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