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---
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
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