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Running
Joshua Lochner
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Parent(s):
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Update README to include installation instructions
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Makefile
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PIP := pip3
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install:
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$(PIP) install -r requirements.txt
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run:
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streamlit run app.py
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README.md
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# SponsorBlock-ML
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Automatically detect in-video YouTube sponsorships, self/unpaid promotions, and interaction reminders. The model was trained using the [SponsorBlock](https://sponsor.ajay.app/) [database](https://sponsor.ajay.app/database) licensed used under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
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Check out the demo application
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## Predicting
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1. Download the repository:
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```bash
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cd sponsorblock-ml
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```
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---
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# SponsorBlock-ML
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Automatically detect in-video YouTube sponsorships, self/unpaid promotions, and interaction reminders. The model was trained using the [SponsorBlock](https://sponsor.ajay.app/) [database](https://sponsor.ajay.app/database) licensed used under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
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Check out the online demo application at [https://xenova.github.io/sponsorblock-ml/](https://xenova.github.io/sponsorblock-ml/), or follow the instructions below to run it locally.
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## Installation
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1. Download the repository:
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```bash
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cd sponsorblock-ml
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```
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2. Install the necessary dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Run the application:
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```bash
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streamlit run app.py
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```
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## Predicting
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- Predict for a single video using the `--video_id` argument. For example:
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```bash
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python src/predict.py --video_id zo_uoFI1WXM
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```
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- Predict for multiple videos using the `--video_ids` argument. For example:
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```bash
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python src/predict.py --video_ids IgF3OX8nT0w ao2Jfm35XeE
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```
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- Predict for a whole channel using the `--channel_id` argument. For example:
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```bash
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python src/predict.py --channel_id UCHnyfMqiRRG1u-2MsSQLbXA
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```
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Note that on the first run, the program will download the necessary models (which may take some time).
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---
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