FinBERT-QA VS TextFooler

Compare FinBERT-QA vs TextFooler and see what are their differences.

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FinBERT-QA TextFooler
1 1
113 465
- -
0.0 0.0
11 months ago over 1 year ago
Python Python
- 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.

FinBERT-QA

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

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 FinBERT-QA and TextFooler you can also consider the following projects:

haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

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

BERT-QE - Code and resources for the paper "BERT-QE: Contextualized Query Expansion for Document Re-ranking".

nli4ct

happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.

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

kiri - Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

nlp-recipes - Natural Language Processing Best Practices & Examples

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

ARElight - Granular Viewer of Sentiments Between Entities in Massively Large Documents and Collections of Texts, powered by AREkit

kiri - Kiri is a visual tool designed for reviewing schematics and layouts of KiCad projects that are version-controlled with Git.