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---
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
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