metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: req_mod_ner_modelv2
    results: []

req_mod_ner_modelv2

This model is a fine-tuned version of pdelobelle/robbert-v2-dutch-ner on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.7795
Precision: 0.5932
Recall: 0.6034
F1: 0.5983
Accuracy: 0.9193

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: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 32

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	120	0.5525	0.6538	0.2931	0.4048	0.9007
No log	2.0	240	0.3596	0.5447	0.5776	0.5607	0.9120
No log	3.0	360	0.4229	0.5495	0.4310	0.4831	0.9031
No log	4.0	480	0.4091	0.5435	0.6466	0.5906	0.9185
0.2667	5.0	600	0.5476	0.6837	0.5776	0.6262	0.9233
0.2667	6.0	720	0.4703	0.5610	0.5948	0.5774	0.9185
0.2667	7.0	840	0.5904	0.5897	0.5948	0.5923	0.9185
0.2667	8.0	960	0.6285	0.5772	0.6121	0.5941	0.9177
0.0436	9.0	1080	0.7077	0.6095	0.5517	0.5792	0.9153
0.0436	10.0	1200	0.6974	0.5929	0.5776	0.5852	0.9177
0.0436	11.0	1320	0.6777	0.5205	0.6552	0.5802	0.9104
0.0436	12.0	1440	0.6601	0.6174	0.6121	0.6147	0.9201
0.0216	13.0	1560	0.6536	0.5809	0.6810	0.6270	0.9209
0.0216	14.0	1680	0.7329	0.5571	0.6724	0.6094	0.9153
0.0216	15.0	1800	0.7276	0.6809	0.5517	0.6095	0.9201
0.0216	16.0	1920	0.7243	0.6017	0.6121	0.6068	0.9209
0.0164	17.0	2040	0.6963	0.592	0.6379	0.6141	0.9217
0.0164	18.0	2160	0.7418	0.6071	0.5862	0.5965	0.9209
0.0164	19.0	2280	0.8015	0.6667	0.5690	0.6140	0.9241
0.0164	20.0	2400	0.7075	0.5168	0.6638	0.5811	0.9136
0.0098	21.0	2520	0.7847	0.6262	0.5776	0.6009	0.9201
0.0098	22.0	2640	0.7588	0.5812	0.5862	0.5837	0.9177
0.0098	23.0	2760	0.7439	0.5530	0.6293	0.5887	0.9153
0.0098	24.0	2880	0.7619	0.5932	0.6034	0.5983	0.9169
0.0067	25.0	3000	0.7605	0.5948	0.5948	0.5948	0.9201
0.0067	26.0	3120	0.7635	0.5847	0.5948	0.5897	0.9185
0.0067	27.0	3240	0.7732	0.6106	0.5948	0.6026	0.9193
0.0067	28.0	3360	0.7727	0.5897	0.5948	0.5923	0.9185
0.0067	29.0	3480	0.7748	0.6	0.5948	0.5974	0.9201
0.006	30.0	3600	0.7759	0.5932	0.6034	0.5983	0.9193
0.006	31.0	3720	0.7792	0.5932	0.6034	0.5983	0.9193
0.006	32.0	3840	0.7795	0.5932	0.6034	0.5983	0.9193

Framework versions

Transformers 4.24.0
Pytorch 2.0.0
Datasets 2.9.0
Tokenizers 0.11.0