|
--- |
|
license: mit |
|
base_model: pdelobelle/robbert-v2-dutch-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: robbert2809_flow |
|
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. --> |
|
|
|
# robbert2809_flow |
|
|
|
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3597 |
|
- Precision: 0.7239 |
|
- Recall: 0.7197 |
|
- F1: 0.7218 |
|
- Accuracy: 0.8938 |
|
|
|
## 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: 2.5e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 8 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 1.0 | 118 | 0.4622 | 0.6165 | 0.5828 | 0.5992 | 0.8584 | |
|
| No log | 2.0 | 236 | 0.4044 | 0.6675 | 0.6620 | 0.6647 | 0.8772 | |
|
| No log | 3.0 | 354 | 0.3787 | 0.6945 | 0.7220 | 0.708 | 0.8904 | |
|
| No log | 4.0 | 472 | 0.3597 | 0.7239 | 0.7197 | 0.7218 | 0.8938 | |
|
| 0.4344 | 5.0 | 590 | 0.3615 | 0.7273 | 0.7430 | 0.7351 | 0.8974 | |
|
| 0.4344 | 6.0 | 708 | 0.3909 | 0.7410 | 0.7418 | 0.7414 | 0.8977 | |
|
| 0.4344 | 7.0 | 826 | 0.3868 | 0.7394 | 0.7343 | 0.7368 | 0.8966 | |
|
| 0.4344 | 8.0 | 944 | 0.3858 | 0.7402 | 0.7389 | 0.7396 | 0.8977 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.2 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
