metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuned_distilbert_model
results: []
finetuned_distilbert_model
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: 0.0728
- Precision: 0.6674
- Recall: 0.6483
- F1: 0.6577
- Accuracy: 0.9705
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0818 | 1.0 | 2185 | 0.0766 | 0.6435 | 0.5998 | 0.6209 | 0.9692 |
| 0.0709 | 2.0 | 4370 | 0.0739 | 0.6532 | 0.6241 | 0.6383 | 0.9700 |
| 0.0638 | 3.0 | 6555 | 0.0728 | 0.6674 | 0.6483 | 0.6577 | 0.9705 |
| 0.0579 | 4.0 | 8740 | 0.0753 | 0.6592 | 0.6813 | 0.6701 | 0.9698 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1