metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: hw2advanced
results: []
hw2advanced
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: 0.2408
- Precision: {'precision': 0.9067133278302073}
- Recall: {'recall': 0.903472079391197}
- F1: {'f1': 0.9050586148832505}
- Accuracy: {'accuracy': 0.9160687311178247}
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.4744 | 1.0 | 1324 | 0.3413 | {'precision': 0.8554480519619763} | {'recall': 0.8395979020979021} | {'f1': 0.8466159268992823} | {'accuracy': 0.866786253776435} |
| 0.3566 | 2.0 | 2648 | 0.2408 | {'precision': 0.9067133278302073} | {'recall': 0.903472079391197} | {'f1': 0.9050586148832505} | {'accuracy': 0.9160687311178247} |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2