metadata
language:
- en
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
datasets:
- schone-achievement
model-index:
- name: final
results: []
final
This model is a fine-tuned version of roberta-large on the GLUE SCHONE_ACH dataset. It achieves the following results on the evaluation set:
- Loss: 0.0846
- Roc Auc: 0.9888
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: 1e-05
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Roc Auc |
|---|---|---|---|---|
| 0.185 | 0.9985 | 332 | 0.1574 | 0.9583 |
| 0.1607 | 2.0 | 665 | 0.1283 | 0.9730 |
| 0.1357 | 2.9985 | 997 | 0.1071 | 0.9818 |
| 0.1214 | 4.0 | 1330 | 0.0978 | 0.9870 |
| 0.1011 | 4.9925 | 1660 | 0.0846 | 0.9888 |
Framework versions
- Transformers 4.44.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1