DuplicatiDistillBert

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.5776
• eval_Accuracy: 0.8765
• eval_F1_macro: 0.8702
• eval_F1_class_0: 0.8768
• eval_F1_class_1: 0.7797
• eval_F1_class_2: 0.9077
• eval_F1_class_3: 0.9000
• eval_F1_class_4: 0.9083
• eval_F1_class_5: 0.8703
• eval_F1_class_6: 0.8330
• eval_F1_class_7: 0.9455
• eval_F1_class_8: 0.9642
• eval_F1_class_9: 0.8581
• eval_F1_class_10: 0.7760
• eval_F1_class_11: 0.8639
• eval_F1_class_12: 0.8035
• eval_F1_class_13: 0.9109
• eval_F1_class_14: 0.8374
• eval_F1_class_15: 0.7641
• eval_F1_class_16: 0.7246
• eval_F1_class_17: 0.9771
• eval_F1_class_18: 0.9031
• eval_F1_class_19: 1.0
• eval_runtime: 106.104
• eval_samples_per_second: 64.993
• eval_steps_per_second: 8.124
• epoch: 0.21
• step: 1008

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Framework versions

• Transformers 4.32.0
• Pytorch 2.0.1+cu117
• Datasets 2.14.4
• Tokenizers 0.13.3