metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ennedendahakotubert
results: []
ennedendahakotubert
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6561
- Accuracy: 0.5476
- F1: 0.4969
- Precision: 0.5597
- Recall: 0.4468
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 2.5202 | 1.0 | 771 | 0.6304 | 0.5729 | 0.3810 | 0.6921 | 0.2628 |
| 2.52 | 2.0 | 1542 | 0.6230 | 0.5768 | 0.6437 | 0.5559 | 0.7644 |
| 2.399 | 3.0 | 2313 | 0.6289 | 0.5682 | 0.6491 | 0.5467 | 0.7988 |
| 2.4829 | 4.0 | 3084 | 0.6366 | 0.5552 | 0.6149 | 0.5422 | 0.7101 |
| 2.4135 | 5.0 | 3855 | 0.6561 | 0.5476 | 0.4969 | 0.5597 | 0.4468 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0