bert-keyword-extractor – AI Model by yanekyuk | AlphaNeural AI

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yanekyuk/
bert-keyword-extractor

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-keyword-extractor

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: 0.1341
Precision: 0.8565
Recall: 0.8874
Accuracy: 0.9738
F1: 0.8717

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
0.1688	1.0	1875	0.1233	0.7194	0.7738	0.9501	0.7456
0.1219	2.0	3750	0.1014	0.7724	0.8166	0.9606	0.7939
0.0834	3.0	5625	0.0977	0.8280	0.8263	0.9672	0.8272
0.0597	4.0	7500	0.0984	0.8304	0.8680	0.9704	0.8488
0.0419	5.0	9375	0.1042	0.8417	0.8687	0.9717	0.8550
0.0315	6.0	11250	0.1161	0.8520	0.8839	0.9729	0.8677
0.0229	7.0	13125	0.1282	0.8469	0.8939	0.9734	0.8698
0.0182	8.0	15000	0.1341	0.8565	0.8874	0.9738	0.8717

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1