EasyNLP vs UBIAI

Overview

UBIAI

Stacks2

Followers11

Votes0

EasyNLP

Stacks1

Followers3

Votes0

GitHub Stars2.2K

Forks257

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An advanced language model comprising 67 billion parameters

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Detailed Comparison

UBIAI	EasyNLP
It is an efficient and easy-to-use text annotation tool for Natural Language Processing (NLP) applications. With this, you can train an NLP model in few hours by collaborating with team members and using the machine learning auto-annotation feature.	It is an easy-to-use NLP development and application toolkit in PyTorch, first released inside Alibaba in 2021. It is built with scalable distributed training strategies and supports a comprehensive suite of NLP algorithms for various NLP applications.
Multi-format document upload: TXT, CSV , JSON , PDF, DOC, HTML; Multilingual: English, French, German, Arabic, Spanish, etc…; Dictionary/Regex auto-annotation: input a list of words or regex patterns along with their associated entities. The tool will automatically scan the documents and auto-annotate; ML auto-annotation: Train an NER model to auto-annotate your documents; Bias detection: visualize entity and word distribution across your documents to detect skewed annotation toward specific entities. Collaboration: Share annotation tasks among team members and monitor progress; Annotation format export: JSON, IOB, Amazon Comprehend, Stanford CoreNLP	Easy to use and highly customizable; Compatible with open-source libraries; Knowledge-injected pre-training; Landing large pre-trained models
Statistics
GitHub Stars -	GitHub Stars 2.2K
GitHub Forks -	GitHub Forks 257
Stacks 2	Stacks 1
Followers 11	Followers 3
Votes 0	Votes 0
Integrations
No integrations available	PyTorch Python

What are some alternatives to UBIAI, EasyNLP?

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.