---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: V10-distilbert-text-classification-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. -->

# V10-distilbert-text-classification-model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2088
- Accuracy: 0.9546
- F1: 0.8213
- Precision: 0.8192
- Recall: 0.8243

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.5687        | 0.11  | 50   | 2.1779          | 0.1200   | 0.0687 | 0.0913    | 0.0550 |
| 1.1872        | 0.22  | 100  | 1.1610          | 0.6475   | 0.4010 | 0.4470    | 0.3901 |
| 0.6574        | 0.33  | 150  | 0.9675          | 0.6300   | 0.3389 | 0.4603    | 0.3683 |
| 0.548         | 0.44  | 200  | 0.6524          | 0.8165   | 0.5001 | 0.4898    | 0.5117 |
| 0.3506        | 0.55  | 250  | 0.6884          | 0.7985   | 0.5037 | 0.6467    | 0.5073 |
| 0.3233        | 0.66  | 300  | 0.5294          | 0.8553   | 0.5177 | 0.5012    | 0.5353 |
| 0.3211        | 0.76  | 350  | 0.5028          | 0.8553   | 0.5989 | 0.5974    | 0.6058 |
| 0.2611        | 0.87  | 400  | 0.7703          | 0.8387   | 0.6148 | 0.5917    | 0.6521 |
| 0.3259        | 0.98  | 450  | 0.6041          | 0.8335   | 0.6121 | 0.5925    | 0.6442 |
| 0.2196        | 1.09  | 500  | 0.5109          | 0.8737   | 0.6300 | 0.6026    | 0.6665 |
| 0.1712        | 1.2   | 550  | 0.6030          | 0.8488   | 0.6231 | 0.7507    | 0.6528 |
| 0.175         | 1.31  | 600  | 0.5176          | 0.8783   | 0.6549 | 0.7620    | 0.6752 |
| 0.257         | 1.42  | 650  | 0.3901          | 0.8873   | 0.6462 | 0.7626    | 0.6783 |
| 0.1759        | 1.53  | 700  | 0.4053          | 0.8955   | 0.6774 | 0.7709    | 0.6947 |
| 0.1309        | 1.64  | 750  | 0.3624          | 0.9251   | 0.7857 | 0.7883    | 0.7927 |
| 0.2394        | 1.75  | 800  | 0.3332          | 0.9171   | 0.7749 | 0.7751    | 0.7848 |
| 0.165         | 1.86  | 850  | 0.6878          | 0.8510   | 0.6446 | 0.6970    | 0.6394 |
| 0.1421        | 1.97  | 900  | 0.3987          | 0.8718   | 0.6345 | 0.7590    | 0.6170 |
| 0.1361        | 2.07  | 950  | 0.3393          | 0.9253   | 0.7738 | 0.7734    | 0.7872 |
| 0.1292        | 2.18  | 1000 | 0.3194          | 0.9300   | 0.8017 | 0.8128    | 0.7930 |
| 0.0754        | 2.29  | 1050 | 0.3485          | 0.9245   | 0.7871 | 0.7842    | 0.8006 |
| 0.1345        | 2.4   | 1100 | 0.2564          | 0.9387   | 0.8022 | 0.7974    | 0.8104 |
| 0.0593        | 2.51  | 1150 | 0.2132          | 0.9541   | 0.8159 | 0.8222    | 0.8109 |
| 0.1019        | 2.62  | 1200 | 0.2234          | 0.9472   | 0.8070 | 0.8044    | 0.8127 |
| 0.0735        | 2.73  | 1250 | 0.2183          | 0.9535   | 0.8155 | 0.8250    | 0.8072 |
| 0.113         | 2.84  | 1300 | 0.2716          | 0.9128   | 0.7208 | 0.8006    | 0.7118 |
| 0.0838        | 2.95  | 1350 | 0.2957          | 0.9330   | 0.7999 | 0.7929    | 0.8128 |
| 0.0797        | 3.06  | 1400 | 0.2758          | 0.9437   | 0.8075 | 0.8117    | 0.8058 |
| 0.0612        | 3.17  | 1450 | 0.2450          | 0.9139   | 0.7200 | 0.7983    | 0.7140 |
| 0.0492        | 3.28  | 1500 | 0.2501          | 0.9480   | 0.8089 | 0.8089    | 0.8118 |
| 0.0294        | 3.38  | 1550 | 0.2745          | 0.9374   | 0.8035 | 0.8011    | 0.8084 |
| 0.0248        | 3.49  | 1600 | 0.2561          | 0.9434   | 0.8099 | 0.8073    | 0.8144 |
| 0.0621        | 3.6   | 1650 | 0.2312          | 0.9491   | 0.8135 | 0.8190    | 0.8094 |
| 0.0541        | 3.71  | 1700 | 0.2512          | 0.9472   | 0.8140 | 0.8177    | 0.8119 |
| 0.0509        | 3.82  | 1750 | 0.2195          | 0.9516   | 0.8145 | 0.8173    | 0.8125 |
| 0.0452        | 3.93  | 1800 | 0.2418          | 0.9480   | 0.8140 | 0.8175    | 0.8120 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2