Ner_Productname

sianbrumm/Ner_Productname

Ner_Productname is a token classification model from sianbrumm. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: May 7, 2024
Updated: Jun 25, 2024
SHA: 059d67a7

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Base models

dbmdz/bert-base-german-uncased

Model card

From the Hugging Face model README

first_ner_model

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

Loss: 0.6659
Label Metrics: {'Alcohol_content': {'precision': 0.5172413793103449, 'recall': 0.7894736842105263, 'f1': 0.625, 'number': 19}, 'Brand': {'precision': 0.8753623188405797, 'recall': 0.9082706766917293, 'f1': 0.8915129151291513, 'number': 665}, 'Packaging.Quantity': {'precision': 0.6891891891891891, 'recall': 0.796875, 'f1': 0.7391304347826088, 'number': 64}, 'Packaging.Type': {'precision': 0.6, 'recall': 0.3, 'f1': 0.4, 'number': 20}, 'Packaging.Volume': {'precision': 0.978494623655914, 'recall': 0.7054263565891473, 'f1': 0.8198198198198199, 'number': 129}, 'Packaging.Weight': {'precision': 0.8891402714932126, 'recall': 0.9974619289340102, 'f1': 0.9401913875598087, 'number': 394}, 'Producttype': {'precision': 0.4715984147952444, 'recall': 0.5344311377245509, 'f1': 0.5010526315789474, 'number': 668}, '_': {'precision': 0.367983367983368, 'recall': 0.42857142857142855, 'f1': 0.3959731543624161, 'number': 413}, 'overall_precision': 0.657608695652174, 'overall_recall': 0.7141652613827993, 'overall_f1': 0.6847210994341149, 'overall_accuracy': 0.7419354838709677}
Overall Metrics: {'overall_precision': 0.657608695652174, 'overall_recall': 0.7141652613827993, 'overall_f1': 0.6847210994341149, 'overall_accuracy': 0.7419354838709677}

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Label Metrics	Overall Metrics
No log	1.0	175	0.6684	{'Alcohol_content': {'precision': 0.46153846153846156, 'recall': 0.3157894736842105, 'f1': 0.37499999999999994, 'number': 19}, 'Brand': {'precision': 0.8885672937771346, 'recall': 0.9233082706766917, 'f1': 0.9056047197640117, 'number': 665}, 'Packaging.Quantity': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1': 0.7384615384615384, 'number': 64}, 'Packaging.Type': {'precision': 0.6666666666666666, 'recall': 0.1, 'f1': 0.1739130434782609, 'number': 20}, 'Packaging.Volume': {'precision': 0.9782608695652174, 'recall': 0.6976744186046512, 'f1': 0.8144796380090498, 'number': 129}, 'Packaging.Weight': {'precision': 0.8828828828828829, 'recall': 0.9949238578680203, 'f1': 0.9355608591885441, 'number': 394}, 'Producttype': {'precision': 0.43159203980099503, 'recall': 0.5194610778443114, 'f1': 0.4714673913043478, 'number': 668}, '_': {'precision': 0.3300970873786408, 'recall': 0.4116222760290557, 'f1': 0.3663793103448276, 'number': 413}, 'overall_precision': 0.6350837138508372, 'overall_recall': 0.7036256323777403, 'overall_f1': 0.6676, 'overall_accuracy': 0.7299687825182102}	{'overall_precision': 0.6350837138508372, 'overall_recall': 0.7036256323777403, 'overall_f1': 0.6676, 'overall_accuracy': 0.7299687825182102}
No log	2.0	350	0.6659	{'Alcohol_content': {'precision': 0.5172413793103449, 'recall': 0.7894736842105263, 'f1': 0.625, 'number': 19}, 'Brand': {'precision': 0.8753623188405797, 'recall': 0.9082706766917293, 'f1': 0.8915129151291513, 'number': 665}, 'Packaging.Quantity': {'precision': 0.6891891891891891, 'recall': 0.796875, 'f1': 0.7391304347826088, 'number': 64}, 'Packaging.Type': {'precision': 0.6, 'recall': 0.3, 'f1': 0.4, 'number': 20}, 'Packaging.Volume': {'precision': 0.978494623655914, 'recall': 0.7054263565891473, 'f1': 0.8198198198198199, 'number': 129}, 'Packaging.Weight': {'precision': 0.8891402714932126, 'recall': 0.9974619289340102, 'f1': 0.9401913875598087, 'number': 394}, 'Producttype': {'precision': 0.4715984147952444, 'recall': 0.5344311377245509, 'f1': 0.5010526315789474, 'number': 668}, '_': {'precision': 0.367983367983368, 'recall': 0.42857142857142855, 'f1': 0.3959731543624161, 'number': 413}, 'overall_precision': 0.657608695652174, 'overall_recall': 0.7141652613827993, 'overall_f1': 0.6847210994341149, 'overall_accuracy': 0.7419354838709677}	{'overall_precision': 0.657608695652174, 'overall_recall': 0.7141652613827993, 'overall_f1': 0.6847210994341149, 'overall_accuracy': 0.7419354838709677}
0.75	3.0	525	0.6749	{'Alcohol_content': {'precision': 0.6363636363636364, 'recall': 0.7368421052631579, 'f1': 0.6829268292682926, 'number': 19}, 'Brand': {'precision': 0.9033674963396779, 'recall': 0.9278195488721804, 'f1': 0.9154302670623146, 'number': 665}, 'Packaging.Quantity': {'precision': 0.6619718309859155, 'recall': 0.734375, 'f1': 0.6962962962962963, 'number': 64}, 'Packaging.Type': {'precision': 0.5384615384615384, 'recall': 0.35, 'f1': 0.4242424242424242, 'number': 20}, 'Packaging.Volume': {'precision': 0.9787234042553191, 'recall': 0.7131782945736435, 'f1': 0.8251121076233184, 'number': 129}, 'Packaging.Weight': {'precision': 0.8888888888888888, 'recall': 0.9949238578680203, 'f1': 0.9389221556886227, 'number': 394}, 'Producttype': {'precision': 0.5122615803814714, 'recall': 0.562874251497006, 'f1': 0.536376604850214, 'number': 668}, '_': {'precision': 0.4088235294117647, 'recall': 0.3365617433414044, 'f1': 0.36918990703851257, 'number': 413}, 'overall_precision': 0.7022518765638032, 'overall_recall': 0.7099494097807757, 'overall_f1': 0.7060796645702306, 'overall_accuracy': 0.7419354838709677}	{'overall_precision': 0.7022518765638032, 'overall_recall': 0.7099494097807757, 'overall_f1': 0.7060796645702306, 'overall_accuracy': 0.7419354838709677}
0.75	4.0	700	0.6891	{'Alcohol_content': {'precision': 0.7142857142857143, 'recall': 0.7894736842105263, 'f1': 0.7500000000000001, 'number': 19}, 'Brand': {'precision': 0.9104258443465492, 'recall': 0.9323308270676691, 'f1': 0.9212481426448736, 'number': 665}, 'Packaging.Quantity': {'precision': 0.6712328767123288, 'recall': 0.765625, 'f1': 0.7153284671532847, 'number': 64}, 'Packaging.Type': {'precision': 0.42105263157894735, 'recall': 0.4, 'f1': 0.41025641025641024, 'number': 20}, 'Packaging.Volume': {'precision': 0.9787234042553191, 'recall': 0.7131782945736435, 'f1': 0.8251121076233184, 'number': 129}, 'Packaging.Weight': {'precision': 0.8888888888888888, 'recall': 0.9949238578680203, 'f1': 0.9389221556886227, 'number': 394}, 'Producttype': {'precision': 0.505464480874317, 'recall': 0.5538922155688623, 'f1': 0.5285714285714285, 'number': 668}, '_': {'precision': 0.4217506631299735, 'recall': 0.38498789346246975, 'f1': 0.40253164556962034, 'number': 413}, 'overall_precision': 0.699343724364233, 'overall_recall': 0.7188026981450253, 'overall_f1': 0.7089397089397088, 'overall_accuracy': 0.7447970863683663}	{'overall_precision': 0.699343724364233, 'overall_recall': 0.7188026981450253, 'overall_f1': 0.7089397089397088, 'overall_accuracy': 0.7447970863683663}
0.75	5.0	875	0.7116	{'Alcohol_content': {'precision': 0.7142857142857143, 'recall': 0.7894736842105263, 'f1': 0.7500000000000001, 'number': 19}, 'Brand': {'precision': 0.9092240117130308, 'recall': 0.9338345864661655, 'f1': 0.9213649851632048, 'number': 665}, 'Packaging.Quantity': {'precision': 0.7, 'recall': 0.765625, 'f1': 0.7313432835820896, 'number': 64}, 'Packaging.Type': {'precision': 0.5, 'recall': 0.55, 'f1': 0.5238095238095238, 'number': 20}, 'Packaging.Volume': {'precision': 0.968421052631579, 'recall': 0.7131782945736435, 'f1': 0.8214285714285715, 'number': 129}, 'Packaging.Weight': {'precision': 0.8886363636363637, 'recall': 0.9923857868020305, 'f1': 0.9376498800959233, 'number': 394}, 'Producttype': {'precision': 0.5089655172413793, 'recall': 0.5523952095808383, 'f1': 0.529791816223977, 'number': 668}, '_': {'precision': 0.4172661870503597, 'recall': 0.4213075060532688, 'f1': 0.419277108433735, 'number': 413}, 'overall_precision': 0.6963202587949858, 'overall_recall': 0.7259696458684655, 'overall_f1': 0.7108359133126935, 'overall_accuracy': 0.7484391259105099}	{'overall_precision': 0.6963202587949858, 'overall_recall': 0.7259696458684655, 'overall_f1': 0.7108359133126935, 'overall_accuracy': 0.7484391259105099}

Framework versions

Transformers 4.41.2
Pytorch 2.0.1+cu117
Datasets 2.20.0
Tokenizers 0.19.1

Task: Token Classification
Library: transformers
Type: bert
License: mit
Created: May 7, 2024
Updated: Jun 25, 2024