bert-base-multilingual-uncased-finetuned-ner-harem – AI Model by GuiTap | AlphaNeural AI

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GuiTap/
bert-base-multilingual-uncased-finetuned-ner-harem

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token-classification

generated_from_trainer

google-bert/bert-base-multilingual-uncased

autotrain_compatible

endpoints_compatible

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bert-base-multilingual-uncased-finetuned-ner-harem

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

Loss: 0.1861
Precision: 0.7833
Recall: 0.7589
F1: 0.7709
Accuracy: 0.9634

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: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	282	0.2275	0.5847	0.6014	0.5929	0.9378
0.2687	2.0	564	0.1620	0.7389	0.6754	0.7057	0.9583
0.2687	3.0	846	0.1395	0.7820	0.7446	0.7628	0.9659
0.0845	4.0	1128	0.1694	0.7458	0.7351	0.7404	0.9586
0.0845	5.0	1410	0.1861	0.7833	0.7589	0.7709	0.9634
0.0398	6.0	1692	0.1821	0.7583	0.7637	0.7610	0.9639
0.0398	7.0	1974	0.2303	0.7789	0.7064	0.7409	0.9595
0.0203	8.0	2256	0.1912	0.7350	0.7876	0.7604	0.9629
0.0109	9.0	2538	0.2304	0.7524	0.7613	0.7568	0.9595
0.0109	10.0	2820	0.2457	0.7617	0.7399	0.7506	0.9622

Framework versions

Transformers 4.41.1
Pytorch 2.1.2
Datasets 2.19.1
Tokenizers 0.19.1