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

# roberta_tec_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: 0.2970
- F1: 0.8202
- Roc Auc: 0.8806
- Recall: 0.8561
- Precision: 0.7871

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:------:|:---------:|
| 0.4549        | 1.0   | 923  | 0.3128          | 0.7604 | 0.8277  | 0.7404 | 0.7815    |
| 0.251         | 2.0   | 1846 | 0.2970          | 0.8202 | 0.8806  | 0.8561 | 0.7871    |
| 0.1509        | 3.0   | 2769 | 0.3228          | 0.8146 | 0.8713  | 0.8246 | 0.8048    |


### Framework versions

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