GAannotations vs Spark NLP
Overview
🔥 Trending in Text & Language Models on StackShare
Detailed Comparison
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. | It is a Chrome extension designed for Google Analytics. It enables the option to add annotations in bulk. |
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 | Understand your data, better and faster; Automate annotations for actions that impacts your data; Add new annotations sources and widen your perspective; Add annotations to Google Analytics, in bulk |
Statistics | |
GitHub Stars 4.1K | GitHub Stars - |
GitHub Forks 733 | GitHub Forks - |
Stacks 28 | Stacks 0 |
Followers 38 | Followers 1 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to Spark NLP, GAannotations?
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.
