metadata
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- recall
- precision
model-index:
- name: roberta_reman
results: []
roberta_reman
This model is a fine-tuned version of ibm/ColD-Fusion on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4272
- F1: 0.7004
- Roc Auc: 0.7862
- Accuracy: 0.4330
- Recall: 0.6831
- Precision: 0.7185
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | Recall | Precision |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 113 | 0.4673 | 0.5668 | 0.6955 | 0.2990 | 0.4930 | 0.6667 |
| No log | 2.0 | 226 | 0.4187 | 0.6397 | 0.7403 | 0.3918 | 0.5563 | 0.7524 |
| No log | 3.0 | 339 | 0.4272 | 0.7004 | 0.7862 | 0.4330 | 0.6831 | 0.7185 |
| No log | 4.0 | 452 | 0.4191 | 0.6566 | 0.7539 | 0.3918 | 0.6127 | 0.7073 |
| 0.3529 | 5.0 | 565 | 0.4246 | 0.6788 | 0.7706 | 0.4124 | 0.6549 | 0.7045 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+rocm5.2
- Datasets 2.8.0
- Tokenizers 0.13.2