K

Spark NLP

Stacks28

Followers38

Votes0

GitHub Stars4.1K

Forks733

Spark NLP vs Transformers: What are the differences?

Introduction

In this article, we will explore the key differences between Spark NLP and Transformers. Both Spark NLP and Transformers are popular natural language processing (NLP) libraries used for text analytics and building NLP models. However, there are some significant distinctions between the two.

Architecture: Spark NLP is built on Apache Spark, a distributed computing framework, which allows it to handle large-scale NLP tasks efficiently. On the other hand, Transformers is built on PyTorch and TensorFlow, which are deep learning libraries primarily focused on neural network-based models.
Pre-trained models: Spark NLP provides a wide range of pre-trained models specifically designed for different NLP tasks such as sentiment analysis, named entity recognition, and text classification. Transformers, on the other hand, offers a comprehensive collection of transformer-based pre-trained models, including popular models like BERT, GPT, and RoBERTa, which are primarily used for tasks like text generation, text translation, and question answering.
Pipeline and workflow: Spark NLP follows a pipeline-based approach, where NLP tasks can be assembled in a sequence of stages, allowing for customized data processing and transformation steps. Transformers, on the other hand, provides a straightforward workflow that primarily focuses on fine-tuning pre-trained models and making predictions.
Model interoperability: Spark NLP allows seamless integration with other Apache Spark libraries and can be easily incorporated into existing Spark workflows. In contrast, Transformers, being based on PyTorch and TensorFlow, is more compatible with deep learning frameworks and can leverage their functionalities and ecosystem.
Language support: Spark NLP provides support for a wide range of languages and provides pre-trained models for several languages. Transformers also supports multiple languages, but the availability of pre-trained models may vary.
Community and documentation: Both Spark NLP and Transformers have active communities and regularly release updates. However, given its popularity and wider adoption, Transformers generally has a more extensive community support and documentation.

In summary, Spark NLP and Transformers differ in their architecture, focus on pre-trained models, workflow approach, integration capabilities, language support, and community support.

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Detailed Comparison

Transformers	Spark NLP
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.	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.
High performance on NLU and NLG tasks; Low barrier to entry for educators and practitioners; Deep learning researchers; Hands-on practitioners; AI/ML/NLP teachers and educators	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 152.1K	GitHub Stars 4.1K
GitHub Forks 31.0K	GitHub Forks 733
Stacks 251	Stacks 28
Followers 64	Followers 38
Votes 0	Votes 0
Integrations
TensorFlow PyTorch	Python Java Scala TensorFlow

What are some alternatives to Transformers, 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.

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 Transformers: What are the differences?

Introduction

Architecture: Spark NLP is built on Apache Spark, a distributed computing framework, which allows it to handle large-scale NLP tasks efficiently. On the other hand, Transformers is built on PyTorch and TensorFlow, which are deep learning libraries primarily focused on neural network-based models.
Pre-trained models: Spark NLP provides a wide range of pre-trained models specifically designed for different NLP tasks such as sentiment analysis, named entity recognition, and text classification. Transformers, on the other hand, offers a comprehensive collection of transformer-based pre-trained models, including popular models like BERT, GPT, and RoBERTa, which are primarily used for tasks like text generation, text translation, and question answering.
Pipeline and workflow: Spark NLP follows a pipeline-based approach, where NLP tasks can be assembled in a sequence of stages, allowing for customized data processing and transformation steps. Transformers, on the other hand, provides a straightforward workflow that primarily focuses on fine-tuning pre-trained models and making predictions.
Model interoperability: Spark NLP allows seamless integration with other Apache Spark libraries and can be easily incorporated into existing Spark workflows. In contrast, Transformers, being based on PyTorch and TensorFlow, is more compatible with deep learning frameworks and can leverage their functionalities and ecosystem.
Language support: Spark NLP provides support for a wide range of languages and provides pre-trained models for several languages. Transformers also supports multiple languages, but the availability of pre-trained models may vary.
Community and documentation: Both Spark NLP and Transformers have active communities and regularly release updates. However, given its popularity and wider adoption, Transformers generally has a more extensive community support and documentation.

In summary, Spark NLP and Transformers differ in their architecture, focus on pre-trained models, workflow approach, integration capabilities, language support, and community support.

Spark NLP vs Transformers

Overview

Spark NLP vs Transformers: What are the differences?