---
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_ST_DA_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. -->

# BERT_ST_DA_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: 0.1864
- Precision: 0.9653
- Recall: 0.9736
- F1: 0.9694
- Accuracy: 0.9655

## 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: 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   | 495  | 0.1375          | 0.9614    | 0.9662 | 0.9638 | 0.9590   |
| 0.2452        | 2.0   | 990  | 0.1262          | 0.9652    | 0.9705 | 0.9679 | 0.9632   |
| 0.0853        | 3.0   | 1485 | 0.1396          | 0.9638    | 0.9677 | 0.9657 | 0.9619   |
| 0.0479        | 4.0   | 1980 | 0.1485          | 0.9637    | 0.9729 | 0.9683 | 0.9651   |
| 0.0275        | 5.0   | 2475 | 0.1641          | 0.9633    | 0.9727 | 0.9680 | 0.9642   |
| 0.0181        | 6.0   | 2970 | 0.1753          | 0.9641    | 0.9739 | 0.9689 | 0.9655   |
| 0.0112        | 7.0   | 3465 | 0.1675          | 0.9659    | 0.9724 | 0.9691 | 0.9657   |
| 0.0074        | 8.0   | 3960 | 0.1817          | 0.9650    | 0.9745 | 0.9697 | 0.9663   |
| 0.0054        | 9.0   | 4455 | 0.1878          | 0.9652    | 0.9737 | 0.9694 | 0.9657   |
| 0.0038        | 10.0  | 4950 | 0.1864          | 0.9653    | 0.9736 | 0.9694 | 0.9655   |


### Framework versions

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