distilbert-base-uncased-finetuned-ner
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0788
- eval_model_preparation_time: 0.0015
- eval_precision: 0.8041
- eval_recall: 0.8533
- eval_f1: 0.8280
- eval_accuracy: 0.9795
- eval_runtime: 3.3268
- eval_samples_per_second: 277.743
- eval_steps_per_second: 17.434
- 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
- optimizer: Use OptimizerNames.ADAMW_TORCH 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 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
