metadata
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: []
bert-base-uncased-FinedTuned
This model is a fine-tuned version of 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