nyt_ingredients-tagger-paraphrase-MiniLM-L3-v2

napsternxg/nyt_ingredients-tagger-paraphrase-MiniLM-L3-v2

nyt_ingredients-tagger-paraphrase-MiniLM-L3-v2 is a machine learning model from napsternxg. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Oct 19, 2023
Updated: Oct 25, 2023
SHA: f2d0675c

Base models

sentence-transformers/paraphrase-MiniLM-L3-v2

Datasets

napsternxg/nyt_ingredients

Model card

From the Hugging Face model README

nyt_ingredients-tagger-paraphrase-MiniLM-L3-v2

This model is a fine-tuned version of sentence-transformers/paraphrase-MiniLM-L3-v2 on the nyt_ingredients dataset. It achieves the following results on the evaluation set:

Loss: 0.4826
Comment: {'precision': 0.641354292623942, 'recall': 0.7546955036994878, 'f1': 0.6934239769904563, 'number': 7028}
Name: {'precision': 0.7884535655058044, 'recall': 0.8198965294244449, 'f1': 0.8038676952340695, 'number': 9278}
Qty: {'precision': 0.9891548736939558, 'recall': 0.9878483687755911, 'f1': 0.9885011895321174, 'number': 7571}
Range End: {'precision': 0.813953488372093, 'recall': 0.7526881720430108, 'f1': 0.782122905027933, 'number': 93}
Unit: {'precision': 0.9268482490272374, 'recall': 0.9833223249669749, 'f1': 0.9542504607002644, 'number': 6056}
Overall Precision: 0.8257
Overall Recall: 0.8797
Overall F1: 0.8519
Overall Accuracy: 0.8289

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
0.7174	0.2	1000	0.6566	{'precision': 0.5088729580444236, 'recall': 0.6345252051582649, 'f1': 0.5647948868453662, 'number': 6824}	{'precision': 0.7654784240150094, 'recall': 0.7879132114052028, 'f1': 0.7765338110165697, 'number': 8803}	{'precision': 0.9619699042407661, 'recall': 0.9810267857142857, 'f1': 0.9714048901782015, 'number': 7168}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 82}	{'precision': 0.9151633349585568, 'recall': 0.9784535186794092, 'f1': 0.945750755794424, 'number': 5755}	0.7711	0.8357	0.8021	0.7841
0.6076	0.4	2000	0.5845	{'precision': 0.5363414634146342, 'recall': 0.6444900351699883, 'f1': 0.5854632587859425, 'number': 6824}	{'precision': 0.7547189819724284, 'recall': 0.8084743837328183, 'f1': 0.7806724071738058, 'number': 8803}	{'precision': 0.974110480409802, 'recall': 0.9815848214285714, 'f1': 0.9778333680772705, 'number': 7168}	{'precision': 0.6, 'recall': 0.14634146341463414, 'f1': 0.23529411764705882, 'number': 82}	{'precision': 0.9114658925979681, 'recall': 0.9821025195482189, 'f1': 0.9454667112746737, 'number': 5755}	0.7793	0.8457	0.8111	0.7988
0.5777	0.59	3000	0.5503	{'precision': 0.55049786628734, 'recall': 0.6805392731535757, 'f1': 0.6086500655307995, 'number': 6824}	{'precision': 0.7717766278568349, 'recall': 0.8132454844939225, 'f1': 0.7919685823330935, 'number': 8803}	{'precision': 0.9715620277510647, 'recall': 0.9866071428571429, 'f1': 0.9790267875683533, 'number': 7168}	{'precision': 0.45714285714285713, 'recall': 0.5853658536585366, 'f1': 0.5133689839572192, 'number': 82}	{'precision': 0.9229885057471264, 'recall': 0.9767158992180712, 'f1': 0.9490924440692275, 'number': 5755}	0.7870	0.8572	0.8206	0.8056
0.553	0.79	4000	0.5343	{'precision': 0.5713428537141486, 'recall': 0.6976846424384525, 'f1': 0.6282245827010623, 'number': 6824}	{'precision': 0.7823947512301804, 'recall': 0.812791093945246, 'f1': 0.7973033207042568, 'number': 8803}	{'precision': 0.9756500206355757, 'recall': 0.9893973214285714, 'f1': 0.9824755835699939, 'number': 7168}	{'precision': 0.5465116279069767, 'recall': 0.573170731707317, 'f1': 0.5595238095238095, 'number': 82}	{'precision': 0.9150137074665377, 'recall': 0.9859252823631625, 'f1': 0.9491468718634996, 'number': 5755}	0.7968	0.8637	0.8289	0.8122
0.5407	0.99	5000	0.5183	{'precision': 0.5958625785001848, 'recall': 0.7091148886283705, 'f1': 0.6475744396119103, 'number': 6824}	{'precision': 0.787269129287599, 'recall': 0.8134726797682609, 'f1': 0.8001564333202973, 'number': 8803}	{'precision': 0.9823488533703961, 'recall': 0.9860491071428571, 'f1': 0.9841955023323818, 'number': 7168}	{'precision': 0.5294117647058824, 'recall': 0.7682926829268293, 'f1': 0.626865671641791, 'number': 82}	{'precision': 0.9231271421576628, 'recall': 0.9827975673327541, 'f1': 0.9520282780676653, 'number': 5755}	0.8085	0.8657	0.8361	0.8186
0.5109	1.19	6000	0.5210	{'precision': 0.611145206824598, 'recall': 0.729630715123095, 'f1': 0.6651526284149355, 'number': 6824}	{'precision': 0.788943623426382, 'recall': 0.8186981710780415, 'f1': 0.803545545768759, 'number': 8803}	{'precision': 0.9822370247016375, 'recall': 0.9874441964285714, 'f1': 0.9848337275636566, 'number': 7168}	{'precision': 0.5614035087719298, 'recall': 0.7804878048780488, 'f1': 0.653061224489796, 'number': 82}	{'precision': 0.924762839385018, 'recall': 0.9824500434404866, 'f1': 0.9527340129749768, 'number': 5755}	0.8133	0.8725	0.8419	0.8201
0.5125	1.39	7000	0.5072	{'precision': 0.607906114885732, 'recall': 0.7211313012895663, 'f1': 0.6596956900596554, 'number': 6824}	{'precision': 0.7913407513495648, 'recall': 0.8159718277859821, 'f1': 0.8034675615212529, 'number': 8803}	{'precision': 0.983300862788756, 'recall': 0.9857700892857143, 'f1': 0.9845339278249966, 'number': 7168}	{'precision': 0.5714285714285714, 'recall': 0.7317073170731707, 'f1': 0.641711229946524, 'number': 82}	{'precision': 0.9279947273026857, 'recall': 0.978627280625543, 'f1': 0.952638700947226, 'number': 5755}	0.8143	0.8683	0.8404	0.8215
0.4967	1.58	8000	0.5088	{'precision': 0.6126803996546195, 'recall': 0.7278722157092614, 'f1': 0.6653271716562856, 'number': 6824}	{'precision': 0.7949600355239786, 'recall': 0.8134726797682609, 'f1': 0.8041098197742967, 'number': 8803}	{'precision': 0.9813148788927336, 'recall': 0.9891183035714286, 'f1': 0.9852011394427846, 'number': 7168}	{'precision': 0.503448275862069, 'recall': 0.8902439024390244, 'f1': 0.6431718061674009, 'number': 82}	{'precision': 0.9266295447101212, 'recall': 0.9831450912250217, 'f1': 0.9540510918135063, 'number': 5755}	0.8156	0.8714	0.8425	0.8218
0.5121	1.78	9000	0.5022	{'precision': 0.6241911398705824, 'recall': 0.735052754982415, 'f1': 0.6751009421265142, 'number': 6824}	{'precision': 0.7883291443558081, 'recall': 0.8194933545382256, 'f1': 0.803609223571349, 'number': 8803}	{'precision': 0.9850642099385818, 'recall': 0.9845145089285714, 'f1': 0.9847892827239743, 'number': 7168}	{'precision': 0.5238095238095238, 'recall': 0.8048780487804879, 'f1': 0.6346153846153846, 'number': 82}	{'precision': 0.9253999347045381, 'recall': 0.9850564726324935, 'f1': 0.9542967763656258, 'number': 5755}	0.8176	0.8739	0.8448	0.8248
0.5008	1.98	10000	0.4998	{'precision': 0.6337506387327542, 'recall': 0.7269929660023446, 'f1': 0.6771771771771772, 'number': 6824}	{'precision': 0.7910726036411494, 'recall': 0.8193797569010565, 'f1': 0.8049774008146867, 'number': 8803}	{'precision': 0.9698246567894522, 'recall': 0.9953962053571429, 'f1': 0.9824440619621343, 'number': 7168}	{'precision': 0.47468354430379744, 'recall': 0.9146341463414634, 'f1': 0.625, 'number': 82}	{'precision': 0.9263984298331698, 'recall': 0.9841876629018245, 'f1': 0.9544190749010025, 'number': 5755}	0.8192	0.8748	0.8461	0.8250
0.4863	2.18	11000	0.4954	{'precision': 0.6343217665615142, 'recall': 0.736664712778429, 'f1': 0.6816733337853415, 'number': 6824}	{'precision': 0.7913881888901063, 'recall': 0.820515733272748, 'f1': 0.8056887897378694, 'number': 8803}	{'precision': 0.9850787895690978, 'recall': 0.9854910714285714, 'f1': 0.9852848873701096, 'number': 7168}	{'precision': 0.6122448979591837, 'recall': 0.7317073170731707, 'f1': 0.6666666666666666, 'number': 82}	{'precision': 0.9250367466927977, 'recall': 0.9841876629018245, 'f1': 0.9536959084020878, 'number': 5755}	0.8224	0.8745	0.8477	0.8273
0.4893	2.38	12000	0.4947	{'precision': 0.6325136612021858, 'recall': 0.7463364595545134, 'f1': 0.6847270771712826, 'number': 6824}	{'precision': 0.7868191721132898, 'recall': 0.820515733272748, 'f1': 0.8033142412278262, 'number': 8803}	{'precision': 0.9809207797594359, 'recall': 0.9898158482142857, 'f1': 0.9853482397055761, 'number': 7168}	{'precision': 0.5068493150684932, 'recall': 0.9024390243902439, 'f1': 0.6491228070175438, 'number': 82}	{'precision': 0.9245928338762215, 'recall': 0.9864465682015638, 'f1': 0.9545187053383775, 'number': 5755}	0.8182	0.8788	0.8474	0.8257
0.489	2.57	13000	0.4869	{'precision': 0.635054931178179, 'recall': 0.736957796014068, 'f1': 0.6822220714915552, 'number': 6824}	{'precision': 0.7892552959161389, 'recall': 0.8210837214585937, 'f1': 0.8048549635320973, 'number': 8803}	{'precision': 0.9829498197948433, 'recall': 0.9892578125, 'f1': 0.9860937282714505, 'number': 7168}	{'precision': 0.5298507462686567, 'recall': 0.8658536585365854, 'f1': 0.6574074074074074, 'number': 82}	{'precision': 0.9235896602178507, 'recall': 0.9871416159860991, 'f1': 0.9543087518898035, 'number': 5755}	0.8209	0.8766	0.8479	0.8272
0.4691	2.77	14000	0.4905	{'precision': 0.6450718427022939, 'recall': 0.75, 'f1': 0.6935899173329719, 'number': 6824}	{'precision': 0.7925502692011867, 'recall': 0.8193797569010565, 'f1': 0.8057417336907953, 'number': 8803}	{'precision': 0.9818584683561834, 'recall': 0.9891183035714286, 'f1': 0.9854750156369451, 'number': 7168}	{'precision': 0.5481481481481482, 'recall': 0.9024390243902439, 'f1': 0.6820276497695853, 'number': 82}	{'precision': 0.9296515450361604, 'recall': 0.9827975673327541, 'f1': 0.9554861052453754, 'number': 5755}	0.8253	0.8784	0.8510	0.8283
0.4781	2.97	15000	0.4878	{'precision': 0.6444919517102615, 'recall': 0.7510257913247362, 'f1': 0.6936924742826204, 'number': 6824}	{'precision': 0.7942146942366916, 'recall': 0.8202885379984096, 'f1': 0.8070410729253981, 'number': 8803}	{'precision': 0.98375, 'recall': 0.9881417410714286, 'f1': 0.9859409799554566, 'number': 7168}	{'precision': 0.5433070866141733, 'recall': 0.8414634146341463, 'f1': 0.6602870813397129, 'number': 82}	{'precision': 0.9268651832460733, 'recall': 0.9843614248479583, 'f1': 0.9547484621218505, 'number': 5755}	0.8255	0.8788	0.8513	0.8293
0.4599	3.17	16000	0.4852	{'precision': 0.6450299096347206, 'recall': 0.742672919109027, 'f1': 0.6904161841836387, 'number': 6824}	{'precision': 0.7944048272078991, 'recall': 0.8225604907417926, 'f1': 0.8082375265096551, 'number': 8803}	{'precision': 0.983072013320383, 'recall': 0.9884207589285714, 'f1': 0.9857391304347826, 'number': 7168}	{'precision': 0.5289855072463768, 'recall': 0.8902439024390244, 'f1': 0.6636363636363637, 'number': 82}	{'precision': 0.9331015068719987, 'recall': 0.9791485664639444, 'f1': 0.9555706291334577, 'number': 5755}	0.8269	0.8767	0.8511	0.8306
0.4636	3.37	17000	0.4889	{'precision': 0.6520854526958291, 'recall': 0.7514654161781946, 'f1': 0.6982570806100218, 'number': 6824}	{'precision': 0.7879774305555556, 'recall': 0.8249460411223447, 'f1': 0.8060380709251347, 'number': 8803}	{'precision': 0.9818785447503112, 'recall': 0.990234375, 'f1': 0.9860387580745988, 'number': 7168}	{'precision': 0.46875, 'recall': 0.9146341463414634, 'f1': 0.6198347107438017, 'number': 82}	{'precision': 0.9251711770459733, 'recall': 0.9860990443092963, 'f1': 0.9546639751030365, 'number': 5755}	0.8247	0.8815	0.8521	0.8303
0.4688	3.56	18000	0.4901	{'precision': 0.6491074772188241, 'recall': 0.7620164126611958, 'f1': 0.7010448264239973, 'number': 6824}	{'precision': 0.7955320787938813, 'recall': 0.8211973190957628, 'f1': 0.8081609837898266, 'number': 8803}	{'precision': 0.9829474559822543, 'recall': 0.9891183035714286, 'f1': 0.9860232250886588, 'number': 7168}	{'precision': 0.5526315789473685, 'recall': 0.7682926829268293, 'f1': 0.6428571428571429, 'number': 82}	{'precision': 0.9250325945241199, 'recall': 0.98627280625543, 'f1': 0.9546716003700276, 'number': 5755}	0.8265	0.8822	0.8534	0.8319
0.4584	3.76	19000	0.4824	{'precision': 0.65155342258146, 'recall': 0.7560082063305978, 'f1': 0.6999050332383666, 'number': 6824}	{'precision': 0.7932665859830564, 'recall': 0.819038963989549, 'f1': 0.8059467918622849, 'number': 8803}	{'precision': 0.981740213030848, 'recall': 0.9900948660714286, 'f1': 0.9858998402444954, 'number': 7168}	{'precision': 0.5289855072463768, 'recall': 0.8902439024390244, 'f1': 0.6636363636363637, 'number': 82}	{'precision': 0.9315113598946329, 'recall': 0.9831450912250217, 'f1': 0.956632006086736, 'number': 5755}	0.8275	0.8800	0.8530	0.8307
0.4701	3.96	20000	0.4806	{'precision': 0.6606470053267507, 'recall': 0.7451641266119577, 'f1': 0.7003649886371462, 'number': 6824}	{'precision': 0.7948661693725319, 'recall': 0.8231284789276383, 'f1': 0.8087504883084994, 'number': 8803}	{'precision': 0.9844357976653697, 'recall': 0.98828125, 'f1': 0.98635477582846, 'number': 7168}	{'precision': 0.5384615384615384, 'recall': 0.8536585365853658, 'f1': 0.660377358490566, 'number': 82}	{'precision': 0.9314549349151425, 'recall': 0.9822762814943528, 'f1': 0.956190798376184, 'number': 5755}	0.8322	0.8780	0.8545	0.8329
0.4441	4.16	21000	0.4863	{'precision': 0.6520674996779595, 'recall': 0.7417936694021102, 'f1': 0.6940426407074792, 'number': 6824}	{'precision': 0.7900534409423056, 'recall': 0.8229012836533001, 'f1': 0.8061428889383486, 'number': 8803}	{'precision': 0.9831851028349082, 'recall': 0.9870256696428571, 'f1': 0.9851016429963798, 'number': 7168}	{'precision': 0.5882352941176471, 'recall': 0.7317073170731707, 'f1': 0.6521739130434783, 'number': 82}	{'precision': 0.9332010582010583, 'recall': 0.9807124239791486, 'f1': 0.9563670253325427, 'number': 5755}	0.8285	0.8761	0.8516	0.8310
0.4375	4.36	22000	0.4853	{'precision': 0.6539783577339274, 'recall': 0.7527842907385698, 'f1': 0.699911438108863, 'number': 6824}	{'precision': 0.7924075488164066, 'recall': 0.8251732363966829, 'f1': 0.8084585420144685, 'number': 8803}	{'precision': 0.9818735298187353, 'recall': 0.9899553571428571, 'f1': 0.985897881208753, 'number': 7168}	{'precision': 0.5103448275862069, 'recall': 0.9024390243902439, 'f1': 0.6519823788546256, 'number': 82}	{'precision': 0.9296490652673007, 'recall': 0.9850564726324935, 'f1': 0.9565510841137266, 'number': 5755}	0.8278	0.8815	0.8538	0.8326
0.4566	4.55	23000	0.4807	{'precision': 0.6580237358101135, 'recall': 0.7475087924970691, 'f1': 0.6999176728869374, 'number': 6824}	{'precision': 0.7963125548726954, 'recall': 0.8242644552993298, 'f1': 0.8100474462740721, 'number': 8803}	{'precision': 0.9836270292770917, 'recall': 0.9889787946428571, 'f1': 0.9862956521739129, 'number': 7168}	{'precision': 0.6090909090909091, 'recall': 0.8170731707317073, 'f1': 0.6979166666666666, 'number': 82}	{'precision': 0.9343936381709742, 'recall': 0.9800173761946134, 'f1': 0.9566618607412434, 'number': 5755}	0.8324	0.8785	0.8548	0.8325
0.4546	4.75	24000	0.4797	{'precision': 0.6631361405321622, 'recall': 0.7523446658851114, 'f1': 0.7049292873815736, 'number': 6824}	{'precision': 0.7910382513661203, 'recall': 0.8222196978302851, 'f1': 0.8063276332646355, 'number': 8803}	{'precision': 0.985369931726348, 'recall': 0.9866071428571429, 'f1': 0.9859881491808993, 'number': 7168}	{'precision': 0.651685393258427, 'recall': 0.7073170731707317, 'f1': 0.6783625730994152, 'number': 82}	{'precision': 0.9321222130470685, 'recall': 0.9807124239791486, 'f1': 0.9558001693480102, 'number': 5755}	0.8323	0.8782	0.8547	0.8324
0.4462	4.95	25000	0.4855	{'precision': 0.6583979328165375, 'recall': 0.7467760844079718, 'f1': 0.6998077451249656, 'number': 6824}	{'precision': 0.7887430864331417, 'recall': 0.8261956151312053, 'f1': 0.8070350643586329, 'number': 8803}	{'precision': 0.9822788315104527, 'recall': 0.9898158482142857, 'f1': 0.9860329372524493, 'number': 7168}	{'precision': 0.5390625, 'recall': 0.8414634146341463, 'f1': 0.6571428571428573, 'number': 82}	{'precision': 0.9290873276428102, 'recall': 0.9834926151172894, 'f1': 0.9555161644298135, 'number': 5755}	0.8286	0.8799	0.8535	0.8325
0.433	5.15	26000	0.4934	{'precision': 0.656934306569343, 'recall': 0.7517584994138335, 'f1': 0.7011549238023645, 'number': 6824}	{'precision': 0.7907638432917488, 'recall': 0.8208565261842554, 'f1': 0.805529234713784, 'number': 8803}	{'precision': 0.9852367688022284, 'recall': 0.9868861607142857, 'f1': 0.9860607750209088, 'number': 7168}	{'precision': 0.5172413793103449, 'recall': 0.9146341463414634, 'f1': 0.6607929515418502, 'number': 82}	{'precision': 0.935024034477043, 'recall': 0.9801911381407472, 'f1': 0.9570749915167968, 'number': 5755}	0.8298	0.8782	0.8533	0.8298
0.442	5.35	27000	0.4850	{'precision': 0.6560364464692483, 'recall': 0.7596717467760844, 'f1': 0.7040608447643624, 'number': 6824}	{'precision': 0.7970520294797052, 'recall': 0.8231284789276383, 'f1': 0.8098804068402817, 'number': 8803}	{'precision': 0.9842902822188239, 'recall': 0.9877232142857143, 'f1': 0.9860037601838312, 'number': 7168}	{'precision': 0.5407407407407407, 'recall': 0.8902439024390244, 'f1': 0.6728110599078342, 'number': 82}	{'precision': 0.9294735115630638, 'recall': 0.9847089487402259, 'f1': 0.956294296321296, 'number': 5755}	0.8301	0.8819	0.8552	0.8333
0.4348	5.54	28000	0.4909	{'precision': 0.6646636185499674, 'recall': 0.7456037514654161, 'f1': 0.7028109676082602, 'number': 6824}	{'precision': 0.7959763831183031, 'recall': 0.8269907985913894, 'f1': 0.8111872527717422, 'number': 8803}	{'precision': 0.982281284606866, 'recall': 0.9899553571428571, 'f1': 0.9861033907726514, 'number': 7168}	{'precision': 0.5867768595041323, 'recall': 0.8658536585365854, 'f1': 0.6995073891625616, 'number': 82}	{'precision': 0.9282555282555283, 'recall': 0.9847089487402259, 'f1': 0.9556492411467116, 'number': 5755}	0.8331	0.8802	0.8560	0.8320
0.4324	5.74	29000	0.4822	{'precision': 0.6659320715582059, 'recall': 0.7527842907385698, 'f1': 0.7066996835878389, 'number': 6824}	{'precision': 0.7957977675640184, 'recall': 0.8260820174940361, 'f1': 0.8106571540047935, 'number': 8803}	{'precision': 0.9828278631768453, 'recall': 0.9900948660714286, 'f1': 0.9864479810966712, 'number': 7168}	{'precision': 0.5982905982905983, 'recall': 0.8536585365853658, 'f1': 0.7035175879396985, 'number': 82}	{'precision': 0.9346850589995014, 'recall': 0.9772371850564726, 'f1': 0.9554875976894326, 'number': 5755}	0.8342	0.8801	0.8566	0.8341
0.4375	5.94	30000	0.4843	{'precision': 0.6641568220173958, 'recall': 0.7497069167643611, 'f1': 0.70434363598816, 'number': 6824}	{'precision': 0.7948914711686034, 'recall': 0.823696467113484, 'f1': 0.8090376569037656, 'number': 8803}	{'precision': 0.9834836918806384, 'recall': 0.9885602678571429, 'f1': 0.9860154456272177, 'number': 7168}	{'precision': 0.5352112676056338, 'recall': 0.926829268292683, 'f1': 0.6785714285714285, 'number': 82}	{'precision': 0.9328703703703703, 'recall': 0.9803649000868809, 'f1': 0.9560281284419215, 'number': 5755}	0.8329	0.8791	0.8554	0.8328
0.4226	6.14	31000	0.4878	{'precision': 0.6603463531791177, 'recall': 0.7655334114888629, 'f1': 0.7090600610790635, 'number': 6824}	{'precision': 0.7907611663208475, 'recall': 0.8225604907417926, 'f1': 0.806347438752784, 'number': 8803}	{'precision': 0.9829356270810211, 'recall': 0.9884207589285714, 'f1': 0.9856705620478576, 'number': 7168}	{'precision': 0.5241379310344828, 'recall': 0.926829268292683, 'f1': 0.669603524229075, 'number': 82}	{'precision': 0.9298015417418403, 'recall': 0.9850564726324935, 'f1': 0.9566317921025986, 'number': 5755}	0.8289	0.8835	0.8553	0.8336
0.4305	6.34	32000	0.4837	{'precision': 0.6565505182964906, 'recall': 0.770369284876905, 'f1': 0.7089205043489988, 'number': 6824}	{'precision': 0.8005732554293904, 'recall': 0.8249460411223447, 'f1': 0.8125769273805529, 'number': 8803}	{'precision': 0.985369931726348, 'recall': 0.9866071428571429, 'f1': 0.9859881491808993, 'number': 7168}	{'precision': 0.5528455284552846, 'recall': 0.8292682926829268, 'f1': 0.6634146341463415, 'number': 82}	{'precision': 0.9309437684972048, 'recall': 0.9838401390095569, 'f1': 0.956661316211878, 'number': 5755}	0.8313	0.8844	0.8570	0.8342
0.4142	6.53	33000	0.4862	{'precision': 0.6611656843483955, 'recall': 0.7397420867526378, 'f1': 0.6982502247734974, 'number': 6824}	{'precision': 0.7957059918939643, 'recall': 0.8251732363966829, 'f1': 0.8101717599821548, 'number': 8803}	{'precision': 0.9761611179613646, 'recall': 0.9940011160714286, 'f1': 0.9850003456141564, 'number': 7168}	{'precision': 0.5323741007194245, 'recall': 0.9024390243902439, 'f1': 0.669683257918552, 'number': 82}	{'precision': 0.9326843755155915, 'recall': 0.9822762814943528, 'f1': 0.9568381855111713, 'number': 5755}	0.8315	0.8789	0.8545	0.8332
0.4327	6.73	34000	0.4835	{'precision': 0.6665352260778128, 'recall': 0.7431125439624854, 'f1': 0.7027439024390244, 'number': 6824}	{'precision': 0.7882020026121027, 'recall': 0.8226740883789617, 'f1': 0.8050692012673003, 'number': 8803}	{'precision': 0.9820317899101589, 'recall': 0.9912109375, 'f1': 0.9866000138859959, 'number': 7168}	{'precision': 0.5833333333333334, 'recall': 0.8536585365853658, 'f1': 0.6930693069306931, 'number': 82}	{'precision': 0.9306035191580332, 'recall': 0.9833188531711555, 'f1': 0.9562352145995269, 'number': 5755}	0.8318	0.8783	0.8544	0.8330
0.4299	6.93	35000	0.4833	{'precision': 0.660639246147969, 'recall': 0.7602579132473622, 'f1': 0.706956462492335, 'number': 6824}	{'precision': 0.7949644225506295, 'recall': 0.8249460411223447, 'f1': 0.8096777790166128, 'number': 8803}	{'precision': 0.9836133870295792, 'recall': 0.9881417410714286, 'f1': 0.9858723641171967, 'number': 7168}	{'precision': 0.5401459854014599, 'recall': 0.9024390243902439, 'f1': 0.6757990867579909, 'number': 82}	{'precision': 0.9348582794629537, 'recall': 0.9800173761946134, 'f1': 0.9569053274516457, 'number': 5755}	0.8316	0.8818	0.8560	0.8333
0.4165	7.13	36000	0.4871	{'precision': 0.6667525441195414, 'recall': 0.7584994138335287, 'f1': 0.7096729965037363, 'number': 6824}	{'precision': 0.7905082536924414, 'recall': 0.8268772009542201, 'f1': 0.8082838265504414, 'number': 8803}	{'precision': 0.9828278631768453, 'recall': 0.9900948660714286, 'f1': 0.9864479810966712, 'number': 7168}	{'precision': 0.5303030303030303, 'recall': 0.8536585365853658, 'f1': 0.6542056074766355, 'number': 82}	{'precision': 0.930909689093601, 'recall': 0.9833188531711555, 'f1': 0.9563968227142133, 'number': 5755}	0.8315	0.8830	0.8565	0.8348
0.4213	7.33	37000	0.4882	{'precision': 0.667705088265836, 'recall': 0.753810082063306, 'f1': 0.7081497797356828, 'number': 6824}	{'precision': 0.7934001306904814, 'recall': 0.827558786777235, 'f1': 0.8101195440644984, 'number': 8803}	{'precision': 0.983754512635379, 'recall': 0.9884207589285714, 'f1': 0.9860821155184412, 'number': 7168}	{'precision': 0.5333333333333333, 'recall': 0.8780487804878049, 'f1': 0.663594470046083, 'number': 82}	{'precision': 0.9285831285831286, 'recall': 0.9850564726324935, 'f1': 0.9559865092748736, 'number': 5755}	0.8327	0.8821	0.8567	0.8333
0.4122	7.52	38000	0.4914	{'precision': 0.660262119862578, 'recall': 0.7604044548651817, 'f1': 0.7068037866920929, 'number': 6824}	{'precision': 0.7921752397558849, 'recall': 0.8257412245825286, 'f1': 0.8086100450525612, 'number': 8803}	{'precision': 0.9839090026355944, 'recall': 0.9895368303571429, 'f1': 0.9867148918411351, 'number': 7168}	{'precision': 0.5714285714285714, 'recall': 0.8292682926829268, 'f1': 0.6766169154228855, 'number': 82}	{'precision': 0.9313693219223174, 'recall': 0.9833188531711555, 'f1': 0.9566393373341222, 'number': 5755}	0.8304	0.8829	0.8559	0.8337
0.4034	7.72	39000	0.4884	{'precision': 0.6667092815136794, 'recall': 0.7642145369284877, 'f1': 0.7121398334016114, 'number': 6824}	{'precision': 0.7976099111939481, 'recall': 0.8264228104055436, 'f1': 0.8117607676857844, 'number': 8803}	{'precision': 0.984309913912802, 'recall': 0.9889787946428571, 'f1': 0.9866388308977034, 'number': 7168}	{'precision': 0.568, 'recall': 0.8658536585365854, 'f1': 0.6859903381642511, 'number': 82}	{'precision': 0.9304733727810651, 'recall': 0.9836663770634231, 'f1': 0.9563307711799983, 'number': 5755}	0.8339	0.8840	0.8582	0.8344
0.4209	7.92	40000	0.4910	{'precision': 0.6634454322896534, 'recall': 0.7545427901524033, 'f1': 0.706067877956805, 'number': 6824}	{'precision': 0.7984163642362256, 'recall': 0.8247188458480064, 'f1': 0.81135449262405, 'number': 8803}	{'precision': 0.9832317073170732, 'recall': 0.9898158482142857, 'f1': 0.9865127919911012, 'number': 7168}	{'precision': 0.5413533834586466, 'recall': 0.8780487804878049, 'f1': 0.6697674418604651, 'number': 82}	{'precision': 0.9315789473684211, 'recall': 0.9841876629018245, 'f1': 0.9571609632446134, 'number': 5755}	0.8335	0.8815	0.8568	0.8327
0.4078	8.12	41000	0.4923	{'precision': 0.6621812123542228, 'recall': 0.7571805392731535, 'f1': 0.7065016749846176, 'number': 6824}	{'precision': 0.7941208611080757, 'recall': 0.8255140293081904, 'f1': 0.8095132004010249, 'number': 8803}	{'precision': 0.9834882752879145, 'recall': 0.9888392857142857, 'f1': 0.9861565217391305, 'number': 7168}	{'precision': 0.5581395348837209, 'recall': 0.8780487804878049, 'f1': 0.6824644549763034, 'number': 82}	{'precision': 0.9316534914361001, 'recall': 0.982971329278888, 'f1': 0.9566246723598546, 'number': 5755}	0.8317	0.8819	0.8561	0.8335
0.4098	8.32	42000	0.4903	{'precision': 0.6613067891781521, 'recall': 0.7593786635404455, 'f1': 0.7069577080491133, 'number': 6824}	{'precision': 0.7961356899769458, 'recall': 0.8238100647506532, 'f1': 0.8097364895042429, 'number': 8803}	{'precision': 0.9838888888888889, 'recall': 0.98828125, 'f1': 0.9860801781737194, 'number': 7168}	{'precision': 0.5669291338582677, 'recall': 0.8780487804878049, 'f1': 0.6889952153110047, 'number': 82}	{'precision': 0.9302478253733792, 'recall': 0.9848827106863597, 'f1': 0.9567859554355165, 'number': 5755}	0.8318	0.8822	0.8562	0.8344
0.4009	8.51	43000	0.4936	{'precision': 0.6647553516819572, 'recall': 0.7645076201641267, 'f1': 0.7111504907306434, 'number': 6824}	{'precision': 0.7969554265688314, 'recall': 0.8266500056798819, 'f1': 0.8115311698449873, 'number': 8803}	{'precision': 0.9840366463076069, 'recall': 0.9889787946428571, 'f1': 0.9865015307542444, 'number': 7168}	{'precision': 0.5689655172413793, 'recall': 0.8048780487804879, 'f1': 0.6666666666666667, 'number': 82}	{'precision': 0.9306605323693723, 'recall': 0.9841876629018245, 'f1': 0.9566759564225994, 'number': 5755}	0.8331	0.8841	0.8578	0.8348
0.41	8.71	44000	0.4894	{'precision': 0.6659480025364616, 'recall': 0.7694900351699883, 'f1': 0.7139846352573254, 'number': 6824}	{'precision': 0.8019158775600088, 'recall': 0.8273315915028967, 'f1': 0.8144254962258876, 'number': 8803}	{'precision': 0.9834974344751075, 'recall': 0.9893973214285714, 'f1': 0.9864385562278323, 'number': 7168}	{'precision': 0.5407407407407407, 'recall': 0.8902439024390244, 'f1': 0.6728110599078342, 'number': 82}	{'precision': 0.9306946953522746, 'recall': 0.9847089487402259, 'f1': 0.9569402228976698, 'number': 5755}	0.8344	0.8859	0.8594	0.8359
0.4177	8.91	45000	0.4894	{'precision': 0.6695323694151362, 'recall': 0.7532239155920282, 'f1': 0.7089166264395559, 'number': 6824}	{'precision': 0.7959273045763083, 'recall': 0.8258548222196979, 'f1': 0.810614930032893, 'number': 8803}	{'precision': 0.9836270292770917, 'recall': 0.9889787946428571, 'f1': 0.9862956521739129, 'number': 7168}	{'precision': 0.5772357723577236, 'recall': 0.8658536585365854, 'f1': 0.6926829268292684, 'number': 82}	{'precision': 0.9314370272936534, 'recall': 0.9843614248479583, 'f1': 0.9571682014023823, 'number': 5755}	0.8350	0.8814	0.8575	0.8339
0.399	9.11	46000	0.4909	{'precision': 0.6659901052898642, 'recall': 0.7693434935521688, 'f1': 0.7139457401237506, 'number': 6824}	{'precision': 0.7953452797202797, 'recall': 0.8268772009542201, 'f1': 0.8108047897521581, 'number': 8803}	{'precision': 0.9826725810923205, 'recall': 0.9889787946428571, 'f1': 0.9858156028368794, 'number': 7168}	{'precision': 0.5488721804511278, 'recall': 0.8902439024390244, 'f1': 0.6790697674418604, 'number': 82}	{'precision': 0.9298820445609436, 'recall': 0.98627280625543, 'f1': 0.9572476600050595, 'number': 5755}	0.8321	0.8860	0.8582	0.8356
0.4066	9.31	47000	0.4892	{'precision': 0.6655544147843943, 'recall': 0.7599648300117233, 'f1': 0.7096332785987959, 'number': 6824}	{'precision': 0.7964563053702286, 'recall': 0.8272179938657276, 'f1': 0.8115457483561797, 'number': 8803}	{'precision': 0.9824075356697604, 'recall': 0.9893973214285714, 'f1': 0.9858900396191006, 'number': 7168}	{'precision': 0.5538461538461539, 'recall': 0.8780487804878049, 'f1': 0.6792452830188679, 'number': 82}	{'precision': 0.9298475659727914, 'recall': 0.9857515204170286, 'f1': 0.9569838056680162, 'number': 5755}	0.8328	0.8838	0.8575	0.8340
0.397	9.5	48000	0.4918	{'precision': 0.6680455015511892, 'recall': 0.757327080890973, 'f1': 0.7098901098901098, 'number': 6824}	{'precision': 0.7944098700731521, 'recall': 0.8265364080427127, 'f1': 0.8101547711836098, 'number': 8803}	{'precision': 0.9830884391461048, 'recall': 0.9893973214285714, 'f1': 0.9862327909887358, 'number': 7168}	{'precision': 0.5625, 'recall': 0.8780487804878049, 'f1': 0.6857142857142857, 'number': 82}	{'precision': 0.9305304647725406, 'recall': 0.9845351867940921, 'f1': 0.9567713610266801, 'number': 5755}	0.8334	0.8827	0.8574	0.8347
0.3987	9.7	49000	0.4936	{'precision': 0.6657629744384198, 'recall': 0.7557151230949589, 'f1': 0.7078929306794783, 'number': 6824}	{'precision': 0.7955613862468569, 'recall': 0.8266500056798819, 'f1': 0.8108077994428969, 'number': 8803}	{'precision': 0.9829474559822543, 'recall': 0.9891183035714286, 'f1': 0.9860232250886588, 'number': 7168}	{'precision': 0.5625, 'recall': 0.8780487804878049, 'f1': 0.6857142857142857, 'number': 82}	{'precision': 0.9306605323693723, 'recall': 0.9841876629018245, 'f1': 0.9566759564225994, 'number': 5755}	0.8331	0.8822	0.8570	0.8339
0.4109	9.9	50000	0.4929	{'precision': 0.6672281974860698, 'recall': 0.7545427901524033, 'f1': 0.7082043875937006, 'number': 6824}	{'precision': 0.794249480704056, 'recall': 0.8252868340338521, 'f1': 0.8094707520891365, 'number': 8803}	{'precision': 0.983222407099279, 'recall': 0.9892578125, 'f1': 0.986230876216968, 'number': 7168}	{'precision': 0.5669291338582677, 'recall': 0.8780487804878049, 'f1': 0.6889952153110047, 'number': 82}	{'precision': 0.9305076392311483, 'recall': 0.9841876629018245, 'f1': 0.956595169734842, 'number': 5755}	0.8333	0.8816	0.8568	0.8339

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.5
Tokenizers 0.14.1

Library: transformers
Type: bert
License: apache-2.0
Created: Oct 19, 2023
Updated: Oct 25, 2023