NeuronBlocks vs Plasticity

Overview

Plasticity

Stacks3

Followers10

Votes0

NeuronBlocks

Stacks2

Followers8

Votes0

GitHub Stars1.5K

Forks193

NeuronBlocks vs Plasticity: What are the differences?

NeuronBlocks: A deep learning modeling toolkit by Microsoft. It is a NLP deep learning modeling toolkit that helps engineers/researchers to build end-to-end pipelines for neural network model training for NLP tasks. The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages; Plasticity: APIs for human-like natural language interfaces. Today's personal assistants and conversational interfaces fail to handle variations in a user's wording or multiple requests in one sentence. We take a language-based semantic approach to handle complex dialogue.

NeuronBlocks and Plasticity belong to "NLP / Sentiment Analysis" category of the tech stack.

NeuronBlocks is an open source tool with 1.08K GitHub stars and 153 GitHub forks. Here's a link to NeuronBlocks's open source repository on GitHub.

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Detailed Comparison

Plasticity	NeuronBlocks
Today's personal assistants and conversational interfaces fail to handle variations in a user's wording or multiple requests in one sentence. We take a language-based semantic approach to handle complex dialogue.	It is a NLP deep learning modeling toolkit that helps engineers/researchers to build end-to-end pipelines for neural network model training for NLP tasks. The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages.
-	Sentence Classification; Sentiment Analysis
Statistics
GitHub Stars -	GitHub Stars 1.5K
GitHub Forks -	GitHub Forks 193
Stacks 3	Stacks 2
Followers 10	Followers 8
Votes 0	Votes 0

What are some alternatives to Plasticity, NeuronBlocks?

rasa NLU

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SpaCy

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Speechly

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MonkeyLearn

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Jina

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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.

Flair

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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.

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.