---
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: 2.7638
- Pearson: 0.2339
- Mse: 2.7638
- Custom Accuracy: 0.2603
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:------:|:---------------:|:----------------:|
| 0.0219        | 5.5556  | 1000  | 2.8140          | 0.2324  | 2.8140 | 0.2437          | 0.1762           |
| 0.0195        | 11.1111 | 2000  | 2.9679          | 0.2078  | 2.9679 | 0.2618          | 0.1762           |
| 0.0184        | 16.6667 | 3000  | 2.7712          | 0.2476  | 2.7712 | 0.2683          | 0.1762           |
| 0.0213        | 22.2222 | 4000  | 2.7564          | 0.2486  | 2.7564 | 0.2661          | 0.1762           |
| 0.0222        | 27.7778 | 5000  | 2.8691          | 0.2333  | 2.8691 | 0.2596          | 0.1762           |
| 0.0151        | 33.3333 | 6000  | 2.7762          | 0.2451  | 2.7762 | 0.2560          | 0.1762           |
| 0.0318        | 38.8889 | 7000  | 2.8121          | 0.2370  | 2.8121 | 0.2647          | 0.1762           |
| 0.0616        | 44.4444 | 8000  | 2.8343          | 0.2195  | 2.8343 | 0.2560          | 0.1762           |
| 0.0335        | 50.0    | 9000  | 2.8070          | 0.2259  | 2.8070 | 0.2676          | 0.1762           |
| 0.0553        | 55.5556 | 10000 | 2.7934          | 0.2330  | 2.7934 | 0.2531          | 0.1762           |
| 0.0718        | 61.1111 | 11000 | 2.7822          | 0.2286  | 2.7822 | 0.2603          | 0.1762           |
| 0.1741        | 66.6667 | 12000 | 2.7638          | 0.2339  | 2.7638 | 0.2603          | 0.1762           |


### Framework versions

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