|
--- |
|
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 |
|
|
