---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: V11-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. -->

# V11-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: 0.4161
- Accuracy: 0.8486
- F1: 0.6847
- Precision: 0.7825
- Recall: 0.7045

## 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: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.7046        | 0.11  | 50   | 1.7455          | 0.3120   | 0.1170 | 0.2748    | 0.1473 |
| 0.7733        | 0.22  | 100  | 0.6968          | 0.8069   | 0.4921 | 0.4803    | 0.5091 |
| 0.2603        | 0.33  | 150  | 0.5350          | 0.8903   | 0.6622 | 0.6451    | 0.6806 |
| 0.2477        | 0.44  | 200  | 0.4257          | 0.8841   | 0.6558 | 0.6363    | 0.6775 |
| 0.1487        | 0.55  | 250  | 0.3818          | 0.9150   | 0.6781 | 0.6632    | 0.6943 |
| 0.1528        | 0.66  | 300  | 0.3854          | 0.9048   | 0.6753 | 0.6694    | 0.6820 |
| 0.1611        | 0.76  | 350  | 0.2742          | 0.9169   | 0.6783 | 0.8038    | 0.6926 |
| 0.0925        | 0.87  | 400  | 0.2712          | 0.9155   | 0.6796 | 0.6665    | 0.6938 |
| 0.0954        | 0.98  | 450  | 0.3096          | 0.9119   | 0.6948 | 0.7995    | 0.7018 |


### Framework versions

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