End of training
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README.md
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---
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license: mit
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base_model: roberta-base
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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: roberta_sst2_padding70model
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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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# roberta_sst2_padding70model
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5675
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- Accuracy: 0.9412
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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: 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: 20
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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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| No log | 1.0 | 433 | 0.1869 | 0.9308 |
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| 0.3423 | 2.0 | 866 | 0.4539 | 0.9039 |
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| 0.2033 | 3.0 | 1299 | 0.2940 | 0.9379 |
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| 0.1087 | 4.0 | 1732 | 0.3001 | 0.9396 |
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| 0.0742 | 5.0 | 2165 | 0.3625 | 0.9379 |
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| 0.0511 | 6.0 | 2598 | 0.4227 | 0.9407 |
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| 0.028 | 7.0 | 3031 | 0.4785 | 0.9352 |
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| 0.028 | 8.0 | 3464 | 0.3861 | 0.9412 |
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| 0.023 | 9.0 | 3897 | 0.4512 | 0.9407 |
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| 0.0201 | 10.0 | 4330 | 0.5132 | 0.9368 |
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| 0.0123 | 11.0 | 4763 | 0.4452 | 0.9423 |
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| 0.0115 | 12.0 | 5196 | 0.4293 | 0.9500 |
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| 0.0071 | 13.0 | 5629 | 0.7280 | 0.9182 |
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| 0.0186 | 14.0 | 6062 | 0.5646 | 0.9368 |
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| 0.0186 | 15.0 | 6495 | 0.5034 | 0.9434 |
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| 0.0049 | 16.0 | 6928 | 0.5029 | 0.9418 |
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| 0.0078 | 17.0 | 7361 | 0.4935 | 0.9456 |
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| 0.0039 | 18.0 | 7794 | 0.5453 | 0.9418 |
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| 0.0027 | 19.0 | 8227 | 0.5793 | 0.9385 |
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| 0.0006 | 20.0 | 8660 | 0.5675 | 0.9412 |
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### Framework versions
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- Transformers 4.32.1
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- Pytorch 2.1.1
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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