---
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