metadata
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: first_try
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
config: qqp
split: validation
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.9094978976007915
- name: F1
type: f1
value: 0.8781916841439461
first_try
This model is a fine-tuned version of bert-base-uncased on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.2975
- Accuracy: 0.9095
- F1: 0.8782
- Combined Score: 0.8938
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 | F1 | Combined Score | |
|---|---|---|---|---|---|---|---|
| 0.3347 | 1.0 | 11371 | 0.2781 | 0.8986 | 0.8645 | 0.8816 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 320, 3: 192, 4: 256, 5: 256, 6: 192, 7: 256, 8: 64, 9: 192, 10: 192, 11: 512, 12: 1675, 13: 1666, 14: 1787, 15: 1791, 16: 1772, 17: 1751, 18: 1709, 19: 1590, 20: 1320, 21: 762, 22: 348, 23: 115})]) |
| 0.3347 | 1.0 | 11371 | 0.2633 | 0.9022 | 0.8709 | 0.8865 | 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})]) |
| 0.1664 | 2.0 | 22742 | 0.2724 | 0.9048 | 0.8736 | 0.8892 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 320, 3: 192, 4: 256, 5: 256, 6: 192, 7: 256, 8: 64, 9: 192, 10: 192, 11: 512, 12: 1675, 13: 1666, 14: 1787, 15: 1791, 16: 1772, 17: 1751, 18: 1709, 19: 1590, 20: 1320, 21: 762, 22: 348, 23: 115})]) |
| 0.1664 | 2.0 | 22742 | 0.2665 | 0.9106 | 0.8809 | 0.8958 | 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})]) |
| 0.092 | 3.0 | 34113 | 0.2872 | 0.9094 | 0.8786 | 0.8940 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 320, 3: 192, 4: 256, 5: 256, 6: 192, 7: 256, 8: 64, 9: 192, 10: 192, 11: 512, 12: 1675, 13: 1666, 14: 1787, 15: 1791, 16: 1772, 17: 1751, 18: 1709, 19: 1590, 20: 1320, 21: 762, 22: 348, 23: 115})]) |
| 0.092 | 3.0 | 34113 | 0.2708 | 0.9141 | 0.8846 | 0.8994 | 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})]) |
| 0.0693 | 4.0 | 45484 | 0.2966 | 0.9088 | 0.8771 | 0.8930 | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 320, 3: 192, 4: 256, 5: 256, 6: 192, 7: 256, 8: 64, 9: 192, 10: 192, 11: 512, 12: 1675, 13: 1666, 14: 1787, 15: 1791, 16: 1772, 17: 1751, 18: 1709, 19: 1590, 20: 1320, 21: 762, 22: 348, 23: 115})]) |
| 0.0693 | 4.0 | 45484 | 0.2779 | 0.9144 | 0.8846 | 0.8995 | 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