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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: Clickbait3 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Clickbait3 |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0248 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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 | 0.0373 | |
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| No log | 0.1 | 100 | 0.0320 | |
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| No log | 0.15 | 150 | 0.0295 | |
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| No log | 0.21 | 200 | 0.0302 | |
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| No log | 0.26 | 250 | 0.0331 | |
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| No log | 0.31 | 300 | 0.0280 | |
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| No log | 0.36 | 350 | 0.0277 | |
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| No log | 0.41 | 400 | 0.0316 | |
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| No log | 0.46 | 450 | 0.0277 | |
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| 0.0343 | 0.51 | 500 | 0.0276 | |
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| 0.0343 | 0.56 | 550 | 0.0282 | |
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| 0.0343 | 0.62 | 600 | 0.0280 | |
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| 0.0343 | 0.67 | 650 | 0.0271 | |
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| 0.0343 | 0.72 | 700 | 0.0264 | |
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| 0.0343 | 0.77 | 750 | 0.0265 | |
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| 0.0343 | 0.82 | 800 | 0.0260 | |
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| 0.0343 | 0.87 | 850 | 0.0263 | |
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| 0.0343 | 0.92 | 900 | 0.0259 | |
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| 0.0343 | 0.97 | 950 | 0.0277 | |
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| 0.0278 | 1.03 | 1000 | 0.0281 | |
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| 0.0278 | 1.08 | 1050 | 0.0294 | |
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| 0.0278 | 1.13 | 1100 | 0.0256 | |
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| 0.0278 | 1.18 | 1150 | 0.0258 | |
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| 0.0278 | 1.23 | 1200 | 0.0254 | |
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| 0.0278 | 1.28 | 1250 | 0.0265 | |
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| 0.0278 | 1.33 | 1300 | 0.0252 | |
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| 0.0278 | 1.38 | 1350 | 0.0251 | |
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| 0.0278 | 1.44 | 1400 | 0.0264 | |
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| 0.0278 | 1.49 | 1450 | 0.0262 | |
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| 0.023 | 1.54 | 1500 | 0.0272 | |
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| 0.023 | 1.59 | 1550 | 0.0278 | |
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| 0.023 | 1.64 | 1600 | 0.0255 | |
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| 0.023 | 1.69 | 1650 | 0.0258 | |
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| 0.023 | 1.74 | 1700 | 0.0262 | |
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| 0.023 | 1.79 | 1750 | 0.0250 | |
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| 0.023 | 1.85 | 1800 | 0.0253 | |
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| 0.023 | 1.9 | 1850 | 0.0271 | |
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| 0.023 | 1.95 | 1900 | 0.0248 | |
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| 0.023 | 2.0 | 1950 | 0.0258 | |
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| 0.0224 | 2.05 | 2000 | 0.0252 | |
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| 0.0224 | 2.1 | 2050 | 0.0259 | |
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| 0.0224 | 2.15 | 2100 | 0.0254 | |
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| 0.0224 | 2.21 | 2150 | 0.0260 | |
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| 0.0224 | 2.26 | 2200 | 0.0254 | |
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| 0.0224 | 2.31 | 2250 | 0.0266 | |
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| 0.0224 | 2.36 | 2300 | 0.0258 | |
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| 0.0224 | 2.41 | 2350 | 0.0258 | |
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| 0.0224 | 2.46 | 2400 | 0.0256 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.11.0 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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