mn-twhin-bert-named-entity – AI Model by 2rtl3 | AlphaNeural AI

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2rtl3/
mn-twhin-bert-named-entity

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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mn-twhin-bert-named-entity

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

Loss: 0.1591
Precision: 0.9068
Recall: 0.9199
F1: 0.9133
Accuracy: 0.9728

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
0.1901	1.0	477	0.1052	0.8528	0.8872	0.8697	0.9666
0.0853	2.0	954	0.1220	0.8731	0.8963	0.8845	0.9666
0.0577	3.0	1431	0.1109	0.8889	0.9082	0.8984	0.9696
0.0396	4.0	1908	0.1172	0.9006	0.9175	0.9090	0.9724
0.0287	5.0	2385	0.1314	0.9002	0.9169	0.9085	0.9720
0.0213	6.0	2862	0.1363	0.9051	0.9181	0.9116	0.9720
0.0158	7.0	3339	0.1437	0.9114	0.9221	0.9167	0.9732
0.011	8.0	3816	0.1517	0.9091	0.9202	0.9146	0.9726
0.0077	9.0	4293	0.1570	0.9070	0.9199	0.9134	0.9728
0.0059	10.0	4770	0.1591	0.9068	0.9199	0.9133	0.9728

Framework versions

Transformers 4.28.0
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3