---
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