https://github.com/harshgeek4coder/handling_textual_data_augmentations_nlp_mlsa
This Repository Contains resources for the event organized in Microsoft Learn Student Ambassadors
https://github.com/harshgeek4coder/handling_textual_data_augmentations_nlp_mlsa
Last synced: about 1 year ago
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This Repository Contains resources for the event organized in Microsoft Learn Student Ambassadors
- Host: GitHub
- URL: https://github.com/harshgeek4coder/handling_textual_data_augmentations_nlp_mlsa
- Owner: harshgeek4coder
- Created: 2021-09-22T04:52:51.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-09-22T15:17:39.000Z (over 4 years ago)
- Last Synced: 2025-02-05T00:05:41.807Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 18.6 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Handling_Textual_Data_Augmentations_NLP_MLSA
This Repository Contains resources for the event organized in Microsoft Learn Student Ambassadors Event
### Event Details :
This Workshop would help you explore about performing textual augmentations for NLP.
The workshop was led by Microsoft Learn Student Ambassador, Harsh Sharma(Alpha Student Ambassador). During this one-hour workshop, you will learn about textual data augmentation, including how to perform data augmentation for NLP, current trends, state-of-the-art architectures, and packages that one could use to generate augmented data. There will be a hands-on session with Q&A.
- Event Organizer : Harsh Sharma - Microsoft Learn Student Ambassador(Alpha Level)
- Event Date : 22nd September 2021, Wednesday
- Time : 5:30 PM - 6:30 PM IST ( IST - India Standard Time )
- Platform : Teams Live Event
Microsoft Technologies used and showcased :
- VScode
- Github
- Teams
## Event Recording Link : [Link](https://1drv.ms/v/s!AqGrLGWg8_-jsDtCMj8TAounInyf?e=0iEVyo)
### Demo of NLPAug Package for Handling Textual Augmentations in NLP:
To run the streamlit application:
```
1. Clone this repository
2. In the root directory, run this command in CLI : streamlit run main.py
```
# Acknowledgements
I would highly encourage to check out the following resources/packages mentioned below, which was referred for this specific event :
* BackTranslation - https://pypi.org/project/BackTranslation/
* Easy Data Augmentation (EDA) -
1. https://github.com/jasonwei20/eda_nlp
2. https://arxiv.org/abs/1901.11196
* Albumenations -
1. https://github.com/albumentations-team/albumentations
2. https://albumentations.ai/docs/
* NLPAug -
https://github.com/makcedward/nlpaug