metadata
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dist_ret_hpqa
results: []
dist_ret_hpqa
This model is a fine-tuned version of nlpproject2023/small-bert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0951
- Accuracy: 0.9760
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: 24
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1464 | 0.99 | 3500 | 0.0951 | 0.9760 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3