|
--- |
|
license: mit |
|
base_model: pdelobelle/robbert-v2-dutch-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- recall |
|
- accuracy |
|
model-index: |
|
- name: robbert0210_lrate2.5b8 |
|
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. --> |
|
|
|
# robbert0210_lrate2.5b8 |
|
|
|
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: 0.3692 |
|
- Precisions: 0.7993 |
|
- Recall: 0.7287 |
|
- F-measure: 0.7307 |
|
- Accuracy: 0.8915 |
|
|
|
## 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: 8 |
|
- eval_batch_size: 8 |
|
- 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 | Precisions | Recall | F-measure | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:| |
|
| No log | 1.0 | 471 | 0.4360 | 0.8519 | 0.6630 | 0.6789 | 0.8678 | |
|
| 0.6455 | 2.0 | 942 | 0.3692 | 0.7993 | 0.7287 | 0.7307 | 0.8915 | |
|
| 0.3291 | 3.0 | 1413 | 0.3768 | 0.7658 | 0.7398 | 0.7397 | 0.8986 | |
|
| 0.2114 | 4.0 | 1884 | 0.4194 | 0.7951 | 0.7452 | 0.7532 | 0.9048 | |
|
| 0.1457 | 5.0 | 2355 | 0.4626 | 0.7756 | 0.7536 | 0.7620 | 0.9021 | |
|
| 0.0955 | 6.0 | 2826 | 0.5145 | 0.8075 | 0.7700 | 0.7858 | 0.9048 | |
|
| 0.0641 | 7.0 | 3297 | 0.5118 | 0.8113 | 0.7997 | 0.8045 | 0.9100 | |
|
| 0.0484 | 8.0 | 3768 | 0.5204 | 0.8052 | 0.7952 | 0.7995 | 0.9093 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.3 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|