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---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: first_try
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.554912808282685
---
<!-- 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. -->
# first_try
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8516
- Matthews Correlation: 0.5549
## 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: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| No log | 1.0 | 268 | 0.7150 | 0.3947 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| No log | 1.0 | 268 | 0.6399 | 0.5222 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.8522 | 2.0 | 536 | 0.7287 | 0.4630 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| 0.8522 | 2.0 | 536 | 0.6622 | 0.5624 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.8522 | 3.0 | 804 | 0.7320 | 0.4775 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| 0.8522 | 3.0 | 804 | 0.6782 | 0.5573 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.3135 | 4.0 | 1072 | 0.8995 | 0.4830 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| 0.3135 | 4.0 | 1072 | 0.7692 | 0.5549 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.3135 | 5.0 | 1340 | 0.8262 | 0.5107 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| 0.3135 | 5.0 | 1340 | 0.6901 | 0.5834 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.155 | 6.0 | 1608 | 0.8722 | 0.5076 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| 0.155 | 6.0 | 1608 | 0.7215 | 0.5925 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.155 | 7.0 | 1876 | 0.9456 | 0.5054 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| 0.155 | 7.0 | 1876 | 0.8113 | 0.5765 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.0957 | 8.0 | 2144 | 0.9191 | 0.5049 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| 0.0957 | 8.0 | 2144 | 0.7811 | 0.5885 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.0957 | 9.0 | 2412 | 0.9647 | 0.4994 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| 0.0957 | 9.0 | 2412 | 0.8087 | 0.5598 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.0729 | 10.0 | 2680 | 0.9290 | 0.4990 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| 0.0729 | 10.0 | 2680 | 0.8079 | 0.5754 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.0729 | 11.0 | 2948 | 0.9496 | 0.4982 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| 0.0729 | 11.0 | 2948 | 0.8124 | 0.5728 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.0626 | 12.0 | 3216 | 0.9496 | 0.4982 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 256, 2: 320, 3: 448, 4: 640, 5: 640, 6: 768, 7: 576, 8: 448, 9: 256, 10: 384, 11: 320, 12: 949, 13: 959, 14: 1110, 15: 1096, 16: 1158, 17: 1062, 18: 1028, 19: 1014, 20: 670, 21: 436, 22: 348, 23: 370})]) |
| 0.0626 | 12.0 | 3216 | 0.8131 | 0.5728 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.12.1
- Datasets 2.13.1
- Tokenizers 0.13.3
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