---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: cold_reman_gpu_v1
  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. -->

# cold_reman_gpu_v1

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: 0.4520
- F1: 0.6592
- Roc Auc: 0.7559
- Recall: 0.6197
- Precision: 0.704

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:------:|:---------:|
| No log        | 1.0   | 452  | 0.4556          | 0.6    | 0.7160  | 0.5282 | 0.6944    |
| 0.4832        | 2.0   | 904  | 0.4520          | 0.6592 | 0.7559  | 0.6197 | 0.704     |
| 0.3505        | 3.0   | 1356 | 0.4658          | 0.6543 | 0.7530  | 0.6197 | 0.6929    |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2