README.md

metadata

license: mit
base_model: thenlper/gte-small
tags:
  - generated_from_trainer
datasets:
  - napsternxg/nyt_ingredients
model-index:
  - name: nyt-ingredient-tagger-gte-small
    results: []

nyt-ingredient-tagger-gte-small

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

• Loss: 0.8647
• Comment: {'precision': 0.700344295381693, 'recall': 0.8211302895322939, 'f1': 0.7559428461587748, 'number': 7184}
• Name: {'precision': 0.8125, 'recall': 0.8314522197140707, 'f1': 0.8218668650055783, 'number': 9303}
• Qty: {'precision': 0.987037037037037, 'recall': 0.9920233980324382, 'f1': 0.9895239358175308, 'number': 7522}
• Range End: {'precision': 0.7394366197183099, 'recall': 0.9375, 'f1': 0.8267716535433071, 'number': 112}
• Unit: {'precision': 0.9283142901862577, 'recall': 0.9870194707938093, 'f1': 0.9567672205194386, 'number': 6009}
• Overall Precision: 0.8470
• Overall Recall: 0.9005
• Overall F1: 0.8729
• Overall Accuracy: 0.8468

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: 5
• label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Comment	Name	Qty	Range End	Unit	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.9427	0.2	1000	0.9321	{'precision': 0.6076682497393722, 'recall': 0.7789161098737936, 'f1': 0.6827173347214992, 'number': 6735}	{'precision': 0.7829228243021347, 'recall': 0.8126349278491081, 'f1': 0.7975022301516503, 'number': 8801}	{'precision': 0.97529493407356, 'recall': 0.9913939051918735, 'f1': 0.9832785279507451, 'number': 7088}	{'precision': 0.52046783625731, 'recall': 0.978021978021978, 'f1': 0.6793893129770994, 'number': 91}	{'precision': 0.914435009797518, 'recall': 0.982973494821836, 'f1': 0.9474663734032653, 'number': 5697}	0.8032	0.8839	0.8416	0.8183
0.9169	0.4	2000	0.9112	{'precision': 0.6358395387941388, 'recall': 0.7860430586488493, 'f1': 0.7030077684084721, 'number': 6735}	{'precision': 0.781960613643782, 'recall': 0.8166117486649245, 'f1': 0.798910626945309, 'number': 8801}	{'precision': 0.9746502285635129, 'recall': 0.9926636568848759, 'f1': 0.9835744740336898, 'number': 7088}	{'precision': 0.6277372262773723, 'recall': 0.945054945054945, 'f1': 0.7543859649122806, 'number': 91}	{'precision': 0.9176201372997712, 'recall': 0.9854309285588907, 'f1': 0.9503173931443081, 'number': 5697}	0.8137	0.8875	0.8490	0.8256
0.9	0.59	3000	0.9021	{'precision': 0.6538183570224042, 'recall': 0.8059391239792131, 'f1': 0.7219525171244263, 'number': 6735}	{'precision': 0.7920824651825858, 'recall': 0.8207021929326213, 'f1': 0.8061383928571427, 'number': 8801}	{'precision': 0.9837443946188341, 'recall': 0.9904063205417607, 'f1': 0.9870641169853768, 'number': 7088}	{'precision': 0.6792452830188679, 'recall': 0.7912087912087912, 'f1': 0.7309644670050762, 'number': 91}	{'precision': 0.9138857235878235, 'recall': 0.9854309285588907, 'f1': 0.9483108108108108, 'number': 5697}	0.8231	0.8925	0.8564	0.8302
0.9061	0.79	4000	0.8912	{'precision': 0.6613263785394933, 'recall': 0.7906458797327395, 'f1': 0.7202272266179753, 'number': 6735}	{'precision': 0.7952695269526953, 'recall': 0.821383933643904, 'f1': 0.8081158124196521, 'number': 8801}	{'precision': 0.985236220472441, 'recall': 0.9885722347629797, 'f1': 0.9869014084507043, 'number': 7088}	{'precision': 0.656, 'recall': 0.9010989010989011, 'f1': 0.7592592592592593, 'number': 91}	{'precision': 0.9215106732348112, 'recall': 0.9850798665964543, 'f1': 0.9522355137015356, 'number': 5697}	0.8289	0.8889	0.8578	0.8326
0.8889	0.99	5000	0.8908	{'precision': 0.6653019447287615, 'recall': 0.7720861172976986, 'f1': 0.7147275101367603, 'number': 6735}	{'precision': 0.7959925134867335, 'recall': 0.8214975570957845, 'f1': 0.8085439498993513, 'number': 8801}	{'precision': 0.9857926571951048, 'recall': 0.9887133182844243, 'f1': 0.9872508276396421, 'number': 7088}	{'precision': 0.6439393939393939, 'recall': 0.9340659340659341, 'f1': 0.7623318385650225, 'number': 91}	{'precision': 0.9227729293594599, 'recall': 0.9836756187467088, 'f1': 0.9522514868309261, 'number': 5697}	0.8317	0.8844	0.8572	0.8311
0.88	1.19	6000	0.8873	{'precision': 0.660211910851297, 'recall': 0.8048997772828508, 'f1': 0.7254114813327982, 'number': 6735}	{'precision': 0.7929331723910896, 'recall': 0.8210430632882627, 'f1': 0.8067433292397008, 'number': 8801}	{'precision': 0.9827899818105499, 'recall': 0.9909706546275395, 'f1': 0.9868633649455567, 'number': 7088}	{'precision': 0.635036496350365, 'recall': 0.9560439560439561, 'f1': 0.7631578947368421, 'number': 91}	{'precision': 0.9224067072168338, 'recall': 0.984904335615236, 'f1': 0.9526315789473684, 'number': 5697}	0.8266	0.8929	0.8585	0.8338
0.8751	1.39	7000	0.8866	{'precision': 0.6715582638975252, 'recall': 0.8017817371937639, 'f1': 0.7309149972929073, 'number': 6735}	{'precision': 0.8020833333333334, 'recall': 0.8224065447108283, 'f1': 0.8121178120617111, 'number': 8801}	{'precision': 0.9849719101123595, 'recall': 0.9894187358916479, 'f1': 0.9871903153153153, 'number': 7088}	{'precision': 0.6258992805755396, 'recall': 0.9560439560439561, 'f1': 0.7565217391304349, 'number': 91}	{'precision': 0.9254720105995363, 'recall': 0.9808671230472178, 'f1': 0.9523647209203238, 'number': 5697}	0.8341	0.8914	0.8618	0.8348
0.8816	1.58	8000	0.8823	{'precision': 0.6672340425531915, 'recall': 0.8148478099480326, 'f1': 0.7336898395721925, 'number': 6735}	{'precision': 0.797549398388343, 'recall': 0.8209294398363822, 'f1': 0.8090705487122061, 'number': 8801}	{'precision': 0.9851018973998594, 'recall': 0.988854401805869, 'f1': 0.9869745828346124, 'number': 7088}	{'precision': 0.6439393939393939, 'recall': 0.9340659340659341, 'f1': 0.7623318385650225, 'number': 91}	{'precision': 0.9253386190948133, 'recall': 0.9833245567842724, 'f1': 0.9534507701472215, 'number': 5697}	0.8308	0.8943	0.8614	0.8360
0.8756	1.78	9000	0.8817	{'precision': 0.6767485822306238, 'recall': 0.7973273942093542, 'f1': 0.7321063394683027, 'number': 6735}	{'precision': 0.8019714254070218, 'recall': 0.8227474150664698, 'f1': 0.8122265844083006, 'number': 8801}	{'precision': 0.9834641255605381, 'recall': 0.9901241534988713, 'f1': 0.9867829021372329, 'number': 7088}	{'precision': 0.7043478260869566, 'recall': 0.8901098901098901, 'f1': 0.7864077669902914, 'number': 91}	{'precision': 0.9209706509263814, 'recall': 0.9859575215025452, 'f1': 0.9523567310952866, 'number': 5697}	0.8355	0.8914	0.8625	0.8375
0.8695	1.98	10000	0.8788	{'precision': 0.6812743986903412, 'recall': 0.8032665181885672, 'f1': 0.7372581084764241, 'number': 6735}	{'precision': 0.7975521005623553, 'recall': 0.821838427451426, 'f1': 0.8095131505316173, 'number': 8801}	{'precision': 0.9779074614422676, 'recall': 0.9929458239277652, 'f1': 0.9853692684634231, 'number': 7088}	{'precision': 0.6267605633802817, 'recall': 0.978021978021978, 'f1': 0.7639484978540774, 'number': 91}	{'precision': 0.9206036745406824, 'recall': 0.9850798665964543, 'f1': 0.9517510387518019, 'number': 5697}	0.8337	0.8934	0.8625	0.8376
0.8537	2.18	11000	0.8804	{'precision': 0.6863314805457301, 'recall': 0.8066815144766147, 'f1': 0.741655859668282, 'number': 6735}	{'precision': 0.8018565587357719, 'recall': 0.8244517668446767, 'f1': 0.8129971988795517, 'number': 8801}	{'precision': 0.9830627099664053, 'recall': 0.9908295711060948, 'f1': 0.9869308600337269, 'number': 7088}	{'precision': 0.6825396825396826, 'recall': 0.945054945054945, 'f1': 0.792626728110599, 'number': 91}	{'precision': 0.9248306624814142, 'recall': 0.9826224328593997, 'f1': 0.9528510638297872, 'number': 5697}	0.8385	0.8938	0.8653	0.8384
0.854	2.38	12000	0.8817	{'precision': 0.6863779033270558, 'recall': 0.8117297698589458, 'f1': 0.7438095238095238, 'number': 6735}	{'precision': 0.8055925432756325, 'recall': 0.8249062606521986, 'f1': 0.8151350137539999, 'number': 8801}	{'precision': 0.9833426651735723, 'recall': 0.9911117381489842, 'f1': 0.9872119168071951, 'number': 7088}	{'precision': 0.6742424242424242, 'recall': 0.978021978021978, 'f1': 0.7982062780269058, 'number': 91}	{'precision': 0.9206687428290444, 'recall': 0.9859575215025452, 'f1': 0.9521952873368368, 'number': 5697}	0.8387	0.8960	0.8664	0.8390
0.8582	2.57	13000	0.8746	{'precision': 0.6879990019960079, 'recall': 0.8188567186340014, 'f1': 0.7477459155311504, 'number': 6735}	{'precision': 0.8027563395810364, 'recall': 0.8272923531416885, 'f1': 0.8148396844049018, 'number': 8801}	{'precision': 0.9822476935979871, 'recall': 0.9913939051918735, 'f1': 0.9867996067967982, 'number': 7088}	{'precision': 0.6693548387096774, 'recall': 0.9120879120879121, 'f1': 0.772093023255814, 'number': 91}	{'precision': 0.9260487481346377, 'recall': 0.9803405301035633, 'f1': 0.9524215552523875, 'number': 5697}	0.8387	0.8972	0.8669	0.8402
0.8554	2.77	14000	0.8743	{'precision': 0.6870807453416149, 'recall': 0.8212323682256867, 'f1': 0.7481907338518768, 'number': 6735}	{'precision': 0.8064945140197274, 'recall': 0.8268378593341665, 'f1': 0.8165394973070018, 'number': 8801}	{'precision': 0.9841692350798543, 'recall': 0.9911117381489842, 'f1': 0.9876282862364684, 'number': 7088}	{'precision': 0.672, 'recall': 0.9230769230769231, 'f1': 0.7777777777777778, 'number': 91}	{'precision': 0.923507155782201, 'recall': 0.9854309285588907, 'f1': 0.9534646739130435, 'number': 5697}	0.8394	0.8986	0.8680	0.8416
0.86	2.97	15000	0.8735	{'precision': 0.6898646083765658, 'recall': 0.8095025983667409, 'f1': 0.7449105068998496, 'number': 6735}	{'precision': 0.8031156778256546, 'recall': 0.8259288717191229, 'f1': 0.8143625364104862, 'number': 8801}	{'precision': 0.9833473271760426, 'recall': 0.9913939051918735, 'f1': 0.9873542222846705, 'number': 7088}	{'precision': 0.6611570247933884, 'recall': 0.8791208791208791, 'f1': 0.7547169811320755, 'number': 91}	{'precision': 0.9261034881798644, 'recall': 0.9833245567842724, 'f1': 0.9538566320449514, 'number': 5697}	0.8401	0.8950	0.8667	0.8423
0.845	3.17	16000	0.8782	{'precision': 0.7035061991734436, 'recall': 0.7835189309576838, 'f1': 0.7413599325653274, 'number': 6735}	{'precision': 0.7948126167710737, 'recall': 0.8217248039995455, 'f1': 0.8080446927374302, 'number': 8801}	{'precision': 0.982394858180802, 'recall': 0.9919582392776524, 'f1': 0.9871533871533871, 'number': 7088}	{'precision': 0.719626168224299, 'recall': 0.8461538461538461, 'f1': 0.7777777777777778, 'number': 91}	{'precision': 0.9267119880616813, 'recall': 0.981042654028436, 'f1': 0.9531036834924966, 'number': 5697}	0.8432	0.8872	0.8646	0.8397
0.846	3.37	17000	0.8759	{'precision': 0.6963411491883535, 'recall': 0.8025241276911655, 'f1': 0.7456715182451542, 'number': 6735}	{'precision': 0.7992290748898678, 'recall': 0.8245653902965572, 'f1': 0.8116995693753145, 'number': 8801}	{'precision': 0.984164798206278, 'recall': 0.9908295711060948, 'f1': 0.9874859392575928, 'number': 7088}	{'precision': 0.6694214876033058, 'recall': 0.8901098901098901, 'f1': 0.7641509433962264, 'number': 91}	{'precision': 0.9245874587458746, 'recall': 0.9835000877654906, 'f1': 0.9531343029684443, 'number': 5697}	0.8412	0.8929	0.8663	0.8403
0.8392	3.56	18000	0.8759	{'precision': 0.7022442588726514, 'recall': 0.799109131403118, 'f1': 0.7475519133273144, 'number': 6735}	{'precision': 0.8000660211267606, 'recall': 0.8261561186228837, 'f1': 0.8129017832187377, 'number': 8801}	{'precision': 0.9823997765050985, 'recall': 0.9922404063205418, 'f1': 0.987295570997403, 'number': 7088}	{'precision': 0.6890756302521008, 'recall': 0.9010989010989011, 'f1': 0.7809523809523808, 'number': 91}	{'precision': 0.9239380968060587, 'recall': 0.9850798665964543, 'f1': 0.9535298615240847, 'number': 5697}	0.8431	0.8933	0.8675	0.8409
0.8375	3.76	19000	0.8780	{'precision': 0.6947714900620332, 'recall': 0.8148478099480326, 'f1': 0.7500341670083366, 'number': 6735}	{'precision': 0.8054323725055432, 'recall': 0.8254743779116009, 'f1': 0.8153302283822457, 'number': 8801}	{'precision': 0.9837648705388383, 'recall': 0.991676072234763, 'f1': 0.9877046300850137, 'number': 7088}	{'precision': 0.6864406779661016, 'recall': 0.8901098901098901, 'f1': 0.7751196172248803, 'number': 91}	{'precision': 0.9236477572559367, 'recall': 0.9831490258030542, 'f1': 0.9524700280588385, 'number': 5697}	0.8419	0.8962	0.8682	0.8413
0.8366	3.96	20000	0.8742	{'precision': 0.7003211303789338, 'recall': 0.8095025983667409, 'f1': 0.7509641873278236, 'number': 6735}	{'precision': 0.804251550044287, 'recall': 0.8253607544597205, 'f1': 0.8146694330735154, 'number': 8801}	{'precision': 0.9832073887489504, 'recall': 0.9912528216704289, 'f1': 0.9872137136433891, 'number': 7088}	{'precision': 0.7053571428571429, 'recall': 0.8681318681318682, 'f1': 0.7783251231527095, 'number': 91}	{'precision': 0.9234189723320159, 'recall': 0.9842022116903634, 'f1': 0.9528422125924038, 'number': 5697}	0.8435	0.8950	0.8685	0.8417
0.8189	4.16	21000	0.8799	{'precision': 0.7027131284557027, 'recall': 0.8114328136599851, 'f1': 0.7531697905181918, 'number': 6735}	{'precision': 0.8056294326241135, 'recall': 0.8260424951710033, 'f1': 0.8157082748948107, 'number': 8801}	{'precision': 0.9825345815285734, 'recall': 0.9920993227990971, 'f1': 0.9872937872937874, 'number': 7088}	{'precision': 0.6864406779661016, 'recall': 0.8901098901098901, 'f1': 0.7751196172248803, 'number': 91}	{'precision': 0.9251195777667821, 'recall': 0.9845532736527998, 'f1': 0.9539115646258504, 'number': 5697}	0.8447	0.8960	0.8696	0.8425
0.8269	4.36	22000	0.8781	{'precision': 0.6995086630462891, 'recall': 0.8032665181885672, 'f1': 0.7478056534660309, 'number': 6735}	{'precision': 0.8004192409532216, 'recall': 0.8243381433927963, 'f1': 0.8122026308424294, 'number': 8801}	{'precision': 0.9833403331933361, 'recall': 0.9909706546275395, 'f1': 0.987140749068934, 'number': 7088}	{'precision': 0.7079646017699115, 'recall': 0.8791208791208791, 'f1': 0.7843137254901961, 'number': 91}	{'precision': 0.9244473771032663, 'recall': 0.9836756187467088, 'f1': 0.9531422740028915, 'number': 5697}	0.8425	0.8930	0.8670	0.8418
0.829	4.55	23000	0.8794	{'precision': 0.70192058136517, 'recall': 0.8031180400890868, 'f1': 0.7491170971539367, 'number': 6735}	{'precision': 0.7987892129884425, 'recall': 0.8245653902965572, 'f1': 0.8114726601811473, 'number': 8801}	{'precision': 0.9836226203807391, 'recall': 0.9913939051918735, 'f1': 0.9874929735806632, 'number': 7088}	{'precision': 0.6991150442477876, 'recall': 0.8681318681318682, 'f1': 0.7745098039215685, 'number': 91}	{'precision': 0.9249050998514606, 'recall': 0.9836756187467088, 'f1': 0.9533855052739026, 'number': 5697}	0.8429	0.8931	0.8673	0.8422
0.8183	4.75	24000	0.8815	{'precision': 0.6988397296952696, 'recall': 0.8138084632516703, 'f1': 0.7519550006859651, 'number': 6735}	{'precision': 0.806115665854199, 'recall': 0.826724235882286, 'f1': 0.8162898973467212, 'number': 8801}	{'precision': 0.9834757036829576, 'recall': 0.9908295711060948, 'f1': 0.9871389415981446, 'number': 7088}	{'precision': 0.6837606837606838, 'recall': 0.8791208791208791, 'f1': 0.7692307692307692, 'number': 91}	{'precision': 0.9244473771032663, 'recall': 0.9836756187467088, 'f1': 0.9531422740028915, 'number': 5697}	0.8435	0.8962	0.8691	0.8428
0.8201	4.95	25000	0.8808	{'precision': 0.7003986112897004, 'recall': 0.8087602078693392, 'f1': 0.7506890848952591, 'number': 6735}	{'precision': 0.8026315789473685, 'recall': 0.8247926372003181, 'f1': 0.8135612216307089, 'number': 8801}	{'precision': 0.9834849545136459, 'recall': 0.9913939051918735, 'f1': 0.9874235930583853, 'number': 7088}	{'precision': 0.6923076923076923, 'recall': 0.8901098901098901, 'f1': 0.7788461538461537, 'number': 91}	{'precision': 0.9247400561148704, 'recall': 0.9835000877654906, 'f1': 0.9532153793807416, 'number': 5697}	0.8432	0.8946	0.8682	0.8429

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.1