metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuning_text_model
results: []
finetuning_text_model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0412
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 1.3149 | 1.0 | 84 | 1.1456 | 0.8690 | 0.8679 | 0.9003 | 0.8690 |
| 0.5046 | 2.0 | 168 | 0.3492 | 0.9881 | 0.9881 | 0.9886 | 0.9881 |
| 0.1225 | 3.0 | 252 | 0.1019 | 0.9940 | 0.9940 | 0.9943 | 0.9940 |
| 0.0717 | 4.0 | 336 | 0.0505 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0462 | 5.0 | 420 | 0.0425 | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0