hbertv1-emotion-logit_KD-tiny_ffn_2
This model is a fine-tuned version of gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_2 on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.4740
- Accuracy: 0.9005
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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 33
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
3.1189 | 1.0 | 250 | 2.6103 | 0.514 |
2.0804 | 2.0 | 500 | 1.4939 | 0.7695 |
1.2677 | 3.0 | 750 | 0.8999 | 0.8445 |
0.8885 | 4.0 | 1000 | 0.6887 | 0.874 |
0.7023 | 5.0 | 1250 | 0.5821 | 0.889 |
0.5796 | 6.0 | 1500 | 0.5364 | 0.8875 |
0.5106 | 7.0 | 1750 | 0.5043 | 0.89 |
0.4603 | 8.0 | 2000 | 0.5055 | 0.889 |
0.405 | 9.0 | 2250 | 0.4903 | 0.89 |
0.3782 | 10.0 | 2500 | 0.4793 | 0.8965 |
0.3488 | 11.0 | 2750 | 0.4832 | 0.8945 |
0.3301 | 12.0 | 3000 | 0.4740 | 0.9005 |
0.3163 | 13.0 | 3250 | 0.4768 | 0.89 |
0.2983 | 14.0 | 3500 | 0.4925 | 0.887 |
0.2835 | 15.0 | 3750 | 0.4764 | 0.898 |
0.2702 | 16.0 | 4000 | 0.4856 | 0.8905 |
0.2522 | 17.0 | 4250 | 0.4829 | 0.897 |
Framework versions
- Transformers 4.35.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.15.0
- Tokenizers 0.15.0
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