---
base_model: Kevinger/setfit-hub-report
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: Hub-Repoop-1706132005
  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. -->

# Hub-Repoop-1706132005

This model is a fine-tuned version of [Kevinger/setfit-hub-report](https://huggingface.co/Kevinger/setfit-hub-report) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1718
- F1: 0.7750
- Roc Auc: 0.8654
- Accuracy: 0.7595

## 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: 13

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 277  | 0.3053          | 0.0    | 0.5     | 0.0      |
| 0.3561        | 2.0   | 554  | 0.2275          | 0.5792 | 0.7105  | 0.4283   |
| 0.3561        | 3.0   | 831  | 0.1904          | 0.7453 | 0.8270  | 0.6730   |
| 0.2037        | 4.0   | 1108 | 0.1791          | 0.7602 | 0.8498  | 0.7257   |
| 0.2037        | 5.0   | 1385 | 0.1724          | 0.7614 | 0.8556  | 0.7405   |
| 0.144         | 6.0   | 1662 | 0.1733          | 0.7557 | 0.8546  | 0.7405   |
| 0.144         | 7.0   | 1939 | 0.1718          | 0.7750 | 0.8654  | 0.7595   |
| 0.1093        | 8.0   | 2216 | 0.1805          | 0.7605 | 0.8579  | 0.7468   |
| 0.1093        | 9.0   | 2493 | 0.1842          | 0.7484 | 0.8508  | 0.7342   |
| 0.0872        | 10.0  | 2770 | 0.1817          | 0.7597 | 0.8577  | 0.7447   |
| 0.0748        | 11.0  | 3047 | 0.1824          | 0.7561 | 0.8579  | 0.7468   |
| 0.0748        | 12.0  | 3324 | 0.1826          | 0.7663 | 0.8630  | 0.7553   |
| 0.0674        | 13.0  | 3601 | 0.1844          | 0.7594 | 0.8585  | 0.7489   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1