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tiny-mlm-glue-stsb-target-glue-qqp
This model is a fine-tuned version of muhtasham/tiny-mlm-glue-stsb on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3868
- Accuracy: 0.8128
- F1: 0.7756
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
0.5792 | 0.04 | 500 | 0.5164 | 0.7290 | 0.6761 |
0.5106 | 0.09 | 1000 | 0.4848 | 0.7490 | 0.7008 |
0.4974 | 0.13 | 1500 | 0.4907 | 0.7414 | 0.7142 |
0.4818 | 0.18 | 2000 | 0.4670 | 0.7555 | 0.7216 |
0.4734 | 0.22 | 2500 | 0.4576 | 0.7624 | 0.7274 |
0.4564 | 0.26 | 3000 | 0.4486 | 0.7675 | 0.7329 |
0.4578 | 0.31 | 3500 | 0.4455 | 0.7688 | 0.7361 |
0.451 | 0.35 | 4000 | 0.4490 | 0.7672 | 0.7383 |
0.4511 | 0.4 | 4500 | 0.4401 | 0.7720 | 0.7426 |
0.4419 | 0.44 | 5000 | 0.4201 | 0.7888 | 0.7491 |
0.4308 | 0.48 | 5500 | 0.4266 | 0.7843 | 0.7507 |
0.4308 | 0.53 | 6000 | 0.4077 | 0.8005 | 0.7566 |
0.4249 | 0.57 | 6500 | 0.4182 | 0.7913 | 0.7558 |
0.4256 | 0.62 | 7000 | 0.4051 | 0.8008 | 0.7582 |
0.4179 | 0.66 | 7500 | 0.4064 | 0.8037 | 0.7630 |
0.4314 | 0.7 | 8000 | 0.4105 | 0.7963 | 0.7607 |
0.4202 | 0.75 | 8500 | 0.3970 | 0.8064 | 0.7656 |
0.4152 | 0.79 | 9000 | 0.4204 | 0.7894 | 0.7615 |
0.4121 | 0.84 | 9500 | 0.3908 | 0.8099 | 0.7667 |
0.4135 | 0.88 | 10000 | 0.3863 | 0.8151 | 0.7707 |
0.4091 | 0.92 | 10500 | 0.3839 | 0.8149 | 0.7702 |
0.4084 | 0.97 | 11000 | 0.3837 | 0.8179 | 0.7719 |
0.4068 | 1.01 | 11500 | 0.3990 | 0.8048 | 0.7712 |
0.3861 | 1.06 | 12000 | 0.3821 | 0.8173 | 0.7743 |
0.3955 | 1.1 | 12500 | 0.3868 | 0.8128 | 0.7756 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2