---
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: 2.7490
- Pearson: 0.2351
- Mse: 2.7490
- Custom Accuracy: 0.2647
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:------:|:---------------:|:----------------:|
| 0.0163        | 5.5556  | 1000  | 2.7976          | 0.2458  | 2.7976 | 0.2473          | 0.1762           |
| 0.0163        | 11.1111 | 2000  | 2.9602          | 0.2203  | 2.9602 | 0.2480          | 0.1762           |
| 0.0141        | 16.6667 | 3000  | 2.8549          | 0.2317  | 2.8549 | 0.2647          | 0.1762           |
| 0.0218        | 22.2222 | 4000  | 2.8754          | 0.2075  | 2.8754 | 0.2625          | 0.1762           |
| 0.0061        | 27.7778 | 5000  | 2.8724          | 0.2360  | 2.8724 | 0.2683          | 0.1762           |
| 0.0747        | 33.3333 | 6000  | 2.8425          | 0.2218  | 2.8425 | 0.2516          | 0.1762           |
| 0.0291        | 38.8889 | 7000  | 2.8143          | 0.2266  | 2.8143 | 0.2618          | 0.1762           |
| 0.0973        | 44.4444 | 8000  | 2.7617          | 0.2327  | 2.7617 | 0.2647          | 0.1762           |
| 0.0575        | 50.0    | 9000  | 2.7532          | 0.2381  | 2.7532 | 0.2654          | 0.1762           |
| 0.0717        | 55.5556 | 10000 | 2.8212          | 0.2249  | 2.8212 | 0.2603          | 0.1762           |
| 0.0862        | 61.1111 | 11000 | 2.7608          | 0.2334  | 2.7608 | 0.2647          | 0.1762           |
| 0.1598        | 66.6667 | 12000 | 2.7490          | 0.2351  | 2.7490 | 0.2647          | 0.1762           |


### Framework versions

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