my_awesome_ner-token_classification_v1.0.7-5

lilyyellow/my_awesome_ner-token_classification_v1.0.7-5

my_awesome_ner-token_classification_v1.0.7-5 is a token classification model from lilyyellow. Use it when you need labels on individual words, such as names. It is set up for transformers.

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

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At a glance

Task: Token Classification
Library: transformers
Model type: electra
Access: Public
Created: Jun 23, 2024
Updated: Jun 24, 2024
SHA: 25916df9

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Model card

From the Hugging Face model README

my_awesome_ner-token_classification_v1.0.7-5

This model is a fine-tuned version of lilyyellow/my_awesome_ner-token_classification_v1.0.7-5 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6412
Age: {'precision': 0.8231292517006803, 'recall': 0.9166666666666666, 'f1': 0.8673835125448027, 'number': 132}
Datetime: {'precision': 0.7318548387096774, 'recall': 0.7378048780487805, 'f1': 0.7348178137651821, 'number': 984}
Disease: {'precision': 0.677536231884058, 'recall': 0.6607773851590106, 'f1': 0.669051878354204, 'number': 283}
Event: {'precision': 0.30604982206405695, 'recall': 0.32575757575757575, 'f1': 0.3155963302752293, 'number': 264}
Gender: {'precision': 0.7419354838709677, 'recall': 0.8070175438596491, 'f1': 0.7731092436974789, 'number': 114}
Law: {'precision': 0.5209003215434084, 'recall': 0.6403162055335968, 'f1': 0.5744680851063829, 'number': 253}
Location: {'precision': 0.7192796610169492, 'recall': 0.7424822307271733, 'f1': 0.7306967984934087, 'number': 1829}
Organization: {'precision': 0.656473649967469, 'recall': 0.7176386913229018, 'f1': 0.6856948691811077, 'number': 1406}
Person: {'precision': 0.7022955523672884, 'recall': 0.7333333333333333, 'f1': 0.717478930010993, 'number': 1335}
Phone: {'precision': 0.8837209302325582, 'recall': 0.9743589743589743, 'f1': 0.9268292682926831, 'number': 78}
Product: {'precision': 0.4470046082949309, 'recall': 0.37890625, 'f1': 0.4101479915433404, 'number': 256}
Quantity: {'precision': 0.5621890547263682, 'recall': 0.6231617647058824, 'f1': 0.5911072362685265, 'number': 544}
Role: {'precision': 0.47593582887700536, 'recall': 0.5144508670520231, 'f1': 0.49444444444444446, 'number': 519}
Transportation: {'precision': 0.5028571428571429, 'recall': 0.6376811594202898, 'f1': 0.5623003194888179, 'number': 138}
Overall Precision: 0.6503
Overall Recall: 0.6868
Overall F1: 0.6680
Overall Accuracy: 0.8884

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: 5e-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: cosine
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Age	Datetime	Disease	Event	Gender	Law	Location	Organization	Person	Phone	Product	Quantity	Role	Transportation	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.132	1.9991	2313	0.4438	{'precision': 0.8482758620689655, 'recall': 0.9318181818181818, 'f1': 0.8880866425992779, 'number': 132}	{'precision': 0.7001897533206831, 'recall': 0.75, 'f1': 0.7242394504416094, 'number': 984}	{'precision': 0.7104247104247104, 'recall': 0.6501766784452296, 'f1': 0.6789667896678967, 'number': 283}	{'precision': 0.30029154518950435, 'recall': 0.39015151515151514, 'f1': 0.3393739703459638, 'number': 264}	{'precision': 0.7647058823529411, 'recall': 0.7982456140350878, 'f1': 0.7811158798283262, 'number': 114}	{'precision': 0.5693430656934306, 'recall': 0.616600790513834, 'f1': 0.5920303605313093, 'number': 253}	{'precision': 0.7090248962655602, 'recall': 0.7474029524330235, 'f1': 0.7277082778812883, 'number': 1829}	{'precision': 0.6387607119314437, 'recall': 0.6891891891891891, 'f1': 0.6630174478275744, 'number': 1406}	{'precision': 0.6537414965986394, 'recall': 0.7198501872659177, 'f1': 0.685204991087344, 'number': 1335}	{'precision': 0.7888888888888889, 'recall': 0.9102564102564102, 'f1': 0.8452380952380951, 'number': 78}	{'precision': 0.37606837606837606, 'recall': 0.34375, 'f1': 0.35918367346938773, 'number': 256}	{'precision': 0.6036217303822937, 'recall': 0.5514705882352942, 'f1': 0.5763688760806917, 'number': 544}	{'precision': 0.4448462929475588, 'recall': 0.47398843930635837, 'f1': 0.458955223880597, 'number': 519}	{'precision': 0.4489795918367347, 'recall': 0.6376811594202898, 'f1': 0.5269461077844311, 'number': 138}	0.6320	0.6742	0.6524	0.8866
0.1236	3.9983	4626	0.4916	{'precision': 0.8299319727891157, 'recall': 0.9242424242424242, 'f1': 0.8745519713261649, 'number': 132}	{'precision': 0.6889952153110048, 'recall': 0.7317073170731707, 'f1': 0.7097092163627403, 'number': 984}	{'precision': 0.6178343949044586, 'recall': 0.6855123674911661, 'f1': 0.6499162479061976, 'number': 283}	{'precision': 0.26216216216216215, 'recall': 0.36742424242424243, 'f1': 0.305993690851735, 'number': 264}	{'precision': 0.7711864406779662, 'recall': 0.7982456140350878, 'f1': 0.7844827586206897, 'number': 114}	{'precision': 0.5325077399380805, 'recall': 0.6798418972332015, 'f1': 0.5972222222222223, 'number': 253}	{'precision': 0.6995329527763363, 'recall': 0.7370147621651175, 'f1': 0.7177848775292864, 'number': 1829}	{'precision': 0.6458598726114649, 'recall': 0.7211948790896159, 'f1': 0.6814516129032258, 'number': 1406}	{'precision': 0.64526588845655, 'recall': 0.7453183520599251, 'f1': 0.691692735488356, 'number': 1335}	{'precision': 0.9156626506024096, 'recall': 0.9743589743589743, 'f1': 0.9440993788819876, 'number': 78}	{'precision': 0.3524904214559387, 'recall': 0.359375, 'f1': 0.3558994197292069, 'number': 256}	{'precision': 0.5358851674641149, 'recall': 0.6176470588235294, 'f1': 0.5738684884713919, 'number': 544}	{'precision': 0.4106060606060606, 'recall': 0.5221579961464354, 'f1': 0.45971162001696353, 'number': 519}	{'precision': 0.5416666666666666, 'recall': 0.6594202898550725, 'f1': 0.5947712418300652, 'number': 138}	0.6138	0.6907	0.6500	0.8800
0.0909	5.9974	6939	0.5451	{'precision': 0.8413793103448276, 'recall': 0.9242424242424242, 'f1': 0.8808664259927798, 'number': 132}	{'precision': 0.7414684591520165, 'recall': 0.7286585365853658, 'f1': 0.735007688364941, 'number': 984}	{'precision': 0.7054263565891473, 'recall': 0.6431095406360424, 'f1': 0.6728280961182995, 'number': 283}	{'precision': 0.33613445378151263, 'recall': 0.30303030303030304, 'f1': 0.3187250996015936, 'number': 264}	{'precision': 0.7627118644067796, 'recall': 0.7894736842105263, 'f1': 0.7758620689655172, 'number': 114}	{'precision': 0.5496688741721855, 'recall': 0.6561264822134387, 'f1': 0.5981981981981982, 'number': 253}	{'precision': 0.7087024491922876, 'recall': 0.7435757244395844, 'f1': 0.7257203842049093, 'number': 1829}	{'precision': 0.6441326530612245, 'recall': 0.7183499288762447, 'f1': 0.6792199058507061, 'number': 1406}	{'precision': 0.6782246879334258, 'recall': 0.7325842696629213, 'f1': 0.7043572200216061, 'number': 1335}	{'precision': 0.8941176470588236, 'recall': 0.9743589743589743, 'f1': 0.9325153374233129, 'number': 78}	{'precision': 0.43564356435643564, 'recall': 0.34375, 'f1': 0.38427947598253276, 'number': 256}	{'precision': 0.5513866231647635, 'recall': 0.6213235294117647, 'f1': 0.5842696629213483, 'number': 544}	{'precision': 0.4785046728971963, 'recall': 0.4932562620423892, 'f1': 0.4857685009487666, 'number': 519}	{'precision': 0.50920245398773, 'recall': 0.6014492753623188, 'f1': 0.5514950166112956, 'number': 138}	0.6483	0.6817	0.6646	0.8882
0.0531	7.9965	9252	0.6110	{'precision': 0.8356164383561644, 'recall': 0.9242424242424242, 'f1': 0.8776978417266188, 'number': 132}	{'precision': 0.7186274509803922, 'recall': 0.7449186991869918, 'f1': 0.7315369261477046, 'number': 984}	{'precision': 0.6541095890410958, 'recall': 0.6749116607773852, 'f1': 0.6643478260869565, 'number': 283}	{'precision': 0.30662020905923343, 'recall': 0.3333333333333333, 'f1': 0.3194192377495463, 'number': 264}	{'precision': 0.71875, 'recall': 0.8070175438596491, 'f1': 0.7603305785123967, 'number': 114}	{'precision': 0.5838926174496645, 'recall': 0.6877470355731226, 'f1': 0.6315789473684211, 'number': 253}	{'precision': 0.7138348237769595, 'recall': 0.7419354838709677, 'f1': 0.7276139410187666, 'number': 1829}	{'precision': 0.6483375959079284, 'recall': 0.7211948790896159, 'f1': 0.6828282828282827, 'number': 1406}	{'precision': 0.6911250873515025, 'recall': 0.7408239700374532, 'f1': 0.7151120751988431, 'number': 1335}	{'precision': 0.8837209302325582, 'recall': 0.9743589743589743, 'f1': 0.9268292682926831, 'number': 78}	{'precision': 0.45045045045045046, 'recall': 0.390625, 'f1': 0.4184100418410041, 'number': 256}	{'precision': 0.5617792421746294, 'recall': 0.6268382352941176, 'f1': 0.5925282363162467, 'number': 544}	{'precision': 0.4652777777777778, 'recall': 0.5163776493256262, 'f1': 0.48949771689497723, 'number': 519}	{'precision': 0.49444444444444446, 'recall': 0.644927536231884, 'f1': 0.559748427672956, 'number': 138}	0.6448	0.6926	0.6678	0.8877
0.0441	9.9957	11565	0.6412	{'precision': 0.8231292517006803, 'recall': 0.9166666666666666, 'f1': 0.8673835125448027, 'number': 132}	{'precision': 0.7318548387096774, 'recall': 0.7378048780487805, 'f1': 0.7348178137651821, 'number': 984}	{'precision': 0.677536231884058, 'recall': 0.6607773851590106, 'f1': 0.669051878354204, 'number': 283}	{'precision': 0.30604982206405695, 'recall': 0.32575757575757575, 'f1': 0.3155963302752293, 'number': 264}	{'precision': 0.7419354838709677, 'recall': 0.8070175438596491, 'f1': 0.7731092436974789, 'number': 114}	{'precision': 0.5209003215434084, 'recall': 0.6403162055335968, 'f1': 0.5744680851063829, 'number': 253}	{'precision': 0.7192796610169492, 'recall': 0.7424822307271733, 'f1': 0.7306967984934087, 'number': 1829}	{'precision': 0.656473649967469, 'recall': 0.7176386913229018, 'f1': 0.6856948691811077, 'number': 1406}	{'precision': 0.7022955523672884, 'recall': 0.7333333333333333, 'f1': 0.717478930010993, 'number': 1335}	{'precision': 0.8837209302325582, 'recall': 0.9743589743589743, 'f1': 0.9268292682926831, 'number': 78}	{'precision': 0.4470046082949309, 'recall': 0.37890625, 'f1': 0.4101479915433404, 'number': 256}	{'precision': 0.5621890547263682, 'recall': 0.6231617647058824, 'f1': 0.5911072362685265, 'number': 544}	{'precision': 0.47593582887700536, 'recall': 0.5144508670520231, 'f1': 0.49444444444444446, 'number': 519}	{'precision': 0.5028571428571429, 'recall': 0.6376811594202898, 'f1': 0.5623003194888179, 'number': 138}	0.6503	0.6868	0.6680	0.8884

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1

Task: Token Classification
Library: transformers
Type: electra
Created: Jun 23, 2024
Updated: Jun 24, 2024