---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: final_V1-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. -->

# final_V1-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: 0.1232
- Accuracy: 0.9743
- F1: 0.8372
- Precision: 0.8341
- Recall: 0.8408

## 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.7299        | 0.11  | 50   | 1.8504          | 0.2773   | 0.0874 | 0.1995    | 0.1296 |
| 0.7996        | 0.22  | 100  | 0.7195          | 0.8242   | 0.5075 | 0.5040    | 0.5175 |
| 0.3009        | 0.33  | 150  | 0.4573          | 0.8991   | 0.6736 | 0.6652    | 0.6833 |
| 0.2559        | 0.44  | 200  | 0.5667          | 0.8701   | 0.6478 | 0.6293    | 0.6709 |
| 0.1636        | 0.55  | 250  | 0.4599          | 0.9040   | 0.6752 | 0.6662    | 0.6864 |
| 0.1813        | 0.66  | 300  | 0.3651          | 0.9103   | 0.6823 | 0.8187    | 0.6881 |
| 0.1695        | 0.76  | 350  | 0.3603          | 0.9114   | 0.6969 | 0.8090    | 0.7002 |
| 0.128         | 0.87  | 400  | 0.3779          | 0.9191   | 0.7197 | 0.7975    | 0.7165 |
| 0.0976        | 0.98  | 450  | 0.3244          | 0.9095   | 0.7308 | 0.7394    | 0.7275 |
| 0.0867        | 1.09  | 500  | 0.1825          | 0.9617   | 0.8283 | 0.8278    | 0.8294 |
| 0.059         | 1.2   | 550  | 0.1847          | 0.9614   | 0.8258 | 0.8234    | 0.8291 |
| 0.0719        | 1.31  | 600  | 0.1783          | 0.9590   | 0.8273 | 0.8263    | 0.8287 |
| 0.0504        | 1.42  | 650  | 0.1311          | 0.9702   | 0.8325 | 0.8337    | 0.8315 |
| 0.0525        | 1.53  | 700  | 0.1531          | 0.9694   | 0.8325 | 0.8355    | 0.8295 |
| 0.0193        | 1.64  | 750  | 0.1675          | 0.9666   | 0.8311 | 0.8291    | 0.8334 |
| 0.081         | 1.75  | 800  | 0.1531          | 0.9705   | 0.8329 | 0.8306    | 0.8354 |
| 0.0453        | 1.86  | 850  | 0.2261          | 0.9295   | 0.7451 | 0.8081    | 0.7321 |
| 0.0401        | 1.97  | 900  | 0.2015          | 0.9601   | 0.8283 | 0.8263    | 0.8315 |
| 0.0686        | 2.07  | 950  | 0.1674          | 0.9642   | 0.8291 | 0.8255    | 0.8337 |
| 0.0353        | 2.18  | 1000 | 0.1639          | 0.9664   | 0.8303 | 0.8264    | 0.8350 |
| 0.0345        | 2.29  | 1050 | 0.1830          | 0.9639   | 0.8295 | 0.8264    | 0.8335 |
| 0.0212        | 2.4   | 1100 | 0.1978          | 0.9634   | 0.8291 | 0.8258    | 0.8336 |
| 0.0028        | 2.51  | 1150 | 0.1864          | 0.9653   | 0.8305 | 0.8273    | 0.8344 |
| 0.0023        | 2.62  | 1200 | 0.1906          | 0.9661   | 0.8309 | 0.8277    | 0.8348 |
| 0.0076        | 2.73  | 1250 | 0.1826          | 0.9669   | 0.8307 | 0.8278    | 0.8341 |
| 0.0272        | 2.84  | 1300 | 0.1830          | 0.9666   | 0.8306 | 0.8283    | 0.8335 |
| 0.0065        | 2.95  | 1350 | 0.1908          | 0.9661   | 0.8303 | 0.8278    | 0.8333 |


### Framework versions

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