ARElight VS TextFooler

Compare ARElight vs TextFooler and see what are their differences.

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ARElight TextFooler
3 1
35 465
- -
8.9 0.0
11 days ago over 1 year ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

ARElight

Posts with mentions or reviews of ARElight. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-19.

TextFooler

Posts with mentions or reviews of TextFooler. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing ARElight and TextFooler you can also consider the following projects:

zshot - Zero and Few shot named entity & relationships recognition

TextAttack - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

DeepKE - [EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction

FinBERT-QA - Financial Domain Question Answering with pre-trained BERT Language Model

nli4ct

ABSA-PyTorch - Aspect Based Sentiment Analysis, PyTorch Implementations. 基于方面的情感分析,使用PyTorch实现。

ccg2lambda - Provide Semantic Parsing solutions and Natural Language Inferences for multiple languages following the idea of the syntax-semantics interface.

typeish - A runtime type checker for bash.... in bash. I'm not sorry.

nlp-recipes - Natural Language Processing Best Practices & Examples

PIXIU - This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).

KitanaQA - KitanaQA: Adversarial training and data augmentation for neural question-answering models