---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 14apr-bert-uncased
  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. -->

# 14apr-bert-uncased

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1141
- Precision: 0.9797
- Recall: 0.9796
- F1: 0.9797
- Accuracy: 0.9774

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1405        | 1.0   | 2500  | 0.1016          | 0.9731    | 0.9761 | 0.9746 | 0.9721   |
| 0.0994        | 2.0   | 5000  | 0.0939          | 0.9776    | 0.9774 | 0.9775 | 0.9750   |
| 0.0731        | 3.0   | 7500  | 0.0968          | 0.9783    | 0.9790 | 0.9787 | 0.9767   |
| 0.045         | 4.0   | 10000 | 0.1075          | 0.9790    | 0.9798 | 0.9794 | 0.9773   |
| 0.035         | 5.0   | 12500 | 0.1141          | 0.9797    | 0.9796 | 0.9797 | 0.9774   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2