ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing. (by dair-ai)
DataAug4NLP
Collection of papers and resources for data augmentation for NLP. (by styfeng)
| ml-visuals | DataAug4NLP | |
|---|---|---|
| 1 | 1 | |
| 11,699 | 816 | |
| 5.2% | - | |
| 1.8 | 0.0 | |
| about 1 year ago | over 1 year ago | |
| 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.
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.
ml-visuals
Posts with mentions or reviews of ml-visuals.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-31.
DataAug4NLP
Posts with mentions or reviews of DataAug4NLP.
We have used some of these posts to build our list of alternatives
and similar projects.
-
[R] A Survey of Data Augmentation Approaches for NLP
Abstract: Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large- scale neural networks that require large amounts of training data. Despite this recent upsurge, this area is still relatively underexplored, perhaps due to the challenges posed by the discrete nature of language data. In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner. We first introduce and motivate data augmentation for NLP, and then discuss major methodologically representative approaches. Next, we highlight techniques that are used for popular NLP applications and tasks. We conclude by outlining current challenges and directions for future research. Overall, our paper aims to clarify the landscape of existing literature in data augmentation for NLP and motivate additional work in this area. We also present a GitHub repository with a paper list that will be continuously updated at this https URL
What are some alternatives?
When comparing ml-visuals and DataAug4NLP you can also consider the following projects:
data-centric-AI - A curated, but incomplete, list of data-centric AI resources.
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
awesome-ai-residency - List of AI Residency Programs
DataAug4Code - Source Code Data Augmentation for Deep Learning: A Survey.