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prose vs Transformers: What are the differences?

Developers describe prose as "A Golang library for text processing". prose is a natural language processing library (English only, at the moment) in pure Go. It supports tokenization, segmentation, part-of-speech tagging, and named-entity extraction. On the other hand, Transformers is detailed as "State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0". 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.

prose and Transformers can be primarily classified as "NLP / Sentiment Analysis" tools.

Some of the features offered by prose are:

  • Tokenizing
  • Segmenting
  • Tagging, NER

On the other hand, Transformers provides the following key features:

  • High performance on NLU and NLG tasks
  • Low barrier to entry for educators and practitioners
  • Deep learning researchers

prose is an open source tool with 2.5K GitHub stars and 116 GitHub forks. Here's a link to prose's open source repository on GitHub.

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What is prose?

prose is a natural language processing library (English only, at the moment) in pure Go. It supports tokenization, segmentation, part-of-speech tagging, and named-entity extraction.

What is 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.

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    What are some alternatives to prose and Transformers?
    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.
    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.
    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.
    Amazon Comprehend
    Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to discover insights from text. Amazon Comprehend provides Keyphrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection APIs so you can easily integrate natural language processing into your applications.
    Google Cloud Natural Language API
    You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage.
    See all alternatives