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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 |
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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 |
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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: 0.0104 |
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- Mse: 0.0104 |
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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: 1 |
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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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| 0.0154 | 0.05 | 50 | 0.0106 | 0.0106 | |
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| 0.0172 | 0.11 | 100 | 0.0109 | 0.0109 | |
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| 0.0166 | 0.16 | 150 | 0.0199 | 0.0199 | |
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| 0.0132 | 0.22 | 200 | 0.0106 | 0.0106 | |
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| 0.0153 | 0.27 | 250 | 0.0120 | 0.0120 | |
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| 0.0131 | 0.32 | 300 | 0.0104 | 0.0104 | |
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| 0.0127 | 0.38 | 350 | 0.0104 | 0.0104 | |
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| 0.0143 | 0.43 | 400 | 0.0110 | 0.0110 | |
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| 0.0146 | 0.48 | 450 | 0.0113 | 0.0113 | |
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| 0.0119 | 0.54 | 500 | 0.0115 | 0.0115 | |
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| 0.0172 | 0.59 | 550 | 0.0107 | 0.0107 | |
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| 0.0111 | 0.65 | 600 | 0.0104 | 0.0104 | |
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| 0.0114 | 0.7 | 650 | 0.0105 | 0.0105 | |
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| 0.0219 | 0.75 | 700 | 0.0106 | 0.0106 | |
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| 0.0118 | 0.81 | 750 | 0.0122 | 0.0122 | |
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| 0.0184 | 0.86 | 800 | 0.0104 | 0.0104 | |
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| 0.0176 | 0.92 | 850 | 0.0104 | 0.0104 | |
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| 0.0137 | 0.97 | 900 | 0.0104 | 0.0104 | |
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### Framework versions |
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- Transformers 4.39.1 |
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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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