ner_conll2003 – AI Model by ramybaly | AlphaNeural AI

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ramybaly/
ner_conll2003

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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ner_conll2003

This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.1495
Precision: 0.8985
Recall: 0.9130
F1: 0.9057
Accuracy: 0.9773

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.423	1.0	877	0.0656	0.9158	0.9268	0.9213	0.9818
0.0575	2.0	1754	0.0574	0.9285	0.9445	0.9364	0.9847
0.0295	3.0	2631	0.0631	0.9414	0.9456	0.9435	0.9859
0.0155	4.0	3508	0.0680	0.9395	0.9467	0.9431	0.9860
0.0097	5.0	4385	0.0694	0.9385	0.9513	0.9449	0.9863
0.0059	6.0	5262	0.0743	0.9363	0.9471	0.9416	0.9860
0.0041	7.0	6139	0.0803	0.9371	0.9518	0.9444	0.9862

Framework versions

Transformers 4.9.1
Pytorch 1.9.0+cu102
Datasets 1.11.0
Tokenizers 0.10.2