---
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: 2.7711
- 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: 1e-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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 0.4658        | 5.5556  | 1000  | 2.8590          | 0.1762   |
| 0.4956        | 11.1111 | 2000  | 2.9260          | 0.1762   |
| 0.349         | 16.6667 | 3000  | 2.9019          | 0.1762   |
| 0.2575        | 22.2222 | 4000  | 2.8843          | 0.1762   |
| 0.2261        | 27.7778 | 5000  | 2.8242          | 0.1762   |
| 0.2592        | 33.3333 | 6000  | 2.8276          | 0.1762   |
| 0.1979        | 38.8889 | 7000  | 2.7752          | 0.1762   |
| 0.2125        | 44.4444 | 8000  | 2.7567          | 0.1762   |
| 0.157         | 50.0    | 9000  | 2.7864          | 0.1762   |
| 0.1751        | 55.5556 | 10000 | 2.7711          | 0.1762   |


### Framework versions

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