readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: Clickbait1
    results: []
  ---
  
  # Clickbait1
  
  This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the [Webis-Clickbait-17](https://zenodo.org/record/5530410) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0257
  
  ## Model description
  
  MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers".
  
  We fine tune this model to evaluate (regression) the clickbait level of title news.
  
  ## Intended uses & limitations
  
  Model looks like the model described in the paper [Predicting Clickbait Strength in Online Social Media](https://aclanthology.org/2020.coling-main.425/) by Indurthi Vijayasaradhi, Syed Bakhtiyar, Gupta Manish, Varma Vasudeva.
  
  The model was trained with english titles.
  
  ## Training and evaluation data
  
  We trained the model with the official training data for the chalenge (clickbait17-train-170630.zip (894 MiB, 19538 posts), plus another set that was just available after the end of the challenge (clickbait17-train-170331.zip (157 MiB, 2459 posts).
  
  ## Training procedure
  Code can be find in [Github](https://github.com/caush/Clickbait).
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | No log        | 0.05  | 50   | 0.0571          |
  | No log        | 0.09  | 100  | 0.0448          |
  | No log        | 0.14  | 150  | 0.0391          |
  | No log        | 0.18  | 200  | 0.0326          |
  | No log        | 0.23  | 250  | 0.0343          |
  | No log        | 0.27  | 300  | 0.0343          |
  | No log        | 0.32  | 350  | 0.0343          |
  | No log        | 0.36  | 400  | 0.0346          |
  | No log        | 0.41  | 450  | 0.0343          |
  | 0.0388        | 0.46  | 500  | 0.0297          |
  | 0.0388        | 0.5   | 550  | 0.0293          |
  | 0.0388        | 0.55  | 600  | 0.0301          |
  | 0.0388        | 0.59  | 650  | 0.0290          |
  | 0.0388        | 0.64  | 700  | 0.0326          |
  | 0.0388        | 0.68  | 750  | 0.0285          |
  | 0.0388        | 0.73  | 800  | 0.0285          |
  | 0.0388        | 0.77  | 850  | 0.0275          |
  | 0.0388        | 0.82  | 900  | 0.0314          |
  | 0.0388        | 0.87  | 950  | 0.0309          |
  | 0.0297        | 0.91  | 1000 | 0.0277          |
  | 0.0297        | 0.96  | 1050 | 0.0281          |
  | 0.0297        | 1.0   | 1100 | 0.0273          |
  | 0.0297        | 1.05  | 1150 | 0.0270          |
  | 0.0297        | 1.09  | 1200 | 0.0291          |
  | 0.0297        | 1.14  | 1250 | 0.0293          |
  | 0.0297        | 1.18  | 1300 | 0.0269          |
  | 0.0297        | 1.23  | 1350 | 0.0276          |
  | 0.0297        | 1.28  | 1400 | 0.0279          |
  | 0.0297        | 1.32  | 1450 | 0.0267          |
  | 0.0265        | 1.37  | 1500 | 0.0270          |
  | 0.0265        | 1.41  | 1550 | 0.0300          |
  | 0.0265        | 1.46  | 1600 | 0.0274          |
  | 0.0265        | 1.5   | 1650 | 0.0274          |
  | 0.0265        | 1.55  | 1700 | 0.0266          |
  | 0.0265        | 1.59  | 1750 | 0.0267          |
  | 0.0265        | 1.64  | 1800 | 0.0267          |
  | 0.0265        | 1.68  | 1850 | 0.0280          |
  | 0.0265        | 1.73  | 1900 | 0.0274          |
  | 0.0265        | 1.78  | 1950 | 0.0272          |
  | 0.025         | 1.82  | 2000 | 0.0261          |
  | 0.025         | 1.87  | 2050 | 0.0268          |
  | 0.025         | 1.91  | 2100 | 0.0268          |
  | 0.025         | 1.96  | 2150 | 0.0259          |
  | 0.025         | 2.0   | 2200 | 0.0257          |
  | 0.025         | 2.05  | 2250 | 0.0260          |
  | 0.025         | 2.09  | 2300 | 0.0263          |
  | 0.025         | 2.14  | 2350 | 0.0262          |
  | 0.025         | 2.19  | 2400 | 0.0269          |
  | 0.025         | 2.23  | 2450 | 0.0262          |
  | 0.0223        | 2.28  | 2500 | 0.0262          |
  | 0.0223        | 2.32  | 2550 | 0.0267          |
  | 0.0223        | 2.37  | 2600 | 0.0260          |
  | 0.0223        | 2.41  | 2650 | 0.0260          |
  | 0.0223        | 2.46  | 2700 | 0.0259          |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0a0+17540c5
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  