metadata

license: mit
base_model: deepset/gbert-large
tags:
  - generated_from_trainer
datasets:
  - universal_dependencies
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: gbert-large-upos
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: universal_dependencies
          type: universal_dependencies
          config: de_gsd
          split: validation
          args: de_gsd
        metrics:
          - name: Precision
            type: precision
            value: 0.825291976991079
          - name: Recall
            type: recall
            value: 0.7826990832215603
          - name: F1
            type: f1
            value: 0.7912197452035137
          - name: Accuracy
            type: accuracy
            value: 0.9413806706114398

gbert-large-upos

This model is a fine-tuned version of deepset/gbert-large on the universal_dependencies dataset. It achieves the following results on the evaluation set:

Loss: 0.1996
Precision: 0.8253
Recall: 0.7827
F1: 0.7912
Accuracy: 0.9414

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	0.3197	0.8098	0.7291	0.7486	0.8936
No log	2.0	876	0.2261	0.8287	0.7679	0.7832	0.9269
No log	3.0	1314	0.1996	0.8253	0.7827	0.7912	0.9414
No log	4.0	1752	0.2183	0.8162	0.8006	0.8041	0.9435
No log	5.0	2190	0.2120	0.8198	0.8025	0.8074	0.9496
No log	6.0	2628	0.2339	0.8207	0.8068	0.8116	0.9489
No log	7.0	3066	0.2728	0.8156	0.8045	0.8071	0.9486
No log	8.0	3504	0.2790	0.8205	0.8110	0.8132	0.9527
No log	9.0	3942	0.2854	0.8306	0.8096	0.8146	0.9527
No log	10.0	4380	0.2906	0.8299	0.8115	0.8151	0.9534

Framework versions

Transformers 4.42.4
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1