albert-base-v2-Shakespear

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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

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0