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---
license: mit
base_model: DTAI-KULeuven/robbert-2023-dutch-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: robbert-2023-dutch-large_ner
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. -->
# robbert-2023-dutch-large_ner
This model is a fine-tuned version of [DTAI-KULeuven/robbert-2023-dutch-large](https://huggingface.co/DTAI-KULeuven/robbert-2023-dutch-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3927
- Precision: 0.9137
- Recall: 0.9190
- F1: 0.9162
- Accuracy: 0.9515
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 438 | 0.3076 | 0.8616 | 0.8592 | 0.8581 | 0.9133 |
| 0.4231 | 2.0 | 876 | 0.2583 | 0.9068 | 0.8795 | 0.8919 | 0.9338 |
| 0.2222 | 3.0 | 1314 | 0.2809 | 0.8821 | 0.8940 | 0.8864 | 0.9331 |
| 0.1519 | 4.0 | 1752 | 0.2549 | 0.9142 | 0.9207 | 0.9169 | 0.9505 |
| 0.1094 | 5.0 | 2190 | 0.2487 | 0.9105 | 0.9145 | 0.9121 | 0.9482 |
| 0.0731 | 6.0 | 2628 | 0.3406 | 0.9094 | 0.9108 | 0.9097 | 0.9473 |
| 0.0445 | 7.0 | 3066 | 0.3137 | 0.9118 | 0.9164 | 0.9139 | 0.9498 |
| 0.0251 | 8.0 | 3504 | 0.3178 | 0.9166 | 0.9209 | 0.9186 | 0.9526 |
| 0.0251 | 9.0 | 3942 | 0.3886 | 0.9118 | 0.9170 | 0.9143 | 0.9504 |
| 0.0129 | 10.0 | 4380 | 0.3927 | 0.9137 | 0.9190 | 0.9162 | 0.9515 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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