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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: quality_model_apr3 |
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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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# quality_model_apr3 |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0117 |
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- Mse: 0.0117 |
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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: 8 |
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- eval_batch_size: 8 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mse | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.0209 | 0.05 | 50 | 0.0135 | 0.0135 | |
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| 0.0179 | 0.11 | 100 | 0.0118 | 0.0118 | |
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| 0.0153 | 0.16 | 150 | 0.0116 | 0.0116 | |
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| 0.0159 | 0.22 | 200 | 0.0131 | 0.0131 | |
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| 0.0169 | 0.27 | 250 | 0.0163 | 0.0163 | |
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| 0.0116 | 0.32 | 300 | 0.0116 | 0.0116 | |
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| 0.0094 | 0.38 | 350 | 0.0123 | 0.0123 | |
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| 0.0168 | 0.43 | 400 | 0.0115 | 0.0115 | |
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| 0.0224 | 0.48 | 450 | 0.0135 | 0.0135 | |
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| 0.0144 | 0.54 | 500 | 0.0116 | 0.0116 | |
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| 0.0147 | 0.59 | 550 | 0.0115 | 0.0115 | |
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| 0.0117 | 0.65 | 600 | 0.0121 | 0.0121 | |
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| 0.0198 | 0.7 | 650 | 0.0120 | 0.0120 | |
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| 0.0119 | 0.75 | 700 | 0.0121 | 0.0121 | |
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| 0.0166 | 0.81 | 750 | 0.0118 | 0.0118 | |
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| 0.0096 | 0.86 | 800 | 0.0123 | 0.0123 | |
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| 0.0166 | 0.92 | 850 | 0.0115 | 0.0115 | |
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| 0.0181 | 0.97 | 900 | 0.0114 | 0.0114 | |
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| 0.0128 | 1.02 | 950 | 0.0114 | 0.0114 | |
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| 0.0174 | 1.08 | 1000 | 0.0113 | 0.0113 | |
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| 0.0161 | 1.13 | 1050 | 0.0126 | 0.0126 | |
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| 0.0174 | 1.19 | 1100 | 0.0141 | 0.0141 | |
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| 0.016 | 1.24 | 1150 | 0.0114 | 0.0114 | |
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| 0.0098 | 1.29 | 1200 | 0.0114 | 0.0114 | |
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| 0.0179 | 1.35 | 1250 | 0.0126 | 0.0126 | |
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| 0.0141 | 1.4 | 1300 | 0.0115 | 0.0115 | |
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| 0.0118 | 1.45 | 1350 | 0.0116 | 0.0116 | |
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| 0.0115 | 1.51 | 1400 | 0.0113 | 0.0113 | |
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| 0.0118 | 1.56 | 1450 | 0.0113 | 0.0113 | |
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| 0.0165 | 1.62 | 1500 | 0.0118 | 0.0118 | |
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| 0.0129 | 1.67 | 1550 | 0.0113 | 0.0113 | |
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| 0.011 | 1.72 | 1600 | 0.0118 | 0.0118 | |
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| 0.0128 | 1.78 | 1650 | 0.0120 | 0.0120 | |
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| 0.0145 | 1.83 | 1700 | 0.0124 | 0.0124 | |
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| 0.014 | 1.89 | 1750 | 0.0114 | 0.0114 | |
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| 0.0155 | 1.94 | 1800 | 0.0114 | 0.0114 | |
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| 0.0144 | 1.99 | 1850 | 0.0114 | 0.0114 | |
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| 0.0141 | 2.05 | 1900 | 0.0114 | 0.0114 | |
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| 0.0108 | 2.1 | 1950 | 0.0117 | 0.0117 | |
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| 0.0109 | 2.16 | 2000 | 0.0113 | 0.0113 | |
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| 0.0124 | 2.21 | 2050 | 0.0132 | 0.0132 | |
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| 0.0169 | 2.26 | 2100 | 0.0123 | 0.0123 | |
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| 0.0115 | 2.32 | 2150 | 0.0120 | 0.0120 | |
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| 0.0102 | 2.37 | 2200 | 0.0117 | 0.0117 | |
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| 0.0189 | 2.42 | 2250 | 0.0116 | 0.0116 | |
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| 0.0136 | 2.48 | 2300 | 0.0115 | 0.0115 | |
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| 0.0116 | 2.53 | 2350 | 0.0119 | 0.0119 | |
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| 0.0141 | 2.59 | 2400 | 0.0119 | 0.0119 | |
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| 0.0098 | 2.64 | 2450 | 0.0120 | 0.0120 | |
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| 0.0081 | 2.69 | 2500 | 0.0117 | 0.0117 | |
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| 0.009 | 2.75 | 2550 | 0.0119 | 0.0119 | |
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| 0.0121 | 2.8 | 2600 | 0.0118 | 0.0118 | |
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| 0.0128 | 2.86 | 2650 | 0.0123 | 0.0123 | |
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| 0.0131 | 2.91 | 2700 | 0.0117 | 0.0117 | |
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| 0.009 | 2.96 | 2750 | 0.0117 | 0.0117 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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