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Multimodal Hashtag Prediction with instagram data & pytorch (2nd Place on OpenResource Hackathon 2019)

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Hashtag Prediction with Pytorch

Multimodal hashtag prediction from instagram

Overview

Model Architecture

  • ALBERT for text, VGG16 for image
  • Take out the [CLS] token from ALBERT, and change it to 100 dim
  • Change to 100 dim after flattening the VGG16 output
  • Concat them, and predict among 100 labels.

Dataset

  • Collect 50000 data from instagram (w/ selenium crawler)
  • Only include English data

How to use

1. Run docker server

$ docker run -d -p 80:80 adieujw/hashtag:latest

2. Put image at Google Drive

  • Copy the id. It will be used when you give request.

3. Request

# URL
localhost:80/predict?image_id=1DGu9R5a9jpkY-fy79VrGFmCdJigzTMC-&text=20%20days%20till%20Christmas%20%F0%9F%98%8D%F0%9F%8E%85&max_seq_len=20&n_label=10

Run on Ainize

Run on Ainize

  1. image_id : the share id you can get from google drive above
  2. text : like caption in instagram
  3. max_seq_len: maximum sequence length
  4. n_label: num of labels you want to predict
https://endpoint.ainize.ai/monologg/hashtag/predict?image_id={image_id}&text={text}&max_seq_len={max_seq_len}&n_label={n_label}
# URL
https://endpoint.ainize.ai/monologg/hashtag/predict?image_id=1DGu9R5a9jpkY-fy79VrGFmCdJigzTMC-&text=20%20days%20till%20Christmas%20%F0%9F%98%8D%F0%9F%8E%85&max_seq_len=20&n_label=10

Result on html

Reference

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Multimodal Hashtag Prediction with instagram data & pytorch (2nd Place on OpenResource Hackathon 2019)

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