kt_punc – AI Model by kktoto | AlphaNeural AI

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kktoto/
kt_punc

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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kt_punc

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

Loss: 0.1703
Precision: 0.7079
Recall: 0.7314
F1: 0.7194
Accuracy: 0.9573

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1661	1.0	600	0.1351	0.6566	0.6833	0.6697	0.9498
0.1246	2.0	1200	0.1330	0.6854	0.6665	0.6758	0.9521
0.1121	3.0	1800	0.1303	0.6885	0.6994	0.6939	0.9537
0.1008	4.0	2400	0.1359	0.6836	0.7248	0.7036	0.9543
0.0809	5.0	3000	0.1404	0.7035	0.7082	0.7059	0.9559
0.0696	6.0	3600	0.1449	0.6986	0.7224	0.7103	0.9560
0.0628	7.0	4200	0.1563	0.7063	0.7214	0.7138	0.9567
0.0561	8.0	4800	0.1618	0.7024	0.7333	0.7175	0.9568
0.0525	9.0	5400	0.1669	0.7083	0.7335	0.7207	0.9574
0.0453	10.0	6000	0.1703	0.7079	0.7314	0.7194	0.9573

Framework versions

Transformers 4.19.1
Pytorch 1.11.0+cu113
Datasets 2.2.1
Tokenizers 0.12.1