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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: 2.7341
- 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
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