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Kudod/
roberta-large-ner-ghtk-cs-add-4label-10-new-data-3090-24Sep-1

Name: roberta-large-ner-ghtk-cs-add-4label-10-new-data-3090-24Sep-1
Availability: InStock

safetensors

xlm-roberta

generated_from_trainer

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roberta-large-ner-ghtk-cs-add-4label-10-new-data-3090-24Sep-1

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2897
Tk: {'precision': 0.8068181818181818, 'recall': 0.6120689655172413, 'f1': 0.6960784313725489, 'number': 116}
A: {'precision': 0.915929203539823, 'recall': 0.9605568445475638, 'f1': 0.9377123442808607, 'number': 431}
Gày: {'precision': 0.7441860465116279, 'recall': 0.9411764705882353, 'f1': 0.8311688311688312, 'number': 34}
Gày trừu tượng: {'precision': 0.9053497942386831, 'recall': 0.9016393442622951, 'f1': 0.9034907597535935, 'number': 488}
Iền: {'precision': 0.6923076923076923, 'recall': 0.9230769230769231, 'f1': 0.7912087912087913, 'number': 39}
Iờ: {'precision': 0.6666666666666666, 'recall': 0.7368421052631579, 'f1': 0.7, 'number': 38}
Ã đơn: {'precision': 0.8415841584158416, 'recall': 0.8374384236453202, 'f1': 0.8395061728395061, 'number': 203}
Đt: {'precision': 0.9193548387096774, 'recall': 0.9738041002277904, 'f1': 0.945796460176991, 'number': 878}
Đt trừu tượng: {'precision': 0.8223140495867769, 'recall': 0.8540772532188842, 'f1': 0.8378947368421052, 'number': 233}
Ịa chỉ cụ thể: {'precision': 0.4583333333333333, 'recall': 0.5116279069767442, 'f1': 0.4835164835164835, 'number': 43}
Overall Precision: 0.8770
Overall Recall: 0.9057
Overall F1: 0.8911
Overall Accuracy: 0.9514

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: 2.5e-05
train_batch_size: 8
eval_batch_size: 8
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	Tk	A	Gày	Gày trừu tượng	Iền	Iờ	Ã đơn	Đt	Đt trừu tượng	Ịa chỉ cụ thể	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	311	0.1810	{'precision': 0.7203389830508474, 'recall': 0.7327586206896551, 'f1': 0.7264957264957266, 'number': 116}	{'precision': 0.912472647702407, 'recall': 0.9675174013921114, 'f1': 0.9391891891891891, 'number': 431}	{'precision': 0.7619047619047619, 'recall': 0.9411764705882353, 'f1': 0.8421052631578947, 'number': 34}	{'precision': 0.9051546391752577, 'recall': 0.8995901639344263, 'f1': 0.9023638232271325, 'number': 488}	{'precision': 0.8043478260869565, 'recall': 0.9487179487179487, 'f1': 0.8705882352941177, 'number': 39}	{'precision': 0.8, 'recall': 0.42105263157894735, 'f1': 0.5517241379310345, 'number': 38}	{'precision': 0.8541666666666666, 'recall': 0.8078817733990148, 'f1': 0.830379746835443, 'number': 203}	{'precision': 0.8981481481481481, 'recall': 0.9943052391799544, 'f1': 0.9437837837837837, 'number': 878}	{'precision': 0.8803827751196173, 'recall': 0.7896995708154506, 'f1': 0.8325791855203619, 'number': 233}	{'precision': 0.36, 'recall': 0.20930232558139536, 'f1': 0.2647058823529412, 'number': 43}	0.8792	0.9013	0.8901	0.9384
0.1517	2.0	622	0.1508	{'precision': 0.7798165137614679, 'recall': 0.7327586206896551, 'f1': 0.7555555555555554, 'number': 116}	{'precision': 0.8924050632911392, 'recall': 0.9814385150812065, 'f1': 0.9348066298342541, 'number': 431}	{'precision': 0.6470588235294118, 'recall': 0.9705882352941176, 'f1': 0.7764705882352942, 'number': 34}	{'precision': 0.87109375, 'recall': 0.9139344262295082, 'f1': 0.892, 'number': 488}	{'precision': 0.6862745098039216, 'recall': 0.8974358974358975, 'f1': 0.7777777777777778, 'number': 39}	{'precision': 0.546875, 'recall': 0.9210526315789473, 'f1': 0.6862745098039216, 'number': 38}	{'precision': 0.8676470588235294, 'recall': 0.8719211822660099, 'f1': 0.8697788697788699, 'number': 203}	{'precision': 0.9362637362637363, 'recall': 0.9703872437357631, 'f1': 0.953020134228188, 'number': 878}	{'precision': 0.8235294117647058, 'recall': 0.9012875536480687, 'f1': 0.8606557377049181, 'number': 233}	{'precision': 0.3090909090909091, 'recall': 0.3953488372093023, 'f1': 0.346938775510204, 'number': 43}	0.8615	0.9241	0.8917	0.9542
0.1517	3.0	933	0.1774	{'precision': 0.8214285714285714, 'recall': 0.39655172413793105, 'f1': 0.5348837209302326, 'number': 116}	{'precision': 0.931350114416476, 'recall': 0.9443155452436195, 'f1': 0.9377880184331796, 'number': 431}	{'precision': 0.7045454545454546, 'recall': 0.9117647058823529, 'f1': 0.794871794871795, 'number': 34}	{'precision': 0.8837675350701403, 'recall': 0.9036885245901639, 'f1': 0.8936170212765957, 'number': 488}	{'precision': 0.7708333333333334, 'recall': 0.9487179487179487, 'f1': 0.8505747126436781, 'number': 39}	{'precision': 0.6041666666666666, 'recall': 0.7631578947368421, 'f1': 0.6744186046511628, 'number': 38}	{'precision': 0.8638743455497382, 'recall': 0.812807881773399, 'f1': 0.8375634517766498, 'number': 203}	{'precision': 0.8615847542627884, 'recall': 0.9783599088838268, 'f1': 0.9162666666666667, 'number': 878}	{'precision': 0.7992277992277992, 'recall': 0.8884120171673819, 'f1': 0.8414634146341463, 'number': 233}	{'precision': 0.5098039215686274, 'recall': 0.6046511627906976, 'f1': 0.553191489361702, 'number': 43}	0.8548	0.8981	0.8759	0.9500
0.0768	4.0	1244	0.1910	{'precision': 0.8155339805825242, 'recall': 0.7241379310344828, 'f1': 0.7671232876712328, 'number': 116}	{'precision': 0.9230769230769231, 'recall': 0.974477958236659, 'f1': 0.9480812641083521, 'number': 431}	{'precision': 0.7441860465116279, 'recall': 0.9411764705882353, 'f1': 0.8311688311688312, 'number': 34}	{'precision': 0.8933601609657947, 'recall': 0.9098360655737705, 'f1': 0.9015228426395939, 'number': 488}	{'precision': 0.7254901960784313, 'recall': 0.9487179487179487, 'f1': 0.8222222222222223, 'number': 39}	{'precision': 0.5737704918032787, 'recall': 0.9210526315789473, 'f1': 0.7070707070707071, 'number': 38}	{'precision': 0.8776595744680851, 'recall': 0.812807881773399, 'f1': 0.8439897698209718, 'number': 203}	{'precision': 0.883248730964467, 'recall': 0.9908883826879271, 'f1': 0.9339774557165861, 'number': 878}	{'precision': 0.81640625, 'recall': 0.8969957081545065, 'f1': 0.8548057259713701, 'number': 233}	{'precision': 0.375, 'recall': 0.4883720930232558, 'f1': 0.42424242424242425, 'number': 43}	0.8597	0.9257	0.8915	0.9528
0.0426	5.0	1555	0.2144	{'precision': 0.7659574468085106, 'recall': 0.6206896551724138, 'f1': 0.6857142857142856, 'number': 116}	{'precision': 0.9173913043478261, 'recall': 0.9791183294663574, 'f1': 0.9472502805836139, 'number': 431}	{'precision': 0.775, 'recall': 0.9117647058823529, 'f1': 0.8378378378378379, 'number': 34}	{'precision': 0.8870967741935484, 'recall': 0.9016393442622951, 'f1': 0.8943089430894309, 'number': 488}	{'precision': 0.7551020408163265, 'recall': 0.9487179487179487, 'f1': 0.8409090909090908, 'number': 39}	{'precision': 0.5614035087719298, 'recall': 0.8421052631578947, 'f1': 0.6736842105263158, 'number': 38}	{'precision': 0.8181818181818182, 'recall': 0.8423645320197044, 'f1': 0.8300970873786409, 'number': 203}	{'precision': 0.8894628099173554, 'recall': 0.9806378132118451, 'f1': 0.932827735644637, 'number': 878}	{'precision': 0.7368421052631579, 'recall': 0.9012875536480687, 'f1': 0.8108108108108107, 'number': 233}	{'precision': 0.40425531914893614, 'recall': 0.4418604651162791, 'f1': 0.4222222222222222, 'number': 43}	0.8484	0.9169	0.8813	0.9468
0.0426	6.0	1866	0.2553	{'precision': 0.7848101265822784, 'recall': 0.5344827586206896, 'f1': 0.6358974358974359, 'number': 116}	{'precision': 0.9222222222222223, 'recall': 0.962877030162413, 'f1': 0.94211123723042, 'number': 431}	{'precision': 0.7837837837837838, 'recall': 0.8529411764705882, 'f1': 0.8169014084507041, 'number': 34}	{'precision': 0.8849206349206349, 'recall': 0.9139344262295082, 'f1': 0.8991935483870968, 'number': 488}	{'precision': 0.7169811320754716, 'recall': 0.9743589743589743, 'f1': 0.8260869565217391, 'number': 39}	{'precision': 0.6122448979591837, 'recall': 0.7894736842105263, 'f1': 0.6896551724137931, 'number': 38}	{'precision': 0.9308176100628931, 'recall': 0.729064039408867, 'f1': 0.8176795580110497, 'number': 203}	{'precision': 0.9514672686230248, 'recall': 0.9601366742596811, 'f1': 0.95578231292517, 'number': 878}	{'precision': 0.823076923076923, 'recall': 0.9184549356223176, 'f1': 0.8681541582150101, 'number': 233}	{'precision': 0.47368421052631576, 'recall': 0.627906976744186, 'f1': 0.5399999999999999, 'number': 43}	0.8887	0.8997	0.8942	0.9512
0.0241	7.0	2177	0.2521	{'precision': 0.7802197802197802, 'recall': 0.6120689655172413, 'f1': 0.6859903381642511, 'number': 116}	{'precision': 0.9242761692650334, 'recall': 0.962877030162413, 'f1': 0.9431818181818182, 'number': 431}	{'precision': 0.7619047619047619, 'recall': 0.9411764705882353, 'f1': 0.8421052631578947, 'number': 34}	{'precision': 0.9038854805725971, 'recall': 0.9057377049180327, 'f1': 0.9048106448311157, 'number': 488}	{'precision': 0.7254901960784313, 'recall': 0.9487179487179487, 'f1': 0.8222222222222223, 'number': 39}	{'precision': 0.7, 'recall': 0.7368421052631579, 'f1': 0.717948717948718, 'number': 38}	{'precision': 0.8246445497630331, 'recall': 0.8571428571428571, 'f1': 0.8405797101449276, 'number': 203}	{'precision': 0.9068783068783068, 'recall': 0.9760820045558086, 'f1': 0.9402084476138233, 'number': 878}	{'precision': 0.8178137651821862, 'recall': 0.8669527896995708, 'f1': 0.8416666666666666, 'number': 233}	{'precision': 0.4339622641509434, 'recall': 0.5348837209302325, 'f1': 0.47916666666666663, 'number': 43}	0.8713	0.9113	0.8908	0.9516
0.0241	8.0	2488	0.2680	{'precision': 0.7634408602150538, 'recall': 0.6120689655172413, 'f1': 0.6794258373205742, 'number': 116}	{'precision': 0.9300225733634312, 'recall': 0.9559164733178654, 'f1': 0.94279176201373, 'number': 431}	{'precision': 0.7209302325581395, 'recall': 0.9117647058823529, 'f1': 0.8051948051948051, 'number': 34}	{'precision': 0.909853249475891, 'recall': 0.889344262295082, 'f1': 0.899481865284974, 'number': 488}	{'precision': 0.7254901960784313, 'recall': 0.9487179487179487, 'f1': 0.8222222222222223, 'number': 39}	{'precision': 0.6363636363636364, 'recall': 0.9210526315789473, 'f1': 0.7526881720430109, 'number': 38}	{'precision': 0.8382352941176471, 'recall': 0.8423645320197044, 'f1': 0.8402948402948404, 'number': 203}	{'precision': 0.9240780911062907, 'recall': 0.9703872437357631, 'f1': 0.9466666666666667, 'number': 878}	{'precision': 0.8388429752066116, 'recall': 0.871244635193133, 'f1': 0.854736842105263, 'number': 233}	{'precision': 0.5434782608695652, 'recall': 0.5813953488372093, 'f1': 0.5617977528089888, 'number': 43}	0.8816	0.9073	0.8943	0.9515
0.0139	9.0	2799	0.2912	{'precision': 0.8068181818181818, 'recall': 0.6120689655172413, 'f1': 0.6960784313725489, 'number': 116}	{'precision': 0.9256756756756757, 'recall': 0.9535962877030162, 'f1': 0.9394285714285714, 'number': 431}	{'precision': 0.6888888888888889, 'recall': 0.9117647058823529, 'f1': 0.7848101265822784, 'number': 34}	{'precision': 0.9079497907949791, 'recall': 0.889344262295082, 'f1': 0.898550724637681, 'number': 488}	{'precision': 0.6923076923076923, 'recall': 0.9230769230769231, 'f1': 0.7912087912087913, 'number': 39}	{'precision': 0.6521739130434783, 'recall': 0.7894736842105263, 'f1': 0.7142857142857143, 'number': 38}	{'precision': 0.8492462311557789, 'recall': 0.8325123152709359, 'f1': 0.8407960199004975, 'number': 203}	{'precision': 0.9155080213903743, 'recall': 0.9749430523917996, 'f1': 0.9442912300055158, 'number': 878}	{'precision': 0.8257261410788381, 'recall': 0.8540772532188842, 'f1': 0.839662447257384, 'number': 233}	{'precision': 0.47058823529411764, 'recall': 0.5581395348837209, 'f1': 0.5106382978723404, 'number': 43}	0.8767	0.9033	0.8898	0.9498
0.0083	10.0	3110	0.2897	{'precision': 0.8068181818181818, 'recall': 0.6120689655172413, 'f1': 0.6960784313725489, 'number': 116}	{'precision': 0.915929203539823, 'recall': 0.9605568445475638, 'f1': 0.9377123442808607, 'number': 431}	{'precision': 0.7441860465116279, 'recall': 0.9411764705882353, 'f1': 0.8311688311688312, 'number': 34}	{'precision': 0.9053497942386831, 'recall': 0.9016393442622951, 'f1': 0.9034907597535935, 'number': 488}	{'precision': 0.6923076923076923, 'recall': 0.9230769230769231, 'f1': 0.7912087912087913, 'number': 39}	{'precision': 0.6666666666666666, 'recall': 0.7368421052631579, 'f1': 0.7, 'number': 38}	{'precision': 0.8415841584158416, 'recall': 0.8374384236453202, 'f1': 0.8395061728395061, 'number': 203}	{'precision': 0.9193548387096774, 'recall': 0.9738041002277904, 'f1': 0.945796460176991, 'number': 878}	{'precision': 0.8223140495867769, 'recall': 0.8540772532188842, 'f1': 0.8378947368421052, 'number': 233}	{'precision': 0.4583333333333333, 'recall': 0.5116279069767442, 'f1': 0.4835164835164835, 'number': 43}	0.8770	0.9057	0.8911	0.9514

Framework versions

Transformers 4.44.0
Pytorch 2.3.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Kudod/roberta-large-ner-ghtk-cs-add-4label-10-new-data-3090-24Sep-1

roberta-large-ner-ghtk-cs-add-4label-10-new-data-3090-24Sep-1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Kudod/
roberta-large-ner-ghtk-cs-add-4label-10-new-data-3090-24Sep-1