metadata

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

robbert-2023-dutch-large-upos

This model is a fine-tuned version of 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