---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-2025
  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. -->

# bert-base-cased-2025

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1722
- Precision: 0.7441
- Recall: 0.775
- F1: 0.7592
- Accuracy: 0.9592

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1764        | 1.0   | 521  | 0.1863          | 0.6678    | 0.7021 | 0.6845 | 0.9460   |
| 0.1187        | 2.0   | 1042 | 0.1648          | 0.7014    | 0.7371 | 0.7188 | 0.9536   |
| 0.0855        | 3.0   | 1563 | 0.1552          | 0.7224    | 0.7673 | 0.7442 | 0.9562   |
| 0.068         | 4.0   | 2084 | 0.1619          | 0.7308    | 0.7694 | 0.7496 | 0.9576   |
| 0.0531        | 5.0   | 2605 | 0.1641          | 0.7431    | 0.7764 | 0.7594 | 0.9594   |
| 0.0428        | 6.0   | 3126 | 0.1722          | 0.7441    | 0.775  | 0.7592 | 0.9592   |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.7.1+cu118
- Datasets 4.2.0
- Tokenizers 0.22.1