---
language:
- nl
license: apache-2.0
base_model: bert-base-uncased
tags:
- abc
- generated_from_trainer
datasets:
- stsb_multi_mt
metrics:
- accuracy
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: 2.7821
- 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: 5e-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: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 0.2457        | 5.5556  | 1000  | 2.4913          | 0.1762   |
| 0.1759        | 11.1111 | 2000  | 2.8424          | 0.1762   |
| 0.1458        | 16.6667 | 3000  | 2.9765          | 0.1762   |
| 0.1132        | 22.2222 | 4000  | 2.7163          | 0.1762   |
| 0.1118        | 27.7778 | 5000  | 2.7272          | 0.1762   |
| 0.1272        | 33.3333 | 6000  | 2.8354          | 0.1762   |
| 0.1233        | 38.8889 | 7000  | 2.6948          | 0.1762   |
| 0.1161        | 44.4444 | 8000  | 2.7358          | 0.1762   |
| 0.0736        | 50.0    | 9000  | 2.7748          | 0.1762   |
| 0.0924        | 55.5556 | 10000 | 2.7821          | 0.1762   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1