metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
results: []
results
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1950
- Accuracy: 0.9279
- Precision: 0.9366
- Recall: 0.918
- F1: 0.9272
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: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.3973 | 1.0 | 782 | 0.2365 | 0.9059 | 0.9528 | 0.8541 | 0.9007 |
| 0.1701 | 2.0 | 1564 | 0.1950 | 0.9279 | 0.9366 | 0.918 | 0.9272 |
| 0.1073 | 3.0 | 2346 | 0.2522 | 0.9319 | 0.9358 | 0.9274 | 0.9316 |
| 0.0574 | 4.0 | 3128 | 0.2988 | 0.9301 | 0.9278 | 0.9328 | 0.9303 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1