roberta-large-ner-ghtk-cs-add-45abel-5-new-data-3090-24Sep-1

Kudod/roberta-large-ner-ghtk-cs-add-45abel-5-new-data-3090-24Sep-1

roberta-large-ner-ghtk-cs-add-45abel-5-new-data-3090-24Sep-1 is a machine learning model from Kudod. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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

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At a glance

Model type: xlm-roberta
Access: Public
Created: Sep 24, 2024
Updated: Sep 24, 2024
SHA: 504105e8

Model card

From the Hugging Face model README

roberta-large-ner-ghtk-cs-add-45abel-5-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: 0.3238
Tk: {'precision': 0.8181818181818182, 'recall': 0.7758620689655172, 'f1': 0.7964601769911505, 'number': 116}
A: {'precision': 0.9177777777777778, 'recall': 0.9582366589327146, 'f1': 0.9375709421112373, 'number': 431}
Gày: {'precision': 0.7272727272727273, 'recall': 0.9411764705882353, 'f1': 0.8205128205128205, 'number': 34}
Gày trừu tượng: {'precision': 0.9018789144050104, 'recall': 0.8852459016393442, 'f1': 0.8934850051706309, 'number': 488}
Iền: {'precision': 0.7254901960784313, 'recall': 0.9487179487179487, 'f1': 0.8222222222222223, 'number': 39}
Iờ: {'precision': 0.5769230769230769, 'recall': 0.7894736842105263, 'f1': 0.6666666666666666, 'number': 38}
Ã đơn: {'precision': 0.8046511627906977, 'recall': 0.8522167487684729, 'f1': 0.8277511961722489, 'number': 203}
Đt: {'precision': 0.9213602550478215, 'recall': 0.9874715261958997, 'f1': 0.9532710280373832, 'number': 878}
Đt trừu tượng: {'precision': 0.8290598290598291, 'recall': 0.8326180257510729, 'f1': 0.8308351177730192, 'number': 233}
Ịa chỉ cụ thể: {'precision': 0.44642857142857145, 'recall': 0.5813953488372093, 'f1': 0.5050505050505051, 'number': 43}
Ịa chỉ trừu tượng: {'precision': 0.6578947368421053, 'recall': 0.6578947368421053, 'f1': 0.6578947368421053, 'number': 76}
Overall Precision: 0.8652
Overall Recall: 0.9085
Overall F1: 0.8863
Overall Accuracy: 0.9491

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	Ịa chỉ trừu tượng	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	330	0.2346	{'precision': 0.7291666666666666, 'recall': 0.3017241379310345, 'f1': 0.42682926829268286, 'number': 116}	{'precision': 0.9172113289760349, 'recall': 0.9767981438515081, 'f1': 0.946067415730337, 'number': 431}	{'precision': 0.6875, 'recall': 0.9705882352941176, 'f1': 0.8048780487804877, 'number': 34}	{'precision': 0.8767676767676768, 'recall': 0.889344262295082, 'f1': 0.8830111902339778, 'number': 488}	{'precision': 0.7115384615384616, 'recall': 0.9487179487179487, 'f1': 0.8131868131868132, 'number': 39}	{'precision': 0.5441176470588235, 'recall': 0.9736842105263158, 'f1': 0.6981132075471699, 'number': 38}	{'precision': 0.8021390374331551, 'recall': 0.7389162561576355, 'f1': 0.7692307692307693, 'number': 203}	{'precision': 0.8747474747474747, 'recall': 0.9863325740318907, 'f1': 0.9271948608137046, 'number': 878}	{'precision': 0.90625, 'recall': 0.6223175965665236, 'f1': 0.7379134860050891, 'number': 233}	{'precision': 0.4126984126984127, 'recall': 0.6046511627906976, 'f1': 0.49056603773584895, 'number': 43}	{'precision': 0.5955056179775281, 'recall': 0.6973684210526315, 'f1': 0.6424242424242425, 'number': 76}	0.8413	0.8674	0.8541	0.9390
0.1722	2.0	660	0.2056	{'precision': 0.8309859154929577, 'recall': 0.5086206896551724, 'f1': 0.6310160427807486, 'number': 116}	{'precision': 0.9002123142250531, 'recall': 0.9837587006960556, 'f1': 0.9401330376940134, 'number': 431}	{'precision': 0.6904761904761905, 'recall': 0.8529411764705882, 'f1': 0.7631578947368423, 'number': 34}	{'precision': 0.8959183673469387, 'recall': 0.8995901639344263, 'f1': 0.8977505112474438, 'number': 488}	{'precision': 0.7037037037037037, 'recall': 0.9743589743589743, 'f1': 0.8172043010752689, 'number': 39}	{'precision': 0.8636363636363636, 'recall': 0.5, 'f1': 0.6333333333333333, 'number': 38}	{'precision': 0.8689655172413793, 'recall': 0.6206896551724138, 'f1': 0.7241379310344829, 'number': 203}	{'precision': 0.9487179487179487, 'recall': 0.969248291571754, 'f1': 0.9588732394366197, 'number': 878}	{'precision': 0.797752808988764, 'recall': 0.9141630901287554, 'f1': 0.852, 'number': 233}	{'precision': 0.40816326530612246, 'recall': 0.46511627906976744, 'f1': 0.43478260869565216, 'number': 43}	{'precision': 0.6891891891891891, 'recall': 0.6710526315789473, 'f1': 0.68, 'number': 76}	0.8788	0.8798	0.8793	0.9451
0.1722	3.0	990	0.3335	{'precision': 0.7962962962962963, 'recall': 0.3706896551724138, 'f1': 0.5058823529411766, 'number': 116}	{'precision': 0.9186813186813186, 'recall': 0.9698375870069605, 'f1': 0.943566591422122, 'number': 431}	{'precision': 0.6666666666666666, 'recall': 0.9411764705882353, 'f1': 0.7804878048780487, 'number': 34}	{'precision': 0.8875255623721882, 'recall': 0.889344262295082, 'f1': 0.888433981576254, 'number': 488}	{'precision': 0.6607142857142857, 'recall': 0.9487179487179487, 'f1': 0.7789473684210526, 'number': 39}	{'precision': 0.603448275862069, 'recall': 0.9210526315789473, 'f1': 0.7291666666666666, 'number': 38}	{'precision': 0.9139072847682119, 'recall': 0.6798029556650246, 'f1': 0.7796610169491525, 'number': 203}	{'precision': 0.9830028328611898, 'recall': 0.3952164009111617, 'f1': 0.5637692932575142, 'number': 878}	{'precision': 0.7816091954022989, 'recall': 0.8755364806866953, 'f1': 0.8259109311740892, 'number': 233}	{'precision': 0.29577464788732394, 'recall': 0.4883720930232558, 'f1': 0.3684210526315789, 'number': 43}	{'precision': 0.5196078431372549, 'recall': 0.6973684210526315, 'f1': 0.5955056179775281, 'number': 76}	0.8398	0.6832	0.7535	0.9119
0.0913	4.0	1320	0.2125	{'precision': 0.7931034482758621, 'recall': 0.5948275862068966, 'f1': 0.6798029556650247, 'number': 116}	{'precision': 0.9120879120879121, 'recall': 0.962877030162413, 'f1': 0.9367945823927765, 'number': 431}	{'precision': 0.8076923076923077, 'recall': 0.6176470588235294, 'f1': 0.7, 'number': 34}	{'precision': 0.8701550387596899, 'recall': 0.9200819672131147, 'f1': 0.894422310756972, 'number': 488}	{'precision': 0.6909090909090909, 'recall': 0.9743589743589743, 'f1': 0.8085106382978723, 'number': 39}	{'precision': 0.5238095238095238, 'recall': 0.868421052631579, 'f1': 0.6534653465346535, 'number': 38}	{'precision': 0.8274111675126904, 'recall': 0.8029556650246306, 'f1': 0.8150000000000001, 'number': 203}	{'precision': 0.9080459770114943, 'recall': 0.989749430523918, 'f1': 0.9471389645776567, 'number': 878}	{'precision': 0.7835820895522388, 'recall': 0.9012875536480687, 'f1': 0.8383233532934131, 'number': 233}	{'precision': 0.5652173913043478, 'recall': 0.6046511627906976, 'f1': 0.5842696629213482, 'number': 43}	{'precision': 0.6025641025641025, 'recall': 0.618421052631579, 'f1': 0.6103896103896105, 'number': 76}	0.8515	0.9073	0.8785	0.9474
0.0539	5.0	1650	0.2860	{'precision': 0.7701149425287356, 'recall': 0.5775862068965517, 'f1': 0.6600985221674877, 'number': 116}	{'precision': 0.9080962800875274, 'recall': 0.962877030162413, 'f1': 0.9346846846846847, 'number': 431}	{'precision': 0.7380952380952381, 'recall': 0.9117647058823529, 'f1': 0.8157894736842106, 'number': 34}	{'precision': 0.8958333333333334, 'recall': 0.8811475409836066, 'f1': 0.8884297520661157, 'number': 488}	{'precision': 0.76, 'recall': 0.9743589743589743, 'f1': 0.853932584269663, 'number': 39}	{'precision': 0.5573770491803278, 'recall': 0.8947368421052632, 'f1': 0.686868686868687, 'number': 38}	{'precision': 0.6879699248120301, 'recall': 0.9014778325123153, 'f1': 0.7803837953091686, 'number': 203}	{'precision': 0.8871951219512195, 'recall': 0.9943052391799544, 'f1': 0.937701396348013, 'number': 878}	{'precision': 0.7667844522968198, 'recall': 0.9313304721030042, 'f1': 0.8410852713178294, 'number': 233}	{'precision': 0.46, 'recall': 0.5348837209302325, 'f1': 0.4946236559139785, 'number': 43}	{'precision': 0.6756756756756757, 'recall': 0.6578947368421053, 'f1': 0.6666666666666667, 'number': 76}	0.8331	0.9155	0.8723	0.9414
0.0539	6.0	1980	0.2655	{'precision': 0.7757009345794392, 'recall': 0.7155172413793104, 'f1': 0.7443946188340808, 'number': 116}	{'precision': 0.9196428571428571, 'recall': 0.9559164733178654, 'f1': 0.937428896473265, 'number': 431}	{'precision': 0.7560975609756098, 'recall': 0.9117647058823529, 'f1': 0.8266666666666665, 'number': 34}	{'precision': 0.8917835671342685, 'recall': 0.9118852459016393, 'f1': 0.9017223910840931, 'number': 488}	{'precision': 0.7115384615384616, 'recall': 0.9487179487179487, 'f1': 0.8131868131868132, 'number': 39}	{'precision': 0.5869565217391305, 'recall': 0.7105263157894737, 'f1': 0.6428571428571428, 'number': 38}	{'precision': 0.7857142857142857, 'recall': 0.8669950738916257, 'f1': 0.8243559718969555, 'number': 203}	{'precision': 0.9034267912772586, 'recall': 0.9908883826879271, 'f1': 0.9451385116784358, 'number': 878}	{'precision': 0.7338129496402878, 'recall': 0.8755364806866953, 'f1': 0.7984344422700587, 'number': 233}	{'precision': 0.40350877192982454, 'recall': 0.5348837209302325, 'f1': 0.4599999999999999, 'number': 43}	{'precision': 0.654320987654321, 'recall': 0.6973684210526315, 'f1': 0.6751592356687897, 'number': 76}	0.8444	0.9155	0.8785	0.9461
0.0309	7.0	2310	0.2935	{'precision': 0.8, 'recall': 0.7241379310344828, 'f1': 0.7601809954751131, 'number': 116}	{'precision': 0.917960088691796, 'recall': 0.9605568445475638, 'f1': 0.9387755102040817, 'number': 431}	{'precision': 0.7111111111111111, 'recall': 0.9411764705882353, 'f1': 0.8101265822784811, 'number': 34}	{'precision': 0.9, 'recall': 0.8852459016393442, 'f1': 0.8925619834710743, 'number': 488}	{'precision': 0.7346938775510204, 'recall': 0.9230769230769231, 'f1': 0.8181818181818182, 'number': 39}	{'precision': 0.5272727272727272, 'recall': 0.7631578947368421, 'f1': 0.6236559139784946, 'number': 38}	{'precision': 0.7853881278538812, 'recall': 0.8472906403940886, 'f1': 0.815165876777251, 'number': 203}	{'precision': 0.914466737064414, 'recall': 0.9863325740318907, 'f1': 0.949041095890411, 'number': 878}	{'precision': 0.8174273858921162, 'recall': 0.8454935622317596, 'f1': 0.8312236286919831, 'number': 233}	{'precision': 0.48214285714285715, 'recall': 0.627906976744186, 'f1': 0.5454545454545454, 'number': 43}	{'precision': 0.7066666666666667, 'recall': 0.6973684210526315, 'f1': 0.7019867549668874, 'number': 76}	0.8601	0.9081	0.8834	0.9482
0.0166	8.0	2640	0.3131	{'precision': 0.83, 'recall': 0.7155172413793104, 'f1': 0.7685185185185185, 'number': 116}	{'precision': 0.9161147902869757, 'recall': 0.962877030162413, 'f1': 0.9389140271493213, 'number': 431}	{'precision': 0.625, 'recall': 0.8823529411764706, 'f1': 0.7317073170731708, 'number': 34}	{'precision': 0.9, 'recall': 0.8852459016393442, 'f1': 0.8925619834710743, 'number': 488}	{'precision': 0.6727272727272727, 'recall': 0.9487179487179487, 'f1': 0.7872340425531915, 'number': 39}	{'precision': 0.5555555555555556, 'recall': 0.7894736842105263, 'f1': 0.6521739130434783, 'number': 38}	{'precision': 0.8316831683168316, 'recall': 0.8275862068965517, 'f1': 0.8296296296296295, 'number': 203}	{'precision': 0.9239013933547695, 'recall': 0.9817767653758542, 'f1': 0.9519602429596907, 'number': 878}	{'precision': 0.812, 'recall': 0.871244635193133, 'f1': 0.8405797101449276, 'number': 233}	{'precision': 0.5, 'recall': 0.627906976744186, 'f1': 0.5567010309278351, 'number': 43}	{'precision': 0.627906976744186, 'recall': 0.7105263157894737, 'f1': 0.6666666666666666, 'number': 76}	0.8622	0.9077	0.8844	0.9464
0.0166	9.0	2970	0.3098	{'precision': 0.8181818181818182, 'recall': 0.7758620689655172, 'f1': 0.7964601769911505, 'number': 116}	{'precision': 0.9203539823008849, 'recall': 0.9651972157772621, 'f1': 0.9422423556058889, 'number': 431}	{'precision': 0.7441860465116279, 'recall': 0.9411764705882353, 'f1': 0.8311688311688312, 'number': 34}	{'precision': 0.9026915113871635, 'recall': 0.8934426229508197, 'f1': 0.898043254376931, 'number': 488}	{'precision': 0.74, 'recall': 0.9487179487179487, 'f1': 0.8314606741573033, 'number': 39}	{'precision': 0.5370370370370371, 'recall': 0.7631578947368421, 'f1': 0.6304347826086957, 'number': 38}	{'precision': 0.812206572769953, 'recall': 0.8522167487684729, 'f1': 0.8317307692307692, 'number': 203}	{'precision': 0.921505376344086, 'recall': 0.9760820045558086, 'f1': 0.9480088495575222, 'number': 878}	{'precision': 0.819327731092437, 'recall': 0.8369098712446352, 'f1': 0.8280254777070063, 'number': 233}	{'precision': 0.49056603773584906, 'recall': 0.6046511627906976, 'f1': 0.5416666666666665, 'number': 43}	{'precision': 0.6455696202531646, 'recall': 0.6710526315789473, 'f1': 0.6580645161290323, 'number': 76}	0.8658	0.9081	0.8864	0.9494
0.0094	10.0	3300	0.3238	{'precision': 0.8181818181818182, 'recall': 0.7758620689655172, 'f1': 0.7964601769911505, 'number': 116}	{'precision': 0.9177777777777778, 'recall': 0.9582366589327146, 'f1': 0.9375709421112373, 'number': 431}	{'precision': 0.7272727272727273, 'recall': 0.9411764705882353, 'f1': 0.8205128205128205, 'number': 34}	{'precision': 0.9018789144050104, 'recall': 0.8852459016393442, 'f1': 0.8934850051706309, 'number': 488}	{'precision': 0.7254901960784313, 'recall': 0.9487179487179487, 'f1': 0.8222222222222223, 'number': 39}	{'precision': 0.5769230769230769, 'recall': 0.7894736842105263, 'f1': 0.6666666666666666, 'number': 38}	{'precision': 0.8046511627906977, 'recall': 0.8522167487684729, 'f1': 0.8277511961722489, 'number': 203}	{'precision': 0.9213602550478215, 'recall': 0.9874715261958997, 'f1': 0.9532710280373832, 'number': 878}	{'precision': 0.8290598290598291, 'recall': 0.8326180257510729, 'f1': 0.8308351177730192, 'number': 233}	{'precision': 0.44642857142857145, 'recall': 0.5813953488372093, 'f1': 0.5050505050505051, 'number': 43}	{'precision': 0.6578947368421053, 'recall': 0.6578947368421053, 'f1': 0.6578947368421053, 'number': 76}	0.8652	0.9085	0.8863	0.9491

Framework versions

Transformers 4.44.0
Pytorch 2.3.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Type: xlm-roberta
Created: Sep 24, 2024
Updated: Sep 24, 2024