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---
license: mit
base_model: DTAI-KULeuven/robbert-2023-dutch-large
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: robbert-2023-dutch-large-upos
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: universal_dependencies
type: universal_dependencies
config: nl_alpino
split: validation
args: nl_alpino
metrics:
- name: Precision
type: precision
value: 0.8288342749653388
- name: Recall
type: recall
value: 0.7844121660589751
- name: F1
type: f1
value: 0.7968496038696615
- name: Accuracy
type: accuracy
value: 0.8897894458638006
---
<!-- 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-upos
This model is a fine-tuned version of [DTAI-KULeuven/robbert-2023-dutch-large](https://huggingface.co/DTAI-KULeuven/robbert-2023-dutch-large) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3606
- Precision: 0.8288
- Recall: 0.7844
- F1: 0.7968
- Accuracy: 0.8898
## 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 | 0.6318 | 0.7041 | 0.6544 | 0.6603 | 0.7663 |
| No log | 2.0 | 876 | 0.5374 | 0.7741 | 0.6827 | 0.7090 | 0.8075 |
| No log | 3.0 | 1314 | 0.4318 | 0.8544 | 0.7431 | 0.7527 | 0.8595 |
| No log | 4.0 | 1752 | 0.4009 | 0.8254 | 0.7677 | 0.7796 | 0.8771 |
| No log | 5.0 | 2190 | 0.3606 | 0.8288 | 0.7844 | 0.7968 | 0.8898 |
| No log | 6.0 | 2628 | 0.3700 | 0.8318 | 0.8002 | 0.8108 | 0.9037 |
| No log | 7.0 | 3066 | 0.3733 | 0.8522 | 0.8024 | 0.8163 | 0.9071 |
| No log | 8.0 | 3504 | 0.3711 | 0.8659 | 0.8203 | 0.8333 | 0.9189 |
| No log | 9.0 | 3942 | 0.3846 | 0.8599 | 0.8222 | 0.8343 | 0.9235 |
| No log | 10.0 | 4380 | 0.3920 | 0.8657 | 0.8263 | 0.8397 | 0.9284 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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