---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: V4-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. -->

# V4-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: 0.1669
- Accuracy: 0.9625
- F1: 0.8267
- Precision: 0.8224
- Recall: 0.8323

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.5437        | 0.11  | 50   | 1.6872          | 0.2896   | 0.0712 | 0.1912    | 0.1327 |
| 0.7301        | 0.22  | 100  | 0.7109          | 0.8078   | 0.4981 | 0.4962    | 0.5091 |
| 0.3015        | 0.33  | 150  | 0.4554          | 0.8988   | 0.6675 | 0.6601    | 0.6756 |
| 0.2653        | 0.44  | 200  | 0.5038          | 0.8682   | 0.6422 | 0.6256    | 0.6615 |
| 0.1756        | 0.55  | 250  | 0.4243          | 0.9106   | 0.6947 | 0.8014    | 0.7020 |
| 0.2054        | 0.66  | 300  | 0.3561          | 0.9087   | 0.6890 | 0.8099    | 0.6808 |
| 0.1555        | 0.76  | 350  | 0.2747          | 0.9166   | 0.7225 | 0.8152    | 0.7111 |
| 0.1215        | 0.87  | 400  | 0.1767          | 0.9625   | 0.8282 | 0.8258    | 0.8313 |
| 0.1221        | 0.98  | 450  | 0.3365          | 0.9243   | 0.7956 | 0.7887    | 0.8103 |
| 0.1382        | 1.09  | 500  | 0.2132          | 0.9554   | 0.8235 | 0.8221    | 0.8263 |
| 0.0829        | 1.2   | 550  | 0.2316          | 0.9524   | 0.8216 | 0.8178    | 0.8279 |
| 0.0978        | 1.31  | 600  | 0.1630          | 0.9680   | 0.8356 | 0.8353    | 0.8363 |
| 0.0876        | 1.42  | 650  | 0.1157          | 0.9718   | 0.8363 | 0.8359    | 0.8370 |
| 0.0818        | 1.53  | 700  | 0.1504          | 0.9669   | 0.8338 | 0.8389    | 0.8289 |
| 0.0642        | 1.64  | 750  | 0.1519          | 0.9713   | 0.8364 | 0.8384    | 0.8347 |
| 0.0984        | 1.75  | 800  | 0.1716          | 0.9666   | 0.8334 | 0.8360    | 0.8310 |
| 0.0724        | 1.86  | 850  | 0.1554          | 0.9694   | 0.8358 | 0.8366    | 0.8353 |
| 0.0546        | 1.97  | 900  | 0.1799          | 0.9642   | 0.8329 | 0.8339    | 0.8328 |
| 0.0559        | 2.07  | 950  | 0.1864          | 0.9642   | 0.8323 | 0.8294    | 0.8363 |
| 0.0396        | 2.18  | 1000 | 0.2251          | 0.9601   | 0.8286 | 0.8262    | 0.8327 |
| 0.0509        | 2.29  | 1050 | 0.1233          | 0.9721   | 0.8341 | 0.8358    | 0.8325 |
| 0.0528        | 2.4   | 1100 | 0.1674          | 0.9669   | 0.8345 | 0.8336    | 0.8360 |
| 0.0269        | 2.51  | 1150 | 0.1662          | 0.9686   | 0.8365 | 0.8350    | 0.8384 |
| 0.009         | 2.62  | 1200 | 0.1835          | 0.9661   | 0.8341 | 0.8310    | 0.8378 |
| 0.0193        | 2.73  | 1250 | 0.1949          | 0.9666   | 0.8339 | 0.8342    | 0.8340 |
| 0.0502        | 2.84  | 1300 | 0.1532          | 0.9694   | 0.8327 | 0.8305    | 0.8351 |
| 0.027         | 2.95  | 1350 | 0.1821          | 0.9680   | 0.8355 | 0.8351    | 0.8365 |
| 0.0271        | 3.06  | 1400 | 0.2110          | 0.9344   | 0.7545 | 0.8226    | 0.7387 |
| 0.0149        | 3.17  | 1450 | 0.2127          | 0.9631   | 0.8336 | 0.8337    | 0.8345 |
| 0.018         | 3.28  | 1500 | 0.1662          | 0.9710   | 0.8366 | 0.8347    | 0.8388 |
| 0.0067        | 3.38  | 1550 | 0.1927          | 0.9669   | 0.8340 | 0.8309    | 0.8378 |
| 0.0102        | 3.49  | 1600 | 0.1735          | 0.9705   | 0.8363 | 0.8333    | 0.8398 |
| 0.0035        | 3.6   | 1650 | 0.1687          | 0.9705   | 0.8356 | 0.8350    | 0.8366 |
| 0.0014        | 3.71  | 1700 | 0.1689          | 0.9713   | 0.8363 | 0.8359    | 0.8370 |
| 0.0147        | 3.82  | 1750 | 0.1648          | 0.9710   | 0.8361 | 0.8355    | 0.8369 |
| 0.0085        | 3.93  | 1800 | 0.1667          | 0.9716   | 0.8364 | 0.8358    | 0.8371 |


### Framework versions

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