---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: final_V1-bert-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-bert-text-classification-model

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1498
- Accuracy: 0.9713
- F1: 0.8341
- Precision: 0.8330
- Recall: 0.8356

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.6252        | 0.11  | 50   | 1.7120          | 0.3451   | 0.1545 | 0.2382    | 0.1762 |
| 0.7857        | 0.22  | 100  | 0.7296          | 0.8209   | 0.4973 | 0.4815    | 0.5166 |
| 0.2986        | 0.33  | 150  | 0.5358          | 0.8830   | 0.6565 | 0.6402    | 0.6744 |
| 0.2612        | 0.44  | 200  | 0.4678          | 0.9035   | 0.6704 | 0.6621    | 0.6795 |
| 0.153         | 0.55  | 250  | 0.4325          | 0.9065   | 0.6648 | 0.6446    | 0.6879 |
| 0.2274        | 0.66  | 300  | 0.3498          | 0.8969   | 0.6440 | 0.6237    | 0.6677 |
| 0.1449        | 0.76  | 350  | 0.4254          | 0.8964   | 0.6885 | 0.8012    | 0.6895 |
| 0.1695        | 0.87  | 400  | 0.3484          | 0.9248   | 0.7301 | 0.7857    | 0.7208 |
| 0.1206        | 0.98  | 450  | 0.3075          | 0.9218   | 0.7351 | 0.7586    | 0.7279 |
| 0.1142        | 1.09  | 500  | 0.2241          | 0.9467   | 0.8063 | 0.7964    | 0.8218 |
| 0.0642        | 1.2   | 550  | 0.2527          | 0.9491   | 0.8159 | 0.8106    | 0.8239 |
| 0.0935        | 1.31  | 600  | 0.1961          | 0.9601   | 0.8216 | 0.8270    | 0.8173 |
| 0.0755        | 1.42  | 650  | 0.1290          | 0.9691   | 0.8272 | 0.8348    | 0.8201 |
| 0.108         | 1.53  | 700  | 0.1712          | 0.9612   | 0.8215 | 0.8311    | 0.8130 |
| 0.0667        | 1.64  | 750  | 0.1449          | 0.9716   | 0.8354 | 0.8371    | 0.8338 |
| 0.0925        | 1.75  | 800  | 0.1193          | 0.9721   | 0.8345 | 0.8353    | 0.8337 |
| 0.0769        | 1.86  | 850  | 0.1477          | 0.9675   | 0.8299 | 0.8270    | 0.8334 |
| 0.0558        | 1.97  | 900  | 0.1988          | 0.9606   | 0.8239 | 0.8194    | 0.8299 |
| 0.0379        | 2.07  | 950  | 0.1546          | 0.9694   | 0.8319 | 0.8300    | 0.8340 |
| 0.0358        | 2.18  | 1000 | 0.1871          | 0.9655   | 0.8295 | 0.8283    | 0.8312 |
| 0.0248        | 2.29  | 1050 | 0.1631          | 0.9661   | 0.8303 | 0.8278    | 0.8333 |
| 0.0412        | 2.4   | 1100 | 0.1688          | 0.9658   | 0.8283 | 0.8235    | 0.8340 |
| 0.0096        | 2.51  | 1150 | 0.1726          | 0.9661   | 0.8316 | 0.8297    | 0.8342 |
| 0.0025        | 2.62  | 1200 | 0.1808          | 0.9653   | 0.8300 | 0.8261    | 0.8348 |
| 0.0074        | 2.73  | 1250 | 0.1697          | 0.9677   | 0.8323 | 0.8291    | 0.8360 |
| 0.028         | 2.84  | 1300 | 0.1630          | 0.9705   | 0.8359 | 0.8344    | 0.8377 |
| 0.0292        | 2.95  | 1350 | 0.1743          | 0.9696   | 0.8352 | 0.8341    | 0.8366 |


### Framework versions

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