---
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: 0.1649
- Accuracy: 0.9713
- F1: 0.8328
- Precision: 0.8290
- Recall: 0.8375

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.7173        | 0.11  | 50   | 1.7812          | 0.3445   | 0.1475 | 0.1755    | 0.1881 |
| 0.9537        | 0.22  | 100  | 0.9779          | 0.7416   | 0.4399 | 0.4301    | 0.4701 |
| 0.373         | 0.33  | 150  | 0.6741          | 0.8321   | 0.6187 | 0.6018    | 0.6423 |
| 0.2625        | 0.44  | 200  | 0.3897          | 0.9070   | 0.6684 | 0.6503    | 0.6892 |
| 0.2216        | 0.55  | 250  | 0.3971          | 0.9089   | 0.6670 | 0.6465    | 0.6920 |
| 0.1583        | 0.66  | 300  | 0.3601          | 0.9029   | 0.6816 | 0.7957    | 0.6757 |
| 0.1661        | 0.76  | 350  | 0.2266          | 0.9180   | 0.6950 | 0.7317    | 0.7019 |
| 0.112         | 0.87  | 400  | 0.2525          | 0.9494   | 0.8020 | 0.7955    | 0.8132 |
| 0.0857        | 0.98  | 450  | 0.2701          | 0.9459   | 0.8124 | 0.8060    | 0.8232 |
| 0.1223        | 1.09  | 500  | 0.1781          | 0.9631   | 0.8281 | 0.8251    | 0.8319 |
| 0.0641        | 1.2   | 550  | 0.2162          | 0.9552   | 0.8236 | 0.8229    | 0.8258 |
| 0.0907        | 1.31  | 600  | 0.1486          | 0.9705   | 0.8351 | 0.8357    | 0.8346 |
| 0.0738        | 1.42  | 650  | 0.1380          | 0.9696   | 0.8300 | 0.8276    | 0.8331 |
| 0.0946        | 1.53  | 700  | 0.1577          | 0.9705   | 0.8357 | 0.8370    | 0.8345 |
| 0.0476        | 1.64  | 750  | 0.1497          | 0.9707   | 0.8349 | 0.8337    | 0.8363 |
| 0.0873        | 1.75  | 800  | 0.1722          | 0.9655   | 0.8318 | 0.8288    | 0.8353 |
| 0.0487        | 1.86  | 850  | 0.1782          | 0.9647   | 0.8312 | 0.8283    | 0.8345 |
| 0.0548        | 1.97  | 900  | 0.1610          | 0.9666   | 0.8336 | 0.8329    | 0.8346 |
| 0.0492        | 2.07  | 950  | 0.1423          | 0.9688   | 0.8338 | 0.8287    | 0.8393 |
| 0.0279        | 2.18  | 1000 | 0.1707          | 0.9669   | 0.8325 | 0.8287    | 0.8371 |
| 0.0401        | 2.29  | 1050 | 0.1583          | 0.9688   | 0.8337 | 0.8300    | 0.8382 |
| 0.0313        | 2.4   | 1100 | 0.1799          | 0.9647   | 0.8306 | 0.8274    | 0.8348 |
| 0.025         | 2.51  | 1150 | 0.1661          | 0.9669   | 0.8320 | 0.8311    | 0.8335 |
| 0.0043        | 2.62  | 1200 | 0.1933          | 0.9647   | 0.8305 | 0.8280    | 0.8339 |
| 0.0115        | 2.73  | 1250 | 0.1570          | 0.9696   | 0.8328 | 0.8308    | 0.8352 |
| 0.0198        | 2.84  | 1300 | 0.1538          | 0.9702   | 0.8340 | 0.8328    | 0.8355 |
| 0.0085        | 2.95  | 1350 | 0.1591          | 0.9694   | 0.8337 | 0.8327    | 0.8351 |


### Framework versions

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