ner_cv – AI Model by nhanv | AlphaNeural AI

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nhanv/
ner_cv

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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reco-ner

This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0668
Precision: 0.8125
Recall: 0.8790
F1: 0.8444
Accuracy: 0.9819

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: 5e-05
train_batch_size: 16
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4516	1.0	626	0.4047	0.4332	0.4564	0.4445	0.8980
0.3677	2.0	1252	0.2774	0.4918	0.5731	0.5293	0.9193
0.2892	3.0	1878	0.2133	0.6139	0.6581	0.6353	0.9384
0.2736	4.0	2504	0.1772	0.6248	0.6854	0.6537	0.9488
0.221	5.0	3130	0.1503	0.6295	0.7328	0.6772	0.9560
0.1569	6.0	3756	0.1283	0.6821	0.8108	0.7409	0.9623
0.1534	7.0	4382	0.0995	0.7412	0.8119	0.7749	0.9708
0.089	8.0	5008	0.0846	0.7695	0.8353	0.8010	0.9760
0.0923	9.0	5634	0.0743	0.7881	0.8740	0.8289	0.9789
0.0711	10.0	6260	0.0668	0.8125	0.8790	0.8444	0.9819

Framework versions

Transformers 4.22.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1