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: 0.3271 eval accuracy: 0.8704 eval precision: 0.8653 eval recall: 0.8829 eval f1: 0.8740 eval runtime: 191.5145 eval samples per second: 4.553 eval steps per second: 0.569 epoch: 0.2197 step: 190 Model description
--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: results results: [] --- # results This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.3271 - eval_accuracy: 0.8704 - eval_precision: 0.8653 - eval_recall: 0.8829 - eval_f1: 0.8740 - eval_runtime: 191.5145 - eval_samples_per_second: 4.553 - eval_steps_per_second: 0.569 - epoch: 0.2197 - step: 190 ## 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: 8 - eval_batch_size: 8 - 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: 2 ### Framework versions - Transformers 5.12.1 - Pytorch 2.11.0+cpu - Datasets 4.0.0 - Tokenizers 0.22.2
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: 0.3271 eval accuracy: 0.8704 eval precision: 0.8653 eval recall: 0.8829 eval f1: 0.8740 eval runtime:…
