metadata

license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
  - generated_from_trainer
metrics:
  - recall
  - accuracy
model-index:
  - name: multibert_dataaugmentation
    results: []

multibert_dataaugmentation

This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7138
Precisions: 0.8609
Recall: 0.8356
F-measure: 0.8464
Accuracy: 0.8989

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: 7.5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 14

Training results

Training Loss	Epoch	Step	Validation Loss	Precisions	Recall	F-measure	Accuracy
0.5775	1.0	285	0.4827	0.7847	0.7040	0.7340	0.8509
0.2623	2.0	570	0.5829	0.8035	0.7359	0.7591	0.8613
0.1503	3.0	855	0.5609	0.7946	0.8083	0.7917	0.8804
0.088	4.0	1140	0.5481	0.8406	0.7997	0.8170	0.8860
0.0592	5.0	1425	0.6359	0.8207	0.8210	0.8120	0.8828
0.0414	6.0	1710	0.6589	0.8313	0.8171	0.8198	0.8843
0.0271	7.0	1995	0.7117	0.8689	0.7882	0.8216	0.8936
0.0179	8.0	2280	0.7138	0.8609	0.8356	0.8464	0.8989
0.0121	9.0	2565	0.7289	0.8456	0.8128	0.8278	0.8946
0.0081	10.0	2850	0.7603	0.8344	0.8223	0.8278	0.8956
0.0058	11.0	3135	0.8126	0.8576	0.8107	0.8322	0.8942
0.0041	12.0	3420	0.8004	0.8582	0.8267	0.8415	0.8955
0.0031	13.0	3705	0.7936	0.8599	0.8275	0.8426	0.8961
0.0028	14.0	3990	0.8076	0.8602	0.8226	0.8401	0.8966

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1