metadata
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-finetuned-mathqa
results: []
distilbert-base-finetuned-mathqa
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: 0.1648
- Accuracy: 0.9576
- F1: 0.9577
- Precision: 0.9578
- Recall: 0.9576
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: 10
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2186 | 1.0 | 2970 | 0.1849 | 0.9455 | 0.9456 | 0.9460 | 0.9454 |
| 0.1889 | 2.0 | 5940 | 0.1687 | 0.9539 | 0.9540 | 0.9539 | 0.9540 |
| 0.1528 | 3.0 | 8910 | 0.1648 | 0.9576 | 0.9577 | 0.9578 | 0.9576 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1