This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
eval_loss: 0.7528
eval_model_preparation_time: 0.002
eval_accuracy: 0.7628
eval_macro_precision: 0.7622
eval_macro_recall: 0.7619
eval_macro_f1: 0.7611
eval_neutral_precision: 0.7921
eval_neutral_recall: 0.7675
eval_neutral_f1: 0.7796
eval_positive_precision: 0.8106
eval_positive_recall: 0.7607
eval_positive_f1: 0.7849
eval_negative_precision: 0.6838
eval_negative_recall: 0.7575
eval_negative_f1: 0.7188
eval_runtime: 17.582
eval_samples_per_second: 472.643
eval_steps_per_second: 29.576
step: 0
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
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
