---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
model-index:
- name: my_awesome_model
  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. -->

# my_awesome_model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7362
- Accuracy: {'accuracy': 0.7291666666666666}
- F1: {'f1': 0.7417218543046359}
- Recall: {'recall': 0.7417218543046358}
- Auc: {'roc_auc': 0.7285251607289602}

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                         | Recall                         | Auc                             |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|:------------------------------:|:-------------------------------:|
| No log        | 0.91  | 10   | 0.5662          | {'accuracy': 0.6875}             | {'f1': 0.7}                | {'recall': 0.695364238410596}  | {'roc_auc': 0.6870981775994585} |
| No log        | 1.82  | 20   | 0.5665          | {'accuracy': 0.6909722222222222} | {'f1': 0.6832740213523132} | {'recall': 0.6357615894039735} | {'roc_auc': 0.693793203461111}  |
| No log        | 2.73  | 30   | 0.5643          | {'accuracy': 0.7256944444444444} | {'f1': 0.7127272727272727} | {'recall': 0.6490066225165563} | {'roc_auc': 0.729612800309373}  |
| No log        | 3.64  | 40   | 0.5743          | {'accuracy': 0.7465277777777778} | {'f1': 0.750853242320819}  | {'recall': 0.7284768211920529} | {'roc_auc': 0.7474500894281433} |
| No log        | 4.55  | 50   | 0.6057          | {'accuracy': 0.7430555555555556} | {'f1': 0.7448275862068965} | {'recall': 0.7152317880794702} | {'roc_auc': 0.7444772079083483} |
| No log        | 5.45  | 60   | 0.6318          | {'accuracy': 0.7291666666666666} | {'f1': 0.7382550335570469} | {'recall': 0.7284768211920529} | {'roc_auc': 0.7292019142456615} |
| No log        | 6.36  | 70   | 0.6664          | {'accuracy': 0.7291666666666666} | {'f1': 0.7450980392156863} | {'recall': 0.7549668874172185} | {'roc_auc': 0.7278484072122589} |
| No log        | 7.27  | 80   | 0.7007          | {'accuracy': 0.7222222222222222} | {'f1': 0.7241379310344827} | {'recall': 0.695364238410596}  | {'roc_auc': 0.7235945279644221} |
| No log        | 8.18  | 90   | 0.7178          | {'accuracy': 0.7326388888888888} | {'f1': 0.7458745874587459} | {'recall': 0.7483443708609272} | {'roc_auc': 0.7318364190071059} |
| No log        | 9.09  | 100  | 0.7396          | {'accuracy': 0.7256944444444444} | {'f1': 0.7285223367697595} | {'recall': 0.7019867549668874} | {'roc_auc': 0.7269057862425677} |
| No log        | 10.0  | 110  | 0.7362          | {'accuracy': 0.7291666666666666} | {'f1': 0.7417218543046359} | {'recall': 0.7417218543046358} | {'roc_auc': 0.7285251607289602} |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0