metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-base-multilingual-cased-finetuned-pos
    results: []

bert-base-multilingual-cased-finetuned-pos

This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1736
Precision: 0.9499
Recall: 0.9504
F1: 0.9501
Accuracy: 0.9551

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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7663	0.27	200	0.2047	0.9318	0.9312	0.9315	0.9388
0.5539	0.53	400	0.1815	0.9381	0.9404	0.9392	0.9460
0.5222	0.8	600	0.1787	0.9400	0.9424	0.9412	0.9468
0.5084	1.07	800	0.1591	0.9470	0.9463	0.9467	0.9519
0.4703	1.33	1000	0.1622	0.9456	0.9458	0.9457	0.9510
0.5005	1.6	1200	0.1666	0.9470	0.9464	0.9467	0.9519
0.4677	1.87	1400	0.1583	0.9483	0.9483	0.9483	0.9532
0.4704	2.13	1600	0.1635	0.9472	0.9475	0.9473	0.9528
0.4639	2.4	1800	0.1569	0.9475	0.9488	0.9482	0.9536
0.4627	2.67	2000	0.1605	0.9474	0.9478	0.9476	0.9527
0.4608	2.93	2200	0.1535	0.9485	0.9495	0.9490	0.9538
0.4306	3.2	2400	0.1646	0.9489	0.9487	0.9488	0.9536
0.4583	3.47	2600	0.1642	0.9488	0.9495	0.9491	0.9539
0.453	3.73	2800	0.1646	0.9498	0.9505	0.9501	0.9554
0.4347	4.0	3000	0.1629	0.9494	0.9504	0.9499	0.9552
0.4425	4.27	3200	0.1738	0.9495	0.9502	0.9498	0.9550
0.4335	4.53	3400	0.1733	0.9499	0.9506	0.9503	0.9550
0.4306	4.8	3600	0.1736	0.9499	0.9504	0.9501	0.9551

Framework versions

Transformers 4.21.0
Pytorch 1.12.0+cu102
Datasets 2.4.0
Tokenizers 0.12.1