---
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: VF_BERT_ST_1000
  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. -->

# VF_BERT_ST_1000

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: 0.1765
- Precision: 0.9705
- Recall: 0.9755
- F1: 0.9730
- Accuracy: 0.9636

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 259  | 0.1575          | 0.9595    | 0.9658 | 0.9626 | 0.9503   |
| 0.2118        | 2.0   | 518  | 0.1388          | 0.9660    | 0.9743 | 0.9701 | 0.9597   |
| 0.2118        | 3.0   | 777  | 0.1366          | 0.9688    | 0.9734 | 0.9711 | 0.9613   |
| 0.0546        | 4.0   | 1036 | 0.1488          | 0.9673    | 0.9726 | 0.9699 | 0.9603   |
| 0.0546        | 5.0   | 1295 | 0.1663          | 0.9675    | 0.9736 | 0.9705 | 0.9609   |
| 0.0251        | 6.0   | 1554 | 0.1673          | 0.9685    | 0.9750 | 0.9717 | 0.9628   |
| 0.0251        | 7.0   | 1813 | 0.1708          | 0.9707    | 0.9753 | 0.9730 | 0.9639   |
| 0.0133        | 8.0   | 2072 | 0.1707          | 0.9701    | 0.9742 | 0.9721 | 0.9631   |
| 0.0133        | 9.0   | 2331 | 0.1771          | 0.9703    | 0.9754 | 0.9728 | 0.9635   |
| 0.0094        | 10.0  | 2590 | 0.1765          | 0.9705    | 0.9755 | 0.9730 | 0.9636   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1