---
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: 2.7341
- Pearson: 0.2384
- Mse: 2.7341
- Custom Accuracy: 0.2567
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:------:|:---------------:|:----------------:|
| 0.0188        | 5.5556  | 1000  | 2.9224          | 0.2311  | 2.9224 | 0.2429          | 0.1762           |
| 0.0367        | 11.1111 | 2000  | 2.8363          | 0.2219  | 2.8363 | 0.2524          | 0.1762           |
| 0.0151        | 16.6667 | 3000  | 2.8033          | 0.2131  | 2.8033 | 0.2509          | 0.1762           |
| 0.0377        | 22.2222 | 4000  | 2.9081          | 0.2205  | 2.9081 | 0.2582          | 0.1762           |
| 0.0458        | 27.7778 | 5000  | 2.8001          | 0.2360  | 2.8001 | 0.2611          | 0.1762           |
| 0.0324        | 33.3333 | 6000  | 2.7521          | 0.2377  | 2.7521 | 0.2567          | 0.1762           |
| 0.0479        | 38.8889 | 7000  | 2.7011          | 0.2441  | 2.7011 | 0.2618          | 0.1762           |
| 0.0685        | 44.4444 | 8000  | 2.7119          | 0.2431  | 2.7119 | 0.2611          | 0.1762           |
| 0.0463        | 50.0    | 9000  | 2.7674          | 0.2287  | 2.7674 | 0.2603          | 0.1762           |
| 0.0879        | 55.5556 | 10000 | 2.7357          | 0.2434  | 2.7357 | 0.2676          | 0.1762           |
| 0.0733        | 61.1111 | 11000 | 2.7392          | 0.2374  | 2.7392 | 0.2567          | 0.1762           |
| 0.1541        | 66.6667 | 12000 | 2.7341          | 0.2384  | 2.7341 | 0.2567          | 0.1762           |


### Framework versions

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