sentiment-model

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.7455
• eval_model_preparation_time: 0.0018
• eval_accuracy: 0.6621
• eval_f1_weighted: 0.6509
• eval_f1_macro: 0.6509
• eval_runtime: 4.4594
• eval_samples_per_second: 195.092
• eval_steps_per_second: 6.279
• 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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 3.1.0
• Tokenizers 0.22.2