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hbertv1-emotion_w_in
This model is a fine-tuned version of gokuls/bert_12_layer_model_v1_complete_training_new_wt_init on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.3634
- Accuracy: 0.8975
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.2867 | 1.0 | 250 | 0.6455 | 0.7945 |
| 0.5383 | 2.0 | 500 | 0.4449 | 0.8755 |
| 0.3928 | 3.0 | 750 | 0.3870 | 0.886 |
| 0.3039 | 4.0 | 1000 | 0.4158 | 0.887 |
| 0.2619 | 5.0 | 1250 | 0.3504 | 0.8915 |
| 0.2245 | 6.0 | 1500 | 0.3277 | 0.8965 |
| 0.1967 | 7.0 | 1750 | 0.3410 | 0.894 |
| 0.1696 | 8.0 | 2000 | 0.3590 | 0.8955 |
| 0.1443 | 9.0 | 2250 | 0.3668 | 0.8965 |
| 0.1279 | 10.0 | 2500 | 0.3634 | 0.8975 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
