bkk-ner-model – AI Model by napatswift | AlphaNeural AI

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napatswift/
bkk-ner-model

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bkk-ner-model

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

Loss: 0.0518
Precision: 0.8850
Recall: 0.9615
F1: 0.9217
Accuracy: 0.9822

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: 2e-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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	8	0.5592	0.3698	0.6827	0.4797	0.7818
No log	2.0	16	0.4491	0.4831	0.8269	0.6099	0.8062
No log	3.0	24	0.3738	0.6226	0.9519	0.7529	0.8399
No log	4.0	32	0.1781	0.6691	0.8942	0.7654	0.9401
No log	5.0	40	0.2201	0.8095	0.9808	0.8870	0.9204
No log	6.0	48	0.0936	0.8130	0.9615	0.8811	0.9710
No log	7.0	56	0.0692	0.8197	0.9615	0.8850	0.9757
No log	8.0	64	0.0712	0.8264	0.9615	0.8889	0.9710
No log	9.0	72	0.0575	0.8621	0.9615	0.9091	0.9803
No log	10.0	80	0.0625	0.8487	0.9712	0.9058	0.9766
No log	11.0	88	0.0580	0.8584	0.9327	0.8940	0.9766
No log	12.0	96	0.0551	0.8684	0.9519	0.9083	0.9813
No log	13.0	104	0.0554	0.8761	0.9519	0.9124	0.9803
No log	14.0	112	0.0535	0.8772	0.9615	0.9174	0.9813
No log	15.0	120	0.0518	0.8850	0.9615	0.9217	0.9822

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2