NoFrequentWords

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: 1.5140
• eval_Accuracy: 0.4027
• eval_F1_macro: 0.1427
• eval_F1_class_0: 0.9205
• eval_F1_class_1: 0.6667
• eval_F1_class_2: 0.1782
• eval_F1_class_3: 0.0
• eval_F1_class_4: 0.0
• eval_F1_class_5: 0.0
• eval_F1_class_6: 0.0204
• eval_F1_class_7: 0.0
• eval_F1_class_8: 0.0
• eval_F1_class_9: 0.9070
• eval_F1_class_10: 0.0253
• eval_F1_class_11: 0.0
• eval_F1_class_12: 0.1140
• eval_F1_class_13: 0.0
• eval_F1_class_14: 0.0220
• eval_F1_class_15: 0.0
• eval_F1_class_16: 0.0
• eval_F1_class_17: 0.0
• eval_F1_class_18: 0.0
• eval_F1_class_19: 0.0
• eval_runtime: 17.6645
• eval_samples_per_second: 63.97
• eval_steps_per_second: 4.019
• epoch: 2.92
• step: 9500

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Framework versions

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