bert-cased-keyword-discriminator – AI Model by yanekyuk | AlphaNeural AI

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yanekyuk/
bert-cased-keyword-discriminator

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-keyword-discriminator

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

Loss: 0.1310
Precision: 0.8522
Recall: 0.8868
Accuracy: 0.9732
F1: 0.8692
Ent/precision: 0.8874
Ent/accuracy: 0.9246
Ent/f1: 0.9056
Con/precision: 0.8011
Con/accuracy: 0.8320
Con/f1: 0.8163

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: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	Accuracy	F1	Ent/precision	Ent/accuracy	Ent/f1	Con/precision	Con/accuracy	Con/f1
0.1744	1.0	1875	0.1261	0.7176	0.7710	0.9494	0.7433	0.7586	0.8503	0.8018	0.6514	0.6561	0.6537
0.1261	2.0	3750	0.1041	0.7742	0.8057	0.9600	0.7896	0.8083	0.8816	0.8433	0.7185	0.6957	0.7070
0.0878	3.0	5625	0.0979	0.8176	0.8140	0.9655	0.8158	0.8518	0.8789	0.8651	0.7634	0.7199	0.7410
0.0625	4.0	7500	0.0976	0.8228	0.8643	0.9696	0.8430	0.8515	0.9182	0.8836	0.7784	0.7862	0.7823
0.0456	5.0	9375	0.1047	0.8304	0.8758	0.9704	0.8525	0.8758	0.9189	0.8968	0.7655	0.8133	0.7887
0.0342	6.0	11250	0.1207	0.8363	0.8887	0.9719	0.8617	0.8719	0.9274	0.8988	0.7846	0.8327	0.8080
0.0256	7.0	13125	0.1241	0.848	0.8892	0.9731	0.8681	0.8791	0.9299	0.9038	0.8019	0.8302	0.8158
0.0205	8.0	15000	0.1310	0.8522	0.8868	0.9732	0.8692	0.8874	0.9246	0.9056	0.8011	0.8320	0.8163

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1