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jinhybr/
OCR-LM-v1

Name: OCR-LM-v1
Availability: InStock

generated_from_trainer

funsd

autotrain_compatible

endpoints_compatible

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OCR-LM-v1

This model is a fine-tuned version of jinhybr/layoutlm-funsd-pytorch on the funsd dataset. It achieves the following results on the evaluation set:

Loss: 1.1740
Answer: {'precision': 0.7201327433628318, 'recall': 0.8046971569839307, 'f1': 0.7600700525394046, 'number': 809}
Header: {'precision': 0.4246575342465753, 'recall': 0.5210084033613446, 'f1': 0.46792452830188674, 'number': 119}
Question: {'precision': 0.8236380424746076, 'recall': 0.8375586854460094, 'f1': 0.8305400372439479, 'number': 1065}
Overall Precision: 0.7525
Overall Recall: 0.8053
Overall F1: 0.7780
Overall Accuracy: 0.8146

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: 3e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.3093	1.0	10	0.7358	{'precision': 0.7053763440860215, 'recall': 0.8108776266996292, 'f1': 0.7544565842438183, 'number': 809}	{'precision': 0.33587786259541985, 'recall': 0.3697478991596639, 'f1': 0.35200000000000004, 'number': 119}	{'precision': 0.7900900900900901, 'recall': 0.8234741784037559, 'f1': 0.8064367816091954, 'number': 1065}	0.7264	0.7913	0.7574	0.8064
0.2626	2.0	20	0.7389	{'precision': 0.7217090069284064, 'recall': 0.7725587144622992, 'f1': 0.746268656716418, 'number': 809}	{'precision': 0.33986928104575165, 'recall': 0.4369747899159664, 'f1': 0.3823529411764706, 'number': 119}	{'precision': 0.7693661971830986, 'recall': 0.8206572769953052, 'f1': 0.79418446160836, 'number': 1065}	0.7197	0.7782	0.7478	0.7999
0.2096	3.0	30	0.7834	{'precision': 0.7417452830188679, 'recall': 0.7775030902348579, 'f1': 0.7592033796016897, 'number': 809}	{'precision': 0.3724137931034483, 'recall': 0.453781512605042, 'f1': 0.40909090909090906, 'number': 119}	{'precision': 0.7889087656529516, 'recall': 0.828169014084507, 'f1': 0.8080622995877232, 'number': 1065}	0.7414	0.7852	0.7627	0.8003
0.1755	4.0	40	0.7856	{'precision': 0.6917372881355932, 'recall': 0.8071693448702101, 'f1': 0.7450085567598402, 'number': 809}	{'precision': 0.35135135135135137, 'recall': 0.4369747899159664, 'f1': 0.3895131086142322, 'number': 119}	{'precision': 0.7893333333333333, 'recall': 0.8338028169014085, 'f1': 0.810958904109589, 'number': 1065}	0.7185	0.7993	0.7568	0.8005
0.1421	5.0	50	0.8088	{'precision': 0.7144444444444444, 'recall': 0.7948084054388134, 'f1': 0.7524868344060853, 'number': 809}	{'precision': 0.39436619718309857, 'recall': 0.47058823529411764, 'f1': 0.4291187739463601, 'number': 119}	{'precision': 0.7915543575920935, 'recall': 0.8272300469483568, 'f1': 0.8089990817263545, 'number': 1065}	0.7332	0.7928	0.7618	0.8014
0.1235	6.0	60	0.8637	{'precision': 0.7262313860252004, 'recall': 0.7836835599505563, 'f1': 0.7538644470868016, 'number': 809}	{'precision': 0.37410071942446044, 'recall': 0.4369747899159664, 'f1': 0.40310077519379844, 'number': 119}	{'precision': 0.7994604316546763, 'recall': 0.8347417840375587, 'f1': 0.8167202572347267, 'number': 1065}	0.7415	0.7903	0.7651	0.8026
0.1057	7.0	70	0.8848	{'precision': 0.7323290845886443, 'recall': 0.7812113720642769, 'f1': 0.7559808612440193, 'number': 809}	{'precision': 0.3986013986013986, 'recall': 0.4789915966386555, 'f1': 0.4351145038167939, 'number': 119}	{'precision': 0.7989080982711556, 'recall': 0.8244131455399061, 'f1': 0.8114602587800368, 'number': 1065}	0.7444	0.7863	0.7648	0.7959
0.1054	8.0	80	0.9131	{'precision': 0.7241758241758242, 'recall': 0.8145859085290482, 'f1': 0.7667248400232693, 'number': 809}	{'precision': 0.41916167664670656, 'recall': 0.5882352941176471, 'f1': 0.4895104895104894, 'number': 119}	{'precision': 0.8152686145146089, 'recall': 0.812206572769953, 'f1': 0.8137347130761995, 'number': 1065}	0.7456	0.7998	0.7717	0.8021
0.0814	9.0	90	0.9202	{'precision': 0.7013129102844639, 'recall': 0.792336217552534, 'f1': 0.7440510737086476, 'number': 809}	{'precision': 0.42758620689655175, 'recall': 0.5210084033613446, 'f1': 0.4696969696969697, 'number': 119}	{'precision': 0.8076572470373746, 'recall': 0.831924882629108, 'f1': 0.8196114708603145, 'number': 1065}	0.7370	0.7973	0.7660	0.8017
0.0722	10.0	100	0.9309	{'precision': 0.711211778029445, 'recall': 0.7762669962917181, 'f1': 0.7423167848699764, 'number': 809}	{'precision': 0.3816793893129771, 'recall': 0.42016806722689076, 'f1': 0.4, 'number': 119}	{'precision': 0.8154706430568499, 'recall': 0.8215962441314554, 'f1': 0.8185219831618334, 'number': 1065}	0.7441	0.7792	0.7613	0.8029
0.062	11.0	110	0.9820	{'precision': 0.717391304347826, 'recall': 0.7750309023485785, 'f1': 0.7450980392156862, 'number': 809}	{'precision': 0.37735849056603776, 'recall': 0.5042016806722689, 'f1': 0.43165467625899284, 'number': 119}	{'precision': 0.7917414721723519, 'recall': 0.828169014084507, 'f1': 0.8095456631482332, 'number': 1065}	0.7308	0.7873	0.7580	0.7977
0.056	12.0	120	0.9787	{'precision': 0.7014270032930845, 'recall': 0.7898640296662547, 'f1': 0.7430232558139536, 'number': 809}	{'precision': 0.3881578947368421, 'recall': 0.4957983193277311, 'f1': 0.4354243542435424, 'number': 119}	{'precision': 0.8092592592592592, 'recall': 0.8206572769953052, 'f1': 0.814918414918415, 'number': 1065}	0.7336	0.7888	0.7602	0.8069
0.0521	13.0	130	1.0012	{'precision': 0.7094972067039106, 'recall': 0.7849196538936959, 'f1': 0.7453051643192488, 'number': 809}	{'precision': 0.39568345323741005, 'recall': 0.46218487394957986, 'f1': 0.4263565891472868, 'number': 119}	{'precision': 0.8278457196613358, 'recall': 0.8262910798122066, 'f1': 0.8270676691729324, 'number': 1065}	0.7487	0.7878	0.7677	0.8054
0.0512	14.0	140	1.0412	{'precision': 0.7181818181818181, 'recall': 0.7812113720642769, 'f1': 0.7483718176435761, 'number': 809}	{'precision': 0.417910447761194, 'recall': 0.47058823529411764, 'f1': 0.4426877470355731, 'number': 119}	{'precision': 0.7925531914893617, 'recall': 0.8394366197183099, 'f1': 0.8153214774281805, 'number': 1065}	0.7386	0.7938	0.7652	0.7924
0.0422	15.0	150	1.0369	{'precision': 0.6987315010570825, 'recall': 0.8170580964153276, 'f1': 0.7532763532763533, 'number': 809}	{'precision': 0.4222222222222222, 'recall': 0.4789915966386555, 'f1': 0.44881889763779526, 'number': 119}	{'precision': 0.8138248847926267, 'recall': 0.8291079812206573, 'f1': 0.8213953488372093, 'number': 1065}	0.7392	0.8033	0.7699	0.8060
0.041	16.0	160	1.0669	{'precision': 0.7108843537414966, 'recall': 0.7750309023485785, 'f1': 0.7415730337078651, 'number': 809}	{'precision': 0.4117647058823529, 'recall': 0.47058823529411764, 'f1': 0.4392156862745098, 'number': 119}	{'precision': 0.7953736654804271, 'recall': 0.8394366197183099, 'f1': 0.8168113293741435, 'number': 1065}	0.7362	0.7913	0.7628	0.7989
0.0338	17.0	170	1.0376	{'precision': 0.7056277056277056, 'recall': 0.8059332509270705, 'f1': 0.7524523946912869, 'number': 809}	{'precision': 0.4117647058823529, 'recall': 0.5294117647058824, 'f1': 0.463235294117647, 'number': 119}	{'precision': 0.8159111933395005, 'recall': 0.828169014084507, 'f1': 0.8219944082013048, 'number': 1065}	0.7400	0.8013	0.7695	0.8062
0.0343	18.0	180	1.0498	{'precision': 0.7165178571428571, 'recall': 0.7935723114956736, 'f1': 0.7530791788856306, 'number': 809}	{'precision': 0.42953020134228187, 'recall': 0.5378151260504201, 'f1': 0.47761194029850745, 'number': 119}	{'precision': 0.8065693430656934, 'recall': 0.8300469483568075, 'f1': 0.8181397501156872, 'number': 1065}	0.7426	0.7978	0.7692	0.8035
0.0294	19.0	190	1.0455	{'precision': 0.7022900763358778, 'recall': 0.796044499381953, 'f1': 0.7462340672074159, 'number': 809}	{'precision': 0.42857142857142855, 'recall': 0.5042016806722689, 'f1': 0.4633204633204633, 'number': 119}	{'precision': 0.8277153558052435, 'recall': 0.8300469483568075, 'f1': 0.8288795124238162, 'number': 1065}	0.7473	0.7968	0.7712	0.8077
0.0302	20.0	200	1.0363	{'precision': 0.7261363636363637, 'recall': 0.7898640296662547, 'f1': 0.7566607460035524, 'number': 809}	{'precision': 0.4225352112676056, 'recall': 0.5042016806722689, 'f1': 0.45977011494252873, 'number': 119}	{'precision': 0.8149498632634458, 'recall': 0.8394366197183099, 'f1': 0.8270120259019426, 'number': 1065}	0.7518	0.7993	0.7748	0.8073
0.0232	21.0	210	1.0406	{'precision': 0.7085152838427947, 'recall': 0.8022249690976514, 'f1': 0.7524637681159421, 'number': 809}	{'precision': 0.4142857142857143, 'recall': 0.48739495798319327, 'f1': 0.4478764478764479, 'number': 119}	{'precision': 0.8198529411764706, 'recall': 0.8375586854460094, 'f1': 0.8286112401300512, 'number': 1065}	0.7458	0.8023	0.7730	0.8096
0.025	22.0	220	1.0627	{'precision': 0.7220338983050848, 'recall': 0.7898640296662547, 'f1': 0.7544273907910272, 'number': 809}	{'precision': 0.4306569343065693, 'recall': 0.4957983193277311, 'f1': 0.46093749999999994, 'number': 119}	{'precision': 0.8222836095764272, 'recall': 0.8384976525821596, 'f1': 0.8303114830311482, 'number': 1065}	0.7547	0.7983	0.7759	0.8125
0.0203	23.0	230	1.0621	{'precision': 0.7149122807017544, 'recall': 0.8059332509270705, 'f1': 0.757699012202208, 'number': 809}	{'precision': 0.42857142857142855, 'recall': 0.5042016806722689, 'f1': 0.4633204633204633, 'number': 119}	{'precision': 0.8182656826568265, 'recall': 0.8328638497652582, 'f1': 0.8255002326663564, 'number': 1065}	0.7486	0.8023	0.7745	0.8139
0.0214	24.0	240	1.1079	{'precision': 0.7268571428571429, 'recall': 0.7861557478368356, 'f1': 0.7553444180522566, 'number': 809}	{'precision': 0.40397350993377484, 'recall': 0.5126050420168067, 'f1': 0.45185185185185184, 'number': 119}	{'precision': 0.8148820326678766, 'recall': 0.8431924882629108, 'f1': 0.8287955699123213, 'number': 1065}	0.7495	0.8003	0.7741	0.8050
0.0179	25.0	250	1.0955	{'precision': 0.7149270482603816, 'recall': 0.7873918417799752, 'f1': 0.7494117647058823, 'number': 809}	{'precision': 0.4057971014492754, 'recall': 0.47058823529411764, 'f1': 0.43579766536964987, 'number': 119}	{'precision': 0.8115942028985508, 'recall': 0.8413145539906103, 'f1': 0.826187183033656, 'number': 1065}	0.7450	0.7973	0.7702	0.8075
0.0181	26.0	260	1.0775	{'precision': 0.7172949002217295, 'recall': 0.799752781211372, 'f1': 0.7562828755113968, 'number': 809}	{'precision': 0.4088050314465409, 'recall': 0.5462184873949579, 'f1': 0.4676258992805755, 'number': 119}	{'precision': 0.8122151321786691, 'recall': 0.8366197183098592, 'f1': 0.8242368177613321, 'number': 1065}	0.7428	0.8043	0.7723	0.8103
0.0169	27.0	270	1.0667	{'precision': 0.7176339285714286, 'recall': 0.7948084054388134, 'f1': 0.7542521994134898, 'number': 809}	{'precision': 0.41721854304635764, 'recall': 0.5294117647058824, 'f1': 0.4666666666666667, 'number': 119}	{'precision': 0.821656050955414, 'recall': 0.847887323943662, 'f1': 0.8345656192236599, 'number': 1065}	0.7498	0.8073	0.7775	0.8115
0.0164	28.0	280	1.0798	{'precision': 0.7106382978723405, 'recall': 0.8257107540173053, 'f1': 0.7638650657518582, 'number': 809}	{'precision': 0.42567567567567566, 'recall': 0.5294117647058824, 'f1': 0.47191011235955055, 'number': 119}	{'precision': 0.8265682656826568, 'recall': 0.8413145539906103, 'f1': 0.8338762214983713, 'number': 1065}	0.7491	0.8164	0.7813	0.8152
0.0178	29.0	290	1.0944	{'precision': 0.7214611872146118, 'recall': 0.7812113720642769, 'f1': 0.7501483679525223, 'number': 809}	{'precision': 0.4496124031007752, 'recall': 0.48739495798319327, 'f1': 0.467741935483871, 'number': 119}	{'precision': 0.8191881918819188, 'recall': 0.8338028169014085, 'f1': 0.8264308980921358, 'number': 1065}	0.7554	0.7918	0.7732	0.8136
0.0151	30.0	300	1.0994	{'precision': 0.7141292442497261, 'recall': 0.8059332509270705, 'f1': 0.7572590011614402, 'number': 809}	{'precision': 0.43795620437956206, 'recall': 0.5042016806722689, 'f1': 0.46875, 'number': 119}	{'precision': 0.8211981566820277, 'recall': 0.8366197183098592, 'f1': 0.8288372093023256, 'number': 1065}	0.7508	0.8043	0.7766	0.8151
0.0127	31.0	310	1.1177	{'precision': 0.7144420131291028, 'recall': 0.8071693448702101, 'f1': 0.7579802669762042, 'number': 809}	{'precision': 0.4264705882352941, 'recall': 0.48739495798319327, 'f1': 0.4549019607843137, 'number': 119}	{'precision': 0.82483781278962, 'recall': 0.8356807511737089, 'f1': 0.8302238805970149, 'number': 1065}	0.7520	0.8033	0.7768	0.8136
0.0123	32.0	320	1.1295	{'precision': 0.7280799112097669, 'recall': 0.8108776266996292, 'f1': 0.7672514619883041, 'number': 809}	{'precision': 0.4316546762589928, 'recall': 0.5042016806722689, 'f1': 0.46511627906976744, 'number': 119}	{'precision': 0.8176043557168784, 'recall': 0.8460093896713615, 'f1': 0.8315643747115828, 'number': 1065}	0.7549	0.8113	0.7821	0.8127
0.0105	33.0	330	1.1422	{'precision': 0.717439293598234, 'recall': 0.8034610630407911, 'f1': 0.7580174927113702, 'number': 809}	{'precision': 0.427536231884058, 'recall': 0.4957983193277311, 'f1': 0.4591439688715953, 'number': 119}	{'precision': 0.8168498168498168, 'recall': 0.8375586854460094, 'f1': 0.8270746407046824, 'number': 1065}	0.7495	0.8033	0.7755	0.8110
0.0099	34.0	340	1.1476	{'precision': 0.7194323144104804, 'recall': 0.8145859085290482, 'f1': 0.7640579710144928, 'number': 809}	{'precision': 0.43478260869565216, 'recall': 0.5042016806722689, 'f1': 0.4669260700389105, 'number': 119}	{'precision': 0.8256880733944955, 'recall': 0.8450704225352113, 'f1': 0.8352668213457076, 'number': 1065}	0.7551	0.8123	0.7827	0.8132
0.0115	35.0	350	1.1590	{'precision': 0.7200878155872668, 'recall': 0.8108776266996292, 'f1': 0.7627906976744185, 'number': 809}	{'precision': 0.4125874125874126, 'recall': 0.4957983193277311, 'f1': 0.450381679389313, 'number': 119}	{'precision': 0.8325581395348837, 'recall': 0.8403755868544601, 'f1': 0.8364485981308412, 'number': 1065}	0.7562	0.8078	0.7812	0.8129
0.0098	36.0	360	1.1619	{'precision': 0.7271714922048997, 'recall': 0.8071693448702101, 'f1': 0.7650849443468072, 'number': 809}	{'precision': 0.41379310344827586, 'recall': 0.5042016806722689, 'f1': 0.45454545454545453, 'number': 119}	{'precision': 0.8226691042047533, 'recall': 0.8450704225352113, 'f1': 0.8337193144974525, 'number': 1065}	0.7548	0.8093	0.7811	0.8140
0.0089	37.0	370	1.1555	{'precision': 0.7289823008849557, 'recall': 0.8145859085290482, 'f1': 0.7694103911266784, 'number': 809}	{'precision': 0.42857142857142855, 'recall': 0.5042016806722689, 'f1': 0.4633204633204633, 'number': 119}	{'precision': 0.8178571428571428, 'recall': 0.860093896713615, 'f1': 0.8384439359267735, 'number': 1065}	0.7555	0.8204	0.7866	0.8158
0.0116	38.0	380	1.1472	{'precision': 0.7161862527716186, 'recall': 0.7985166872682324, 'f1': 0.7551139684395091, 'number': 809}	{'precision': 0.42962962962962964, 'recall': 0.48739495798319327, 'f1': 0.45669291338582674, 'number': 119}	{'precision': 0.8250460405156538, 'recall': 0.8413145539906103, 'f1': 0.8331008833100882, 'number': 1065}	0.7537	0.8028	0.7775	0.8152
0.0089	39.0	390	1.1558	{'precision': 0.7158590308370044, 'recall': 0.8034610630407911, 'f1': 0.7571345369831101, 'number': 809}	{'precision': 0.41721854304635764, 'recall': 0.5294117647058824, 'f1': 0.4666666666666667, 'number': 119}	{'precision': 0.8302583025830258, 'recall': 0.8450704225352113, 'f1': 0.8375988832014891, 'number': 1065}	0.7527	0.8093	0.7800	0.8120
0.0085	40.0	400	1.1576	{'precision': 0.7169398907103826, 'recall': 0.8108776266996292, 'f1': 0.7610208816705337, 'number': 809}	{'precision': 0.41843971631205673, 'recall': 0.4957983193277311, 'f1': 0.4538461538461538, 'number': 119}	{'precision': 0.8249772105742935, 'recall': 0.8497652582159625, 'f1': 0.8371877890841812, 'number': 1065}	0.7524	0.8128	0.7815	0.8127
0.0079	41.0	410	1.1551	{'precision': 0.716500553709856, 'recall': 0.799752781211372, 'f1': 0.7558411214953271, 'number': 809}	{'precision': 0.44696969696969696, 'recall': 0.4957983193277311, 'f1': 0.47011952191235057, 'number': 119}	{'precision': 0.8264462809917356, 'recall': 0.8450704225352113, 'f1': 0.8356545961002786, 'number': 1065}	0.7561	0.8058	0.7802	0.8145
0.0069	42.0	420	1.1656	{'precision': 0.7169603524229075, 'recall': 0.8046971569839307, 'f1': 0.7582993593476993, 'number': 809}	{'precision': 0.4315068493150685, 'recall': 0.5294117647058824, 'f1': 0.4754716981132075, 'number': 119}	{'precision': 0.8236914600550964, 'recall': 0.8422535211267606, 'f1': 0.8328690807799443, 'number': 1065}	0.7517	0.8083	0.7790	0.8137
0.0067	43.0	430	1.1720	{'precision': 0.7145993413830956, 'recall': 0.8046971569839307, 'f1': 0.7569767441860464, 'number': 809}	{'precision': 0.43661971830985913, 'recall': 0.5210084033613446, 'f1': 0.47509578544061304, 'number': 119}	{'precision': 0.8190909090909091, 'recall': 0.8460093896713615, 'f1': 0.8323325635103926, 'number': 1065}	0.7497	0.8098	0.7786	0.8126
0.0083	44.0	440	1.1720	{'precision': 0.7203579418344519, 'recall': 0.796044499381953, 'f1': 0.756312389900176, 'number': 809}	{'precision': 0.4397163120567376, 'recall': 0.5210084033613446, 'f1': 0.47692307692307695, 'number': 119}	{'precision': 0.8225659690627843, 'recall': 0.8488262910798122, 'f1': 0.8354898336414048, 'number': 1065}	0.7545	0.8078	0.7802	0.8150
0.0072	45.0	450	1.1733	{'precision': 0.727683615819209, 'recall': 0.796044499381953, 'f1': 0.7603305785123966, 'number': 809}	{'precision': 0.4338235294117647, 'recall': 0.4957983193277311, 'f1': 0.4627450980392157, 'number': 119}	{'precision': 0.8209090909090909, 'recall': 0.847887323943662, 'f1': 0.8341801385681292, 'number': 1065}	0.7572	0.8058	0.7807	0.8148
0.0092	46.0	460	1.1712	{'precision': 0.7188888888888889, 'recall': 0.799752781211372, 'f1': 0.7571679344645992, 'number': 809}	{'precision': 0.4306569343065693, 'recall': 0.4957983193277311, 'f1': 0.46093749999999994, 'number': 119}	{'precision': 0.8214285714285714, 'recall': 0.8422535211267606, 'f1': 0.8317107093184979, 'number': 1065}	0.7529	0.8043	0.7778	0.8146
0.0063	47.0	470	1.1723	{'precision': 0.7158590308370044, 'recall': 0.8034610630407911, 'f1': 0.7571345369831101, 'number': 809}	{'precision': 0.4326241134751773, 'recall': 0.5126050420168067, 'f1': 0.4692307692307692, 'number': 119}	{'precision': 0.8212648945921174, 'recall': 0.8413145539906103, 'f1': 0.8311688311688312, 'number': 1065}	0.7509	0.8063	0.7776	0.8154
0.0064	48.0	480	1.1740	{'precision': 0.7166482910694597, 'recall': 0.8034610630407911, 'f1': 0.7575757575757576, 'number': 809}	{'precision': 0.42657342657342656, 'recall': 0.5126050420168067, 'f1': 0.46564885496183206, 'number': 119}	{'precision': 0.8226102941176471, 'recall': 0.8403755868544601, 'f1': 0.8313980492336275, 'number': 1065}	0.7512	0.8058	0.7775	0.8147
0.0069	49.0	490	1.1742	{'precision': 0.7209302325581395, 'recall': 0.8046971569839307, 'f1': 0.7605140186915887, 'number': 809}	{'precision': 0.4246575342465753, 'recall': 0.5210084033613446, 'f1': 0.46792452830188674, 'number': 119}	{'precision': 0.8236380424746076, 'recall': 0.8375586854460094, 'f1': 0.8305400372439479, 'number': 1065}	0.7528	0.8053	0.7782	0.8145
0.0062	50.0	500	1.1740	{'precision': 0.7201327433628318, 'recall': 0.8046971569839307, 'f1': 0.7600700525394046, 'number': 809}	{'precision': 0.4246575342465753, 'recall': 0.5210084033613446, 'f1': 0.46792452830188674, 'number': 119}	{'precision': 0.8236380424746076, 'recall': 0.8375586854460094, 'f1': 0.8305400372439479, 'number': 1065}	0.7525	0.8053	0.7780	0.8146

Framework versions

Transformers 4.23.1
Pytorch 1.12.1
Datasets 2.6.1
Tokenizers 0.13.1

jinhybr/OCR-LM-v1

OCR-LM-v1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jinhybr/
OCR-LM-v1