EmoCentricSarcBERT27FebRstate1000AvgPadding
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7978
- Accuracy: 0.8800
- Precision: 0.8571
- Recall: 0.8306
- F1: 0.8436
- Mcc: 0.7466
- Roc Auc: 0.9506
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: 32
- seed: 42
- distributed_type: tpu
- optimizer: Use OptimizerNames.ADAMW_TORCH_XLA with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Mcc | Roc Auc |
|---|---|---|---|---|---|---|---|---|---|
| 0.3887 | 1.0 | 735 | 0.2875 | 0.8690 | 0.8458 | 0.8118 | 0.8284 | 0.7229 | 0.9441 |
| 0.2685 | 2.0 | 1470 | 0.2775 | 0.8749 | 0.8385 | 0.8410 | 0.8398 | 0.7372 | 0.9534 |
| 0.1384 | 3.0 | 2205 | 0.3958 | 0.8816 | 0.8683 | 0.8205 | 0.8437 | 0.7493 | 0.9546 |
| 0.0982 | 4.0 | 2940 | 0.5692 | 0.8799 | 0.8670 | 0.8170 | 0.8413 | 0.7456 | 0.9496 |
| 0.0487 | 5.0 | 3675 | 0.7054 | 0.8758 | 0.8858 | 0.7821 | 0.8307 | 0.7367 | 0.9465 |
| 0.0427 | 6.0 | 4410 | 0.7538 | 0.8787 | 0.8448 | 0.8437 | 0.8442 | 0.7448 | 0.9487 |
| 0.0275 | 7.0 | 5145 | 0.7978 | 0.8800 | 0.8571 | 0.8306 | 0.8436 | 0.7466 | 0.9506 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.9.0+cpu
- Datasets 4.5.0
- Tokenizers 0.22.2