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napsternxg/
nyt_ingredients-tagger-gte-small-L3-ingredient-v2

Name: nyt_ingredients-tagger-gte-small-L3-ingredient-v2
Availability: InStock

transformers

pytorch

bert

generated_from_trainer

napsternxg/nyt_ingredients

napsternxg/gte-small-L3-ingredient-v2

finetune

endpoints_compatible

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nyt_ingredients-tagger-gte-small-L3-ingredient-v2

This model is a fine-tuned version of napsternxg/gte-small-L3-ingredient-v2 on the nyt_ingredients dataset. It achieves the following results on the evaluation set:

Loss: 0.4705
Comment: {'precision': 0.6653720111664037, 'recall': 0.7644679960953842, 'f1': 0.7114860480207656, 'number': 7171}
Name: {'precision': 0.7963734889537307, 'recall': 0.8212788823213326, 'f1': 0.8086344637849849, 'number': 9305}
Qty: {'precision': 0.981755684822845, 'recall': 0.9907938625750501, 'f1': 0.9862540673351484, 'number': 7495}
Range End: {'precision': 0.6240601503759399, 'recall': 0.9222222222222223, 'f1': 0.7443946188340806, 'number': 90}
Unit: {'precision': 0.923100969052829, 'recall': 0.9861412589747871, 'f1': 0.95358036651328, 'number': 5989}
Overall Precision: 0.8312
Overall Recall: 0.8832
Overall F1: 0.8564
Overall Accuracy: 0.8350

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: 32
eval_batch_size: 32
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	Comment	Name	Qty	Range End	Unit	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.6931	0.2	1000	0.6230	{'precision': 0.5236127508854782, 'recall': 0.6426604839878278, 'f1': 0.5770606987183658, 'number': 6901}	{'precision': 0.7773012207192346, 'recall': 0.7980128711753416, 'f1': 0.7875208913649026, 'number': 8857}	{'precision': 0.9824561403508771, 'recall': 0.9737098344693281, 'f1': 0.9780634343998882, 'number': 7189}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 92}	{'precision': 0.9192320423700762, 'recall': 0.9689462665736218, 'f1': 0.9434346865975879, 'number': 5732}	0.7829	0.8362	0.8086	0.7986
0.5826	0.4	2000	0.5531	{'precision': 0.5435365427205968, 'recall': 0.6756991740327489, 'f1': 0.602454780361757, 'number': 6901}	{'precision': 0.7757141305528402, 'recall': 0.8063678446426555, 'f1': 0.7907440212577502, 'number': 8857}	{'precision': 0.9659028914348063, 'recall': 0.9851161496731117, 'f1': 0.9754149163280765, 'number': 7189}	{'precision': 0.6055045871559633, 'recall': 0.717391304347826, 'f1': 0.6567164179104478, 'number': 92}	{'precision': 0.9177049180327869, 'recall': 0.9766224703419399, 'f1': 0.9462474645030426, 'number': 5732}	0.7837	0.8533	0.8170	0.8079
0.5435	0.59	3000	0.5327	{'precision': 0.5801165331391115, 'recall': 0.6925083321257789, 'f1': 0.6313494946826079, 'number': 6901}	{'precision': 0.7839085513299626, 'recall': 0.8052387941740996, 'f1': 0.7944305207463102, 'number': 8857}	{'precision': 0.9771436487048067, 'recall': 0.9812213103352344, 'f1': 0.9791782343142699, 'number': 7189}	{'precision': 0.6302521008403361, 'recall': 0.8152173913043478, 'f1': 0.7109004739336493, 'number': 92}	{'precision': 0.9126637554585153, 'recall': 0.9844731332868109, 'f1': 0.9472093999160722, 'number': 5732}	0.7999	0.8579	0.8279	0.8150
0.5333	0.79	4000	0.5212	{'precision': 0.5875760104924288, 'recall': 0.7140994058832053, 'f1': 0.6446886446886447, 'number': 6901}	{'precision': 0.786660777385159, 'recall': 0.8043355537992548, 'f1': 0.7953999888349244, 'number': 8857}	{'precision': 0.9837716843872412, 'recall': 0.9781610794269022, 'f1': 0.9809583594894328, 'number': 7189}	{'precision': 0.6390977443609023, 'recall': 0.9239130434782609, 'f1': 0.7555555555555556, 'number': 92}	{'precision': 0.9263366992219831, 'recall': 0.9762735519888346, 'f1': 0.9506497918967128, 'number': 5732}	0.8050	0.8608	0.8319	0.8183
0.5199	0.99	5000	0.5121	{'precision': 0.5873457536898137, 'recall': 0.7035212288074192, 'f1': 0.6402057097646205, 'number': 6901}	{'precision': 0.7890984865101996, 'recall': 0.812351812126002, 'f1': 0.8005563282336579, 'number': 8857}	{'precision': 0.978519955654102, 'recall': 0.9821950201697037, 'f1': 0.9803540437348144, 'number': 7189}	{'precision': 0.6615384615384615, 'recall': 0.9347826086956522, 'f1': 0.7747747747747747, 'number': 92}	{'precision': 0.9197843489625878, 'recall': 0.9822051639916259, 'f1': 0.9499704716105627, 'number': 5732}	0.8047	0.8629	0.8328	0.8186
0.4983	1.19	6000	0.5088	{'precision': 0.6041901894367081, 'recall': 0.6978698739313143, 'f1': 0.6476600322754169, 'number': 6901}	{'precision': 0.7801047120418848, 'recall': 0.8074968951112115, 'f1': 0.7935644937586686, 'number': 8857}	{'precision': 0.980998613037448, 'recall': 0.983864237028794, 'f1': 0.9824293353705119, 'number': 7189}	{'precision': 0.6511627906976745, 'recall': 0.9130434782608695, 'f1': 0.7601809954751132, 'number': 92}	{'precision': 0.9193864229765013, 'recall': 0.9829030006978368, 'f1': 0.9500843170320405, 'number': 5732}	0.8090	0.8606	0.8340	0.8192
0.5031	1.39	7000	0.5031	{'precision': 0.6055345911949686, 'recall': 0.6975800608607449, 'f1': 0.6483065113460373, 'number': 6901}	{'precision': 0.7835422343324251, 'recall': 0.8116743818448685, 'f1': 0.797360248447205, 'number': 8857}	{'precision': 0.9752815160670145, 'recall': 0.9878981777715955, 'f1': 0.9815493055075669, 'number': 7189}	{'precision': 0.6666666666666666, 'recall': 0.9130434782608695, 'f1': 0.7706422018348623, 'number': 92}	{'precision': 0.9269467043001827, 'recall': 0.9740055826936497, 'f1': 0.9498936622713738, 'number': 5732}	0.8107	0.8610	0.8351	0.8199
0.492	1.58	8000	0.5041	{'precision': 0.6209482341557814, 'recall': 0.743950152151862, 'f1': 0.6769068494956819, 'number': 6901}	{'precision': 0.7778260869565218, 'recall': 0.8079485152986339, 'f1': 0.7926012072880323, 'number': 8857}	{'precision': 0.983001254005852, 'recall': 0.9813604117401585, 'f1': 0.9821801475706529, 'number': 7189}	{'precision': 0.6423357664233577, 'recall': 0.9565217391304348, 'f1': 0.7685589519650656, 'number': 92}	{'precision': 0.9215493188905302, 'recall': 0.9795882763433357, 'f1': 0.9496828752642706, 'number': 5732}	0.8113	0.8706	0.8399	0.8246
0.4817	1.78	9000	0.4901	{'precision': 0.6196705426356589, 'recall': 0.7413418345167367, 'f1': 0.6750676255195619, 'number': 6901}	{'precision': 0.7891447368421053, 'recall': 0.8125776222197132, 'f1': 0.8006897702620014, 'number': 8857}	{'precision': 0.9798370390829996, 'recall': 0.9869244679371262, 'f1': 0.9833679833679833, 'number': 7189}	{'precision': 0.6474820143884892, 'recall': 0.9782608695652174, 'f1': 0.7792207792207793, 'number': 92}	{'precision': 0.9168422758956071, 'recall': 0.9867411025819958, 'f1': 0.9505083606419629, 'number': 5732}	0.8134	0.8743	0.8427	0.8293
0.4905	1.98	10000	0.4892	{'precision': 0.6241463414634146, 'recall': 0.7416316475873062, 'f1': 0.6778359049069598, 'number': 6901}	{'precision': 0.7911287653095002, 'recall': 0.8095291859546122, 'f1': 0.8002232142857143, 'number': 8857}	{'precision': 0.9846860643185299, 'recall': 0.983864237028794, 'f1': 0.9842749791260784, 'number': 7189}	{'precision': 0.6717557251908397, 'recall': 0.9565217391304348, 'f1': 0.789237668161435, 'number': 92}	{'precision': 0.9170723790976956, 'recall': 0.9858688066992324, 'f1': 0.9502270052127123, 'number': 5732}	0.8166	0.8724	0.8436	0.8288
0.4757	2.18	11000	0.4954	{'precision': 0.6271186440677966, 'recall': 0.7398927691638892, 'f1': 0.6788539520042544, 'number': 6901}	{'precision': 0.7921020789792103, 'recall': 0.8130292424071356, 'f1': 0.802429240026744, 'number': 8857}	{'precision': 0.9801077496891836, 'recall': 0.9869244679371262, 'f1': 0.9835042971998891, 'number': 7189}	{'precision': 0.6428571428571429, 'recall': 0.9782608695652174, 'f1': 0.7758620689655172, 'number': 92}	{'precision': 0.9226609864001311, 'recall': 0.9823796231681786, 'f1': 0.9515842839036756, 'number': 5732}	0.8179	0.8732	0.8447	0.8274
0.4691	2.38	12000	0.4903	{'precision': 0.6292872780345414, 'recall': 0.7497464135632517, 'f1': 0.6842557693579316, 'number': 6901}	{'precision': 0.7935547734271887, 'recall': 0.8146099130631139, 'f1': 0.803944509443423, 'number': 8857}	{'precision': 0.9806816613771215, 'recall': 0.9885936847962165, 'f1': 0.984621778886118, 'number': 7189}	{'precision': 0.6544117647058824, 'recall': 0.967391304347826, 'f1': 0.780701754385965, 'number': 92}	{'precision': 0.9238204456094364, 'recall': 0.9837752965806001, 'f1': 0.9528556944913822, 'number': 5732}	0.8189	0.8767	0.8468	0.8301
0.4765	2.57	13000	0.4846	{'precision': 0.6338439095550693, 'recall': 0.7555426749746413, 'f1': 0.6893633899649634, 'number': 6901}	{'precision': 0.7977640026566305, 'recall': 0.8137066726882691, 'f1': 0.805656475322788, 'number': 8857}	{'precision': 0.9850891861761427, 'recall': 0.9833078314090973, 'f1': 0.9841977027497391, 'number': 7189}	{'precision': 0.6774193548387096, 'recall': 0.9130434782608695, 'f1': 0.7777777777777777, 'number': 92}	{'precision': 0.9219950940310712, 'recall': 0.9836008374040475, 'f1': 0.9518021440027011, 'number': 5732}	0.8219	0.8763	0.8482	0.8307
0.4747	2.77	14000	0.4844	{'precision': 0.6380844929171368, 'recall': 0.7375742645993334, 'f1': 0.6842317515795134, 'number': 6901}	{'precision': 0.791246160596753, 'recall': 0.8143841029694028, 'f1': 0.8026484170700496, 'number': 8857}	{'precision': 0.9796255506607929, 'recall': 0.9898455974405341, 'f1': 0.9847090569431951, 'number': 7189}	{'precision': 0.6546762589928058, 'recall': 0.9891304347826086, 'f1': 0.7878787878787878, 'number': 92}	{'precision': 0.9195252804422045, 'recall': 0.9867411025819958, 'f1': 0.9519481612387444, 'number': 5732}	0.8212	0.8747	0.8471	0.8314
0.4654	2.97	15000	0.4808	{'precision': 0.6453574975173784, 'recall': 0.7533690769453703, 'f1': 0.6951928862739855, 'number': 6901}	{'precision': 0.7925608011444921, 'recall': 0.8131421474539912, 'f1': 0.8027195720017833, 'number': 8857}	{'precision': 0.9827013562136728, 'recall': 0.9877590763666713, 'f1': 0.9852237252861602, 'number': 7189}	{'precision': 0.6641221374045801, 'recall': 0.9456521739130435, 'f1': 0.7802690582959643, 'number': 92}	{'precision': 0.9255791030064071, 'recall': 0.9829030006978368, 'f1': 0.9533801506049582, 'number': 5732}	0.8246	0.8767	0.8499	0.8332
0.4586	3.17	16000	0.4827	{'precision': 0.6517362858580775, 'recall': 0.7506158527749601, 'f1': 0.6976900801400768, 'number': 6901}	{'precision': 0.7901383703052932, 'recall': 0.812351812126002, 'f1': 0.8010911317708622, 'number': 8857}	{'precision': 0.979906413432425, 'recall': 0.990402003060231, 'f1': 0.9851262538913871, 'number': 7189}	{'precision': 0.6641221374045801, 'recall': 0.9456521739130435, 'f1': 0.7802690582959643, 'number': 92}	{'precision': 0.9193364774760123, 'recall': 0.9862177250523377, 'f1': 0.9516034003871727, 'number': 5732}	0.8247	0.8771	0.8501	0.8317
0.4612	3.37	17000	0.4869	{'precision': 0.6441053408811224, 'recall': 0.7584408056803362, 'f1': 0.6966127636920211, 'number': 6901}	{'precision': 0.7845718637797846, 'recall': 0.8141582928756915, 'f1': 0.7990913120567377, 'number': 8857}	{'precision': 0.9844271412680756, 'recall': 0.9848379468632633, 'f1': 0.9846325012168833, 'number': 7189}	{'precision': 0.6641221374045801, 'recall': 0.9456521739130435, 'f1': 0.7802690582959643, 'number': 92}	{'precision': 0.9158439877002751, 'recall': 0.9872644801116539, 'f1': 0.9502140878179833, 'number': 5732}	0.8200	0.8783	0.8482	0.8308
0.4546	3.56	18000	0.4806	{'precision': 0.6482188295165394, 'recall': 0.7382987972757571, 'f1': 0.6903326332904275, 'number': 6901}	{'precision': 0.788654181184669, 'recall': 0.8177712543750706, 'f1': 0.8029488387561666, 'number': 8857}	{'precision': 0.9817704736914791, 'recall': 0.9888718876060648, 'f1': 0.9853083853083853, 'number': 7189}	{'precision': 0.6854838709677419, 'recall': 0.9239130434782609, 'f1': 0.787037037037037, 'number': 92}	{'precision': 0.9236179260713118, 'recall': 0.9851709699930217, 'f1': 0.9534019922336655, 'number': 5732}	0.8249	0.8752	0.8493	0.8315
0.4471	3.76	19000	0.4823	{'precision': 0.6466100652147164, 'recall': 0.7614838429213158, 'f1': 0.6993611924407772, 'number': 6901}	{'precision': 0.7929254092057564, 'recall': 0.8149486282036807, 'f1': 0.8037861915367484, 'number': 8857}	{'precision': 0.983906770255272, 'recall': 0.9865071637223536, 'f1': 0.9852052510939778, 'number': 7189}	{'precision': 0.676923076923077, 'recall': 0.9565217391304348, 'f1': 0.7927927927927929, 'number': 92}	{'precision': 0.9211943220753793, 'recall': 0.984996510816469, 'f1': 0.9520276536548351, 'number': 5732}	0.8242	0.8793	0.8508	0.8328
0.4542	3.96	20000	0.4758	{'precision': 0.6508980521123198, 'recall': 0.7456890305752789, 'f1': 0.6950766529344229, 'number': 6901}	{'precision': 0.7868081382629621, 'recall': 0.8121260020322908, 'f1': 0.7992666259236624, 'number': 8857}	{'precision': 0.9829734219269103, 'recall': 0.9877590763666713, 'f1': 0.9853604384930272, 'number': 7189}	{'precision': 0.6744186046511628, 'recall': 0.9456521739130435, 'f1': 0.7873303167420815, 'number': 92}	{'precision': 0.920559986977047, 'recall': 0.9865666434054431, 'f1': 0.9524210526315788, 'number': 5732}	0.8244	0.8753	0.8491	0.8326
0.4452	4.16	21000	0.4833	{'precision': 0.6566687938736439, 'recall': 0.7455441240399941, 'f1': 0.6982899022801302, 'number': 6901}	{'precision': 0.7843478260869565, 'recall': 0.8147228181099695, 'f1': 0.7992468294844105, 'number': 8857}	{'precision': 0.981750311074243, 'recall': 0.9877590763666713, 'f1': 0.9847455276660656, 'number': 7189}	{'precision': 0.6854838709677419, 'recall': 0.9239130434782609, 'f1': 0.787037037037037, 'number': 92}	{'precision': 0.9249303392886412, 'recall': 0.9844731332868109, 'f1': 0.9537733457280487, 'number': 5732}	0.8261	0.8755	0.8501	0.8339
0.4412	4.36	22000	0.4888	{'precision': 0.6534954407294833, 'recall': 0.7477177220692653, 'f1': 0.6974386700006758, 'number': 6901}	{'precision': 0.7870067582297798, 'recall': 0.8151744382973919, 'f1': 0.8008429926238145, 'number': 8857}	{'precision': 0.9839268394069558, 'recall': 0.9877590763666713, 'f1': 0.9858392336526447, 'number': 7189}	{'precision': 0.6717557251908397, 'recall': 0.9565217391304348, 'f1': 0.789237668161435, 'number': 92}	{'precision': 0.9248280379954144, 'recall': 0.9851709699930217, 'f1': 0.9540462916033113, 'number': 5732}	0.8261	0.8764	0.8505	0.8325
0.4423	4.55	23000	0.4827	{'precision': 0.6532828282828282, 'recall': 0.7497464135632517, 'f1': 0.6981985021253626, 'number': 6901}	{'precision': 0.7874007180937874, 'recall': 0.817093824093937, 'f1': 0.8019725177304966, 'number': 8857}	{'precision': 0.9835202880487467, 'recall': 0.9878981777715955, 'f1': 0.9857043719639139, 'number': 7189}	{'precision': 0.6766917293233082, 'recall': 0.9782608695652174, 'f1': 0.7999999999999999, 'number': 92}	{'precision': 0.9264850672792911, 'recall': 0.984996510816469, 'f1': 0.9548452562151193, 'number': 5732}	0.8262	0.8776	0.8511	0.8338
0.4507	4.75	24000	0.4740	{'precision': 0.6584520600693108, 'recall': 0.7433705260107231, 'f1': 0.69833923223523, 'number': 6901}	{'precision': 0.7933530766699572, 'recall': 0.8166422039065147, 'f1': 0.8048291977300545, 'number': 8857}	{'precision': 0.9801898472967395, 'recall': 0.9910975100848518, 'f1': 0.9856135011758197, 'number': 7189}	{'precision': 0.6692307692307692, 'recall': 0.9456521739130435, 'f1': 0.7837837837837838, 'number': 92}	{'precision': 0.922976501305483, 'recall': 0.9867411025819958, 'f1': 0.9537942664418213, 'number': 5732}	0.8290	0.8770	0.8523	0.8347
0.4452	4.95	25000	0.4791	{'precision': 0.6528792569659443, 'recall': 0.7639472540211564, 'f1': 0.7040598290598291, 'number': 6901}	{'precision': 0.7875054513737462, 'recall': 0.8155131534379587, 'f1': 0.8012646292084975, 'number': 8857}	{'precision': 0.9825798423890502, 'recall': 0.9885936847962165, 'f1': 0.9855775897933713, 'number': 7189}	{'precision': 0.6640625, 'recall': 0.9239130434782609, 'f1': 0.7727272727272728, 'number': 92}	{'precision': 0.9227881162259223, 'recall': 0.9862177250523377, 'f1': 0.953449148254343, 'number': 5732}	0.8245	0.8807	0.8517	0.8352
0.4274	5.15	26000	0.4807	{'precision': 0.6607946026986506, 'recall': 0.766410665120997, 'f1': 0.7096947333109694, 'number': 6901}	{'precision': 0.795727342803656, 'recall': 0.8158518685785254, 'f1': 0.8056639536180176, 'number': 8857}	{'precision': 0.9844660194174757, 'recall': 0.9873417721518988, 'f1': 0.9859017987360232, 'number': 7189}	{'precision': 0.6935483870967742, 'recall': 0.9347826086956522, 'f1': 0.7962962962962964, 'number': 92}	{'precision': 0.9229010127409344, 'recall': 0.9856943475226797, 'f1': 0.9532647207693606, 'number': 5732}	0.8300	0.8811	0.8548	0.8361
0.4312	5.35	27000	0.4861	{'precision': 0.6542461005199307, 'recall': 0.7658310389798579, 'f1': 0.7056545830829829, 'number': 6901}	{'precision': 0.7903508771929825, 'recall': 0.8138195777351248, 'f1': 0.8019135562107137, 'number': 8857}	{'precision': 0.981501932633904, 'recall': 0.989010989010989, 'f1': 0.9852421533984619, 'number': 7189}	{'precision': 0.6717557251908397, 'recall': 0.9565217391304348, 'f1': 0.789237668161435, 'number': 92}	{'precision': 0.9154405820533549, 'recall': 0.9877878576413119, 'f1': 0.9502391541495343, 'number': 5732}	0.8243	0.8812	0.8518	0.8335
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Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.5
Tokenizers 0.14.1

napsternxg/nyt_ingredients-tagger-gte-small-L3-ingredient-v2

nyt_ingredients-tagger-gte-small-L3-ingredient-v2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

napsternxg/
nyt_ingredients-tagger-gte-small-L3-ingredient-v2