metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results: []
distilbert-base-uncased-finetuned-cola
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: 0.5539
- Accuracy: 0.8169
- F1: 0.8741
- Precision: 0.8329
- Recall: 0.9196
- Matthews Correlation: 0.5504
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 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Matthews Correlation |
|---|---|---|---|---|---|---|---|---|
| 0.5215 | 1.0 | 535 | 0.4659 | 0.7747 | 0.8514 | 0.7826 | 0.9334 | 0.4284 |
| 0.3554 | 2.0 | 1070 | 0.4588 | 0.8073 | 0.8646 | 0.8403 | 0.8904 | 0.5339 |
| 0.2373 | 3.0 | 1605 | 0.5539 | 0.8169 | 0.8741 | 0.8329 | 0.9196 | 0.5504 |
| 0.1736 | 4.0 | 2140 | 0.7879 | 0.8111 | 0.8723 | 0.8187 | 0.9334 | 0.5321 |
| 0.1293 | 5.0 | 2675 | 0.8469 | 0.8102 | 0.8699 | 0.8265 | 0.9182 | 0.5324 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3