https://github.com/kumar-shridhar/online-toxicity-detection
APOLLO-1: Online Toxicity Detection
https://github.com/kumar-shridhar/online-toxicity-detection
attention-model bert-model deep-learning nlp nlp-machine-learning roberta toxic-comment-classification
Last synced: 6 months ago
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APOLLO-1: Online Toxicity Detection
- Host: GitHub
- URL: https://github.com/kumar-shridhar/online-toxicity-detection
- Owner: kumar-shridhar
- License: mit
- Created: 2020-05-10T12:52:18.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-06-06T12:16:03.000Z (over 5 years ago)
- Last Synced: 2025-03-31T08:39:31.005Z (7 months ago)
- Topics: attention-model, bert-model, deep-learning, nlp, nlp-machine-learning, roberta, toxic-comment-classification
- Language: Python
- Homepage:
- Size: 11.8 MB
- Stars: 2
- Watchers: 4
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](https://www.python.org/downloads/release/python-376/)
[](https://github.com/tensorflow/tensorflow/releases)
[](https://github.com/kumar-shridhar/APOLLO-1/blob/master/LICENSE)
##### Read more about the project in our [blog](https://medium.com/@shridhar743/are-you-indulging-in-a-toxic-conversation-c67708b8895).
# Online Toxicity Detection
Project **Apollo** consist of a series of projects that are aimed at using Deep Learning for various applications. This work presents the first project: **APOLLO-1**. This project is aimed at developing an application that detects **toxicity in an online conversation**.
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## How to run
1. Clone the repo: ``` git clone https://github.com/kumar-shridhar/Online-Toxicity-Detection.git```
2. Make sure you have anaconda installed. If not, check [here](https://docs.anaconda.com/anaconda/install/).
3. Install all the requirements using conda yaml: ```conda env create -f environment_{os}.yml``` where ```{os}``` can be ```Windows``` or ```Linux```.
4. Download saved model from [here](https://drive.google.com/file/d/1RNd4L_zGVrFF_Cl-6KfoHIInMO-5A0e3/view?usp=sharing)
5. Unzip the model and save in ```Online-Toxicity-Detection/apollo/inference``` folder.
6. Run command:
* ``` cd Online-Toxicity-Detection```
* ``` python apollo/Frontend/app.py```
7. Go to the link in the console and provide the YouTube URL, and adjust the ```sensitivity``` and ```number of comments```. The results will be displayed in a chart form.
8. You can export the ```.csv``` file of the final results by clicking on ```export results```.
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## References
* [Egbertbouman](https://github.com/egbertbouman/youtube-comment-downloader)
* [HuggingFace](https://github.com/huggingface/transformers)
* [Xhlulu](https://www.kaggle.com/xhlulu/jigsaw-tpu-xlm-roberta)
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### Contact
Feel free to contact the authors in case of any issues.
[Naveed Akram](https://github.com/n-akram), [Ritu Yadav](https://github.com/RituYadav92), [Venkatesh Iyer](https://github.com/venkyiyer)
[Sadique Adnan Siddiqui](https://github.com/sadique-adnan), [Ashutosh Mishra](https://github.com/ashutoshmishra1014), [Kumar Shridhar](https://kumar-shridhar.github.io/)