metadata
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-base-mr-6000ar
results: []
roberta-base-mr-6000ar
This model was trained from scratch on the Internal Selection for BDC Satria Data 2024 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0515
- Accuracy: 0.9413
- Precision: 0.9643
- Recall: 0.9265
- F1: 0.9450
Model description
Training dataset was augmented with the paraphrasing method to generate 6000 extra data.
Intended uses & limitations
This model was not the model used for the final submission on the internal selection.
Training and evaluation data
The training dataset had 1500 rows of data, and an extra 6000 augmented data. The evaluation dataset had 500 rows of data.
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0185 | 1.0 | 821 | 0.0800 | 0.9173 | 0.8879 | 0.9706 | 0.9274 |
| 0.0121 | 2.0 | 1642 | 0.0789 | 0.9147 | 0.9778 | 0.8627 | 0.9167 |
| 0.0101 | 3.0 | 2463 | 0.0515 | 0.9413 | 0.9643 | 0.9265 | 0.9450 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1