---
language:
- nl
license: apache-2.0
base_model: bert-base-uncased
tags:
- abc
- generated_from_trainer
datasets:
- stsb_multi_mt
model-index:
- name: bert-base-uncased-FinedTuned
  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-uncased-FinedTuned

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the stsb_multi_mt dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7758
- Pearson: 0.2352
- Mse: 2.7758
- Custom Accuracy: 0.2611
- Dataset Accuracy: 0.1762

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 12000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Pearson | Mse    | Custom Accuracy | Dataset Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:------:|:---------------:|:----------------:|
| 0.028         | 5.5556  | 1000  | 2.7386          | 0.2467  | 2.7386 | 0.2502          | 0.1762           |
| 0.0269        | 11.1111 | 2000  | 2.8265          | 0.2229  | 2.8265 | 0.2589          | 0.1762           |
| 0.0088        | 16.6667 | 3000  | 2.8485          | 0.2219  | 2.8485 | 0.2654          | 0.1762           |
| 0.0141        | 22.2222 | 4000  | 2.8855          | 0.2086  | 2.8855 | 0.2661          | 0.1762           |
| 0.0099        | 27.7778 | 5000  | 2.8081          | 0.2328  | 2.8081 | 0.2632          | 0.1762           |
| 0.0248        | 33.3333 | 6000  | 2.7765          | 0.2309  | 2.7765 | 0.2625          | 0.1762           |
| 0.0353        | 38.8889 | 7000  | 2.8126          | 0.2296  | 2.8126 | 0.2748          | 0.1762           |
| 0.0892        | 44.4444 | 8000  | 2.8362          | 0.2327  | 2.8362 | 0.2567          | 0.1762           |
| 0.0488        | 50.0    | 9000  | 2.7667          | 0.2363  | 2.7667 | 0.2596          | 0.1762           |
| 0.0538        | 55.5556 | 10000 | 2.7885          | 0.2363  | 2.7885 | 0.2632          | 0.1762           |
| 0.0829        | 61.1111 | 11000 | 2.7837          | 0.2348  | 2.7837 | 0.2647          | 0.1762           |
| 0.1473        | 66.6667 | 12000 | 2.7758          | 0.2352  | 2.7758 | 0.2611          | 0.1762           |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1