---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- recall
model-index:
- name: roberta_comp
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# roberta_comp

This model is a fine-tuned version of [ibm/ColD-Fusion](https://huggingface.co/ibm/ColD-Fusion) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3066
- F1: 0.8077
- Roc Auc: 0.8650
- Accuracy: 0.5765
- Recall: 0.8105

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:------:|
| No log        | 1.0   | 231  | 0.3455          | 0.7594 | 0.8284  | 0.5306   | 0.7474 |
| No log        | 2.0   | 462  | 0.2986          | 0.7986 | 0.8569  | 0.5714   | 0.7930 |
| 0.3143        | 3.0   | 693  | 0.3006          | 0.8056 | 0.8632  | 0.5867   | 0.8070 |
| 0.3143        | 4.0   | 924  | 0.3066          | 0.8077 | 0.8650  | 0.5765   | 0.8105 |
| 0.1365        | 5.0   | 1155 | 0.3117          | 0.8028 | 0.8618  | 0.5663   | 0.8070 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+rocm5.2
- Datasets 2.8.0
- Tokenizers 0.13.2