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Kudod/
roberta-large-ner-ghtk-gam-7-label-new-data-3090-11Sep-1

Name: roberta-large-ner-ghtk-gam-7-label-new-data-3090-11Sep-1
Availability: InStock

safetensors

xlm-roberta

generated_from_trainer

FacebookAI/xlm-roberta-large

finetune

mit

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roberta-large-ner-ghtk-gam-7-label-new-data-3090-11Sep-1

This model is a fine-tuned version of FacebookAI/xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3028
Hiều cao khách hàng: {'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20}
Oại da: {'precision': 0.9047619047619048, 'recall': 0.8260869565217391, 'f1': 0.8636363636363636, 'number': 23}
Àu da: {'precision': 0.78125, 'recall': 0.6578947368421053, 'f1': 0.7142857142857143, 'number': 38}
Áng khuôn mặt: {'precision': 0.7368421052631579, 'recall': 0.875, 'f1': 0.7999999999999999, 'number': 16}
Áng người: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}
Ân nặng khách hàng: {'precision': 0.9, 'recall': 0.8709677419354839, 'f1': 0.8852459016393444, 'number': 31}
Ặc điểm khác của da: {'precision': 0.8620689655172413, 'recall': 0.8928571428571429, 'f1': 0.8771929824561403, 'number': 28}
Overall Precision: 0.8563
Overall Recall: 0.8462
Overall F1: 0.8512
Overall Accuracy: 0.9619

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: 4
eval_batch_size: 4
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	Hiều cao khách hàng	Oại da	Àu da	Áng khuôn mặt	Áng người	Ân nặng khách hàng	Ặc điểm khác của da	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	141	0.2795	{'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20}	{'precision': 0.5161290322580645, 'recall': 0.6956521739130435, 'f1': 0.5925925925925926, 'number': 23}	{'precision': 0.6666666666666666, 'recall': 0.5789473684210527, 'f1': 0.619718309859155, 'number': 38}	{'precision': 0.625, 'recall': 0.625, 'f1': 0.625, 'number': 16}	{'precision': 0.6666666666666666, 'recall': 0.7692307692307693, 'f1': 0.7142857142857142, 'number': 13}	{'precision': 0.8888888888888888, 'recall': 0.7741935483870968, 'f1': 0.8275862068965517, 'number': 31}	{'precision': 0.5161290322580645, 'recall': 0.5714285714285714, 'f1': 0.5423728813559322, 'number': 28}	0.6744	0.6864	0.6804	0.9196
No log	2.0	282	0.2887	{'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20}	{'precision': 0.7727272727272727, 'recall': 0.7391304347826086, 'f1': 0.7555555555555555, 'number': 23}	{'precision': 0.6363636363636364, 'recall': 0.5526315789473685, 'f1': 0.5915492957746479, 'number': 38}	{'precision': 0.6, 'recall': 0.75, 'f1': 0.6666666666666665, 'number': 16}	{'precision': 0.7058823529411765, 'recall': 0.9230769230769231, 'f1': 0.8000000000000002, 'number': 13}	{'precision': 0.90625, 'recall': 0.9354838709677419, 'f1': 0.9206349206349206, 'number': 31}	{'precision': 0.6571428571428571, 'recall': 0.8214285714285714, 'f1': 0.73015873015873, 'number': 28}	0.7363	0.7929	0.7635	0.9262
No log	3.0	423	0.2731	{'precision': 0.9523809523809523, 'recall': 1.0, 'f1': 0.975609756097561, 'number': 20}	{'precision': 0.8095238095238095, 'recall': 0.7391304347826086, 'f1': 0.7727272727272727, 'number': 23}	{'precision': 0.7419354838709677, 'recall': 0.6052631578947368, 'f1': 0.6666666666666666, 'number': 38}	{'precision': 0.7368421052631579, 'recall': 0.875, 'f1': 0.7999999999999999, 'number': 16}	{'precision': 0.8571428571428571, 'recall': 0.9230769230769231, 'f1': 0.888888888888889, 'number': 13}	{'precision': 0.9655172413793104, 'recall': 0.9032258064516129, 'f1': 0.9333333333333333, 'number': 31}	{'precision': 0.5121951219512195, 'recall': 0.75, 'f1': 0.6086956521739131, 'number': 28}	0.7670	0.7988	0.7826	0.9328
0.4116	4.0	564	0.2516	{'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20}	{'precision': 0.8636363636363636, 'recall': 0.8260869565217391, 'f1': 0.8444444444444444, 'number': 23}	{'precision': 0.7241379310344828, 'recall': 0.5526315789473685, 'f1': 0.6268656716417911, 'number': 38}	{'precision': 0.7777777777777778, 'recall': 0.875, 'f1': 0.823529411764706, 'number': 16}	{'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13}	{'precision': 0.9354838709677419, 'recall': 0.9354838709677419, 'f1': 0.9354838709677419, 'number': 31}	{'precision': 0.78125, 'recall': 0.8928571428571429, 'f1': 0.8333333333333334, 'number': 28}	0.8333	0.8284	0.8309	0.9502
0.4116	5.0	705	0.2274	{'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20}	{'precision': 0.8181818181818182, 'recall': 0.782608695652174, 'f1': 0.8, 'number': 23}	{'precision': 0.8064516129032258, 'recall': 0.6578947368421053, 'f1': 0.7246376811594202, 'number': 38}	{'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}	{'precision': 0.9285714285714286, 'recall': 0.8387096774193549, 'f1': 0.8813559322033899, 'number': 31}	{'precision': 0.8275862068965517, 'recall': 0.8571428571428571, 'f1': 0.8421052631578947, 'number': 28}	0.8528	0.8225	0.8373	0.9594
0.4116	6.0	846	0.2420	{'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 20}	{'precision': 0.6666666666666666, 'recall': 0.6956521739130435, 'f1': 0.6808510638297872, 'number': 23}	{'precision': 0.7878787878787878, 'recall': 0.6842105263157895, 'f1': 0.732394366197183, 'number': 38}	{'precision': 0.8235294117647058, 'recall': 0.875, 'f1': 0.8484848484848485, 'number': 16}	{'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13}	{'precision': 0.9354838709677419, 'recall': 0.9354838709677419, 'f1': 0.9354838709677419, 'number': 31}	{'precision': 0.7666666666666667, 'recall': 0.8214285714285714, 'f1': 0.793103448275862, 'number': 28}	0.8140	0.8284	0.8211	0.9527
0.4116	7.0	987	0.2390	{'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20}	{'precision': 0.8636363636363636, 'recall': 0.8260869565217391, 'f1': 0.8444444444444444, 'number': 23}	{'precision': 0.7419354838709677, 'recall': 0.6052631578947368, 'f1': 0.6666666666666666, 'number': 38}	{'precision': 0.8421052631578947, 'recall': 1.0, 'f1': 0.9142857142857143, 'number': 16}	{'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13}	{'precision': 0.9032258064516129, 'recall': 0.9032258064516129, 'f1': 0.9032258064516129, 'number': 31}	{'precision': 0.896551724137931, 'recall': 0.9285714285714286, 'f1': 0.912280701754386, 'number': 28}	0.8571	0.8521	0.8546	0.9619
0.1291	8.0	1128	0.2731	{'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20}	{'precision': 0.9047619047619048, 'recall': 0.8260869565217391, 'f1': 0.8636363636363636, 'number': 23}	{'precision': 0.75, 'recall': 0.631578947368421, 'f1': 0.6857142857142857, 'number': 38}	{'precision': 0.7894736842105263, 'recall': 0.9375, 'f1': 0.8571428571428572, 'number': 16}	{'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13}	{'precision': 0.9333333333333333, 'recall': 0.9032258064516129, 'f1': 0.9180327868852459, 'number': 31}	{'precision': 0.896551724137931, 'recall': 0.9285714285714286, 'f1': 0.912280701754386, 'number': 28}	0.8623	0.8521	0.8571	0.9602
0.1291	9.0	1269	0.2968	{'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20}	{'precision': 0.9047619047619048, 'recall': 0.8260869565217391, 'f1': 0.8636363636363636, 'number': 23}	{'precision': 0.7741935483870968, 'recall': 0.631578947368421, 'f1': 0.6956521739130435, 'number': 38}	{'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16}	{'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13}	{'precision': 0.9, 'recall': 0.8709677419354839, 'f1': 0.8852459016393444, 'number': 31}	{'precision': 0.8620689655172413, 'recall': 0.8928571428571429, 'f1': 0.8771929824561403, 'number': 28}	0.8537	0.8284	0.8408	0.9610
0.1291	10.0	1410	0.3028	{'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20}	{'precision': 0.9047619047619048, 'recall': 0.8260869565217391, 'f1': 0.8636363636363636, 'number': 23}	{'precision': 0.78125, 'recall': 0.6578947368421053, 'f1': 0.7142857142857143, 'number': 38}	{'precision': 0.7368421052631579, 'recall': 0.875, 'f1': 0.7999999999999999, 'number': 16}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}	{'precision': 0.9, 'recall': 0.8709677419354839, 'f1': 0.8852459016393444, 'number': 31}	{'precision': 0.8620689655172413, 'recall': 0.8928571428571429, 'f1': 0.8771929824561403, 'number': 28}	0.8563	0.8462	0.8512	0.9619

Framework versions

Transformers 4.44.0
Pytorch 2.3.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Kudod/roberta-large-ner-ghtk-gam-7-label-new-data-3090-11Sep-1

roberta-large-ner-ghtk-gam-7-label-new-data-3090-11Sep-1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Kudod/
roberta-large-ner-ghtk-gam-7-label-new-data-3090-11Sep-1