metadata
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distillbert-names-data
results: []
distillbert-names-data
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:
- Loss: 0.0855
- Accuracy: 0.9833
- F1: 0.9767
- Precision: 0.9702
- Recall: 0.9832
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: 5e-05
- train_batch_size: 600
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0283 | 1.0 | 703 | 0.0615 | 0.9830 | 0.9763 | 0.9713 | 0.9814 |
| 0.0238 | 2.0 | 1406 | 0.0723 | 0.9834 | 0.9769 | 0.9695 | 0.9843 |
| 0.0132 | 3.0 | 2109 | 0.0855 | 0.9833 | 0.9767 | 0.9702 | 0.9832 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.0
- Datasets 2.18.0
- Tokenizers 0.15.2