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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: distilbert-base-uncased__sst2__train-16-6
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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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# distilbert-base-uncased__sst2__train-16-6
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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: 0.8356
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- Accuracy: 0.6480
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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: 2e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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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: 50
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.6978 | 1.0 | 7 | 0.6807 | 0.4286 |
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| 0.6482 | 2.0 | 14 | 0.6775 | 0.4286 |
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| 0.6051 | 3.0 | 21 | 0.6623 | 0.5714 |
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| 0.486 | 4.0 | 28 | 0.6710 | 0.5714 |
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| 0.4612 | 5.0 | 35 | 0.5325 | 0.7143 |
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| 0.2233 | 6.0 | 42 | 0.4992 | 0.7143 |
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| 0.1328 | 7.0 | 49 | 0.4753 | 0.7143 |
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| 0.0905 | 8.0 | 56 | 0.2416 | 1.0 |
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| 0.0413 | 9.0 | 63 | 0.2079 | 1.0 |
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| 0.0356 | 10.0 | 70 | 0.2234 | 0.8571 |
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| 0.0217 | 11.0 | 77 | 0.2639 | 0.8571 |
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| 0.0121 | 12.0 | 84 | 0.2977 | 0.8571 |
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| 0.0105 | 13.0 | 91 | 0.3468 | 0.8571 |
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| 0.0085 | 14.0 | 98 | 0.3912 | 0.8571 |
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| 0.0077 | 15.0 | 105 | 0.4000 | 0.8571 |
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| 0.0071 | 16.0 | 112 | 0.4015 | 0.8571 |
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| 0.0078 | 17.0 | 119 | 0.3865 | 0.8571 |
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| 0.0059 | 18.0 | 126 | 0.3603 | 0.8571 |
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| 0.0051 | 19.0 | 133 | 0.3231 | 0.8571 |
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### Framework versions
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- Transformers 4.15.0
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- Pytorch 1.10.2+cu102
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- Datasets 1.18.2
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- Tokenizers 0.10.3
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