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---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding50model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_sst2_padding50model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5107
- Accuracy: 0.9462
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.1735 | 0.9319 |
| 0.327 | 2.0 | 866 | 0.2500 | 0.9336 |
| 0.1893 | 3.0 | 1299 | 0.2987 | 0.9407 |
| 0.1229 | 4.0 | 1732 | 0.3376 | 0.9418 |
| 0.0753 | 5.0 | 2165 | 0.3283 | 0.9484 |
| 0.0496 | 6.0 | 2598 | 0.5720 | 0.9116 |
| 0.0349 | 7.0 | 3031 | 0.4278 | 0.9363 |
| 0.0349 | 8.0 | 3464 | 0.4501 | 0.9379 |
| 0.0254 | 9.0 | 3897 | 0.4728 | 0.9374 |
| 0.0217 | 10.0 | 4330 | 0.4662 | 0.9368 |
| 0.0171 | 11.0 | 4763 | 0.4622 | 0.9418 |
| 0.0082 | 12.0 | 5196 | 0.4804 | 0.9429 |
| 0.0094 | 13.0 | 5629 | 0.4789 | 0.9445 |
| 0.0047 | 14.0 | 6062 | 0.5459 | 0.9423 |
| 0.0047 | 15.0 | 6495 | 0.4672 | 0.9434 |
| 0.009 | 16.0 | 6928 | 0.5178 | 0.9445 |
| 0.0021 | 17.0 | 7361 | 0.5107 | 0.9467 |
| 0.0042 | 18.0 | 7794 | 0.5101 | 0.9445 |
| 0.0053 | 19.0 | 8227 | 0.5043 | 0.9462 |
| 0.0017 | 20.0 | 8660 | 0.5107 | 0.9462 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
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