bert-base-multilingual-cased-ner-demo – AI Model by Tengisbold | AlphaNeural AI

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Tengisbold/
bert-base-multilingual-cased-ner-demo

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-base-multilingual-cased-ner-demo

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: 0.1333
Precision: 0.9160
Recall: 0.9229
F1: 0.9194
Accuracy: 0.9779

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: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1658	1.0	572	0.1113	0.8581	0.8792	0.8685	0.9658
0.0816	2.0	1144	0.0880	0.8950	0.9096	0.9022	0.9737
0.0554	3.0	1716	0.0935	0.8941	0.9096	0.9018	0.9741
0.0394	4.0	2288	0.1069	0.9070	0.9189	0.9129	0.9762
0.0284	5.0	2860	0.1029	0.9007	0.9184	0.9095	0.9752
0.0192	6.0	3432	0.1110	0.9102	0.9214	0.9157	0.9764
0.0139	7.0	4004	0.1156	0.9166	0.9272	0.9218	0.9786
0.0095	8.0	4576	0.1319	0.9091	0.9174	0.9132	0.9761
0.0066	9.0	5148	0.1313	0.9132	0.9226	0.9179	0.9781
0.0053	10.0	5720	0.1333	0.9160	0.9229	0.9194	0.9779

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3