albert-base-v2-Shakespear
This model is a fine-tuned version of albert-base-v2 on 2 datasets. The first dataset consists of Shakespeare's poems, and the second consists of the CNN news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0127
- Accuracy: 0.9989
- F1: 0.9989
- Recall: 0.9989
Model description
This model was created for text classification to determine whether a given text is in Shakespeare's style or not. You can use this model to classify texts or as a validation metric for evaluating Shakespeare-style text generation models. The label 'LABEL_1' indicates that the text is in Shakespeare's style, while 'LABEL_0' means that it is not.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH 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 | F1 | Recall |
|---|---|---|---|---|---|---|
| No log | 1.0 | 226 | 0.0208 | 0.9967 | 0.9967 | 0.9967 |
| No log | 2.0 | 452 | 0.0107 | 0.9989 | 0.9989 | 0.9989 |
| 0.027 | 3.0 | 678 | 0.0113 | 0.9989 | 0.9989 | 0.9989 |
| 0.027 | 4.0 | 904 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0087 | 5.0 | 1130 | 0.0121 | 0.9989 | 0.9989 | 0.9989 |
| 0.0087 | 6.0 | 1356 | 0.0123 | 0.9989 | 0.9989 | 0.9989 |
| 0.001 | 7.0 | 1582 | 0.0125 | 0.9989 | 0.9989 | 0.9989 |
| 0.001 | 8.0 | 1808 | 0.0126 | 0.9989 | 0.9989 | 0.9989 |
| 0.0 | 9.0 | 2034 | 0.0127 | 0.9989 | 0.9989 | 0.9989 |
| 0.0 | 10.0 | 2260 | 0.0127 | 0.9989 | 0.9989 | 0.9989 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0