metadata
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: first_try
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
config: mnli
split: validation_matched
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8417412530512612
first_try
This model is a fine-tuned version of bert-base-uncased on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.4506
- Accuracy: 0.8417
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: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|---|---|---|---|---|---|
| 0.3038 | 1.0 | 12272 | 0.4950 | 0.8238 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 256, 1: 256, 2: 192, 3: 320, 4: 192, 5: 384, 6: 128, 7: 256, 8: 256, 9: 256, 10: 192, 11: 256, 12: 1542, 13: 1611, 14: 1891, 15: 1877, 16: 1825, 17: 1790, 18: 1678, 19: 1544, 20: 1223, 21: 628, 22: 345, 23: 213})]) |
| 0.3038 | 1.0 | 12272 | 0.4592 | 0.8385 | 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})]) |
| 0.1683 | 2.0 | 24544 | 0.4678 | 0.8326 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 256, 1: 256, 2: 192, 3: 320, 4: 192, 5: 384, 6: 128, 7: 256, 8: 256, 9: 256, 10: 192, 11: 256, 12: 1542, 13: 1611, 14: 1891, 15: 1877, 16: 1825, 17: 1790, 18: 1678, 19: 1544, 20: 1223, 21: 628, 22: 345, 23: 213})]) |
| 0.1683 | 2.0 | 24544 | 0.4285 | 0.8479 | 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})]) |
| 0.1132 | 3.0 | 36816 | 0.4638 | 0.8381 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 256, 1: 256, 2: 192, 3: 320, 4: 192, 5: 384, 6: 128, 7: 256, 8: 256, 9: 256, 10: 192, 11: 256, 12: 1542, 13: 1611, 14: 1891, 15: 1877, 16: 1825, 17: 1790, 18: 1678, 19: 1544, 20: 1223, 21: 628, 22: 345, 23: 213})]) |
| 0.1132 | 3.0 | 36816 | 0.4231 | 0.8492 | 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})]) |
| 0.0894 | 4.0 | 49088 | 0.4678 | 0.8383 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 256, 1: 256, 2: 192, 3: 320, 4: 192, 5: 384, 6: 128, 7: 256, 8: 256, 9: 256, 10: 192, 11: 256, 12: 1542, 13: 1611, 14: 1891, 15: 1877, 16: 1825, 17: 1790, 18: 1678, 19: 1544, 20: 1223, 21: 628, 22: 345, 23: 213})]) |
| 0.0894 | 4.0 | 49088 | 0.4261 | 0.8497 | 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