End of training

README.md

@@ -0,0 +1,77 @@
+---
+license: mit
+base_model: FacebookAI/xlm-roberta-large
+tags:
+- generated_from_trainer
+model-index:
+- name: roberta-large-ner-ghtk-gam-7-label-new-data-3090-13Sep-1
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# roberta-large-ner-ghtk-gam-7-label-new-data-3090-13Sep-1
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3314
+- Hiều cao khách hàng: {'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20}
+- Oại da: {'precision': 0.8571428571428571, 'recall': 0.782608695652174, 'f1': 0.8181818181818182, 'number': 23}
+- Àu da: {'precision': 0.7, 'recall': 0.5526315789473685, 'f1': 0.6176470588235295, 'number': 38}
+- Áng khuôn mặt: {'precision': 0.8235294117647058, 'recall': 0.875, 'f1': 0.8484848484848485, 'number': 16}
+- Áng người: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}
+- Ân nặng khách hàng: {'precision': 0.9333333333333333, 'recall': 0.9032258064516129, 'f1': 0.9180327868852459, 'number': 31}
+- Ặc điểm khác của da: {'precision': 0.7666666666666667, 'recall': 0.8214285714285714, 'f1': 0.793103448275862, 'number': 28}
+- Overall Precision: 0.8354
+- Overall Recall: 0.8107
+- Overall F1: 0.8228
+- Overall Accuracy: 0.9519
+
+## 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  | 0.2557          | {'precision': 0.95, 'recall': 0.95, 'f1': 0.9500000000000001, 'number': 20}              | {'precision': 0.7222222222222222, 'recall': 0.5652173913043478, 'f1': 0.6341463414634146, 'number': 23} | {'precision': 0.6785714285714286, 'recall': 0.5, 'f1': 0.5757575757575758, 'number': 38}                | {'precision': 0.8333333333333334, 'recall': 0.625, 'f1': 0.7142857142857143, 'number': 16}  | {'precision': 0.8666666666666667, 'recall': 1.0, 'f1': 0.9285714285714286, 'number': 13} | {'precision': 0.9, 'recall': 0.8709677419354839, 'f1': 0.8852459016393444, 'number': 31}                | {'precision': 0.5142857142857142, 'recall': 0.6428571428571429, 'f1': 0.5714285714285714, 'number': 28} | 0.7532            | 0.7041         | 0.7278     | 0.9254           |
+| No log        | 2.0   | 282  | 0.1904          | {'precision': 0.9090909090909091, 'recall': 1.0, 'f1': 0.9523809523809523, 'number': 20} | {'precision': 0.782608695652174, 'recall': 0.782608695652174, 'f1': 0.782608695652174, 'number': 23}    | {'precision': 0.6333333333333333, 'recall': 0.5, 'f1': 0.5588235294117647, 'number': 38}                | {'precision': 0.8421052631578947, 'recall': 1.0, 'f1': 0.9142857142857143, 'number': 16}    | {'precision': 0.9285714285714286, 'recall': 1.0, 'f1': 0.962962962962963, 'number': 13}  | {'precision': 0.9333333333333333, 'recall': 0.9032258064516129, 'f1': 0.9180327868852459, 'number': 31} | {'precision': 0.6875, 'recall': 0.7857142857142857, 'f1': 0.7333333333333334, 'number': 28}             | 0.8               | 0.8047         | 0.8024     | 0.9436           |
+| No log        | 3.0   | 423  | 0.2762          | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.9047619047619048, 'recall': 0.8260869565217391, 'f1': 0.8636363636363636, 'number': 23} | {'precision': 0.6785714285714286, 'recall': 0.5, 'f1': 0.5757575757575758, 'number': 38}                | {'precision': 0.8, 'recall': 0.75, 'f1': 0.7741935483870969, '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': 0.6666666666666666, 'recall': 0.7857142857142857, 'f1': 0.721311475409836, 'number': 28}  | 0.8217            | 0.7633         | 0.7914     | 0.9428           |
+| 0.4074        | 4.0   | 564  | 0.2128          | {'precision': 0.8571428571428571, 'recall': 0.9, 'f1': 0.8780487804878048, 'number': 20} | {'precision': 0.7391304347826086, 'recall': 0.7391304347826086, 'f1': 0.7391304347826085, 'number': 23} | {'precision': 0.6774193548387096, 'recall': 0.5526315789473685, 'f1': 0.6086956521739131, 'number': 38} | {'precision': 0.8235294117647058, 'recall': 0.875, 'f1': 0.8484848484848485, 'number': 16}  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}                               | {'precision': 0.8787878787878788, 'recall': 0.9354838709677419, 'f1': 0.90625, 'number': 31}            | {'precision': 0.8620689655172413, 'recall': 0.8928571428571429, 'f1': 0.8771929824561403, 'number': 28} | 0.8204            | 0.8107         | 0.8155     | 0.9544           |
+| 0.4074        | 5.0   | 705  | 0.2746          | {'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20} | {'precision': 0.9, 'recall': 0.782608695652174, 'f1': 0.8372093023255814, 'number': 23}                 | {'precision': 0.7, 'recall': 0.5526315789473685, 'f1': 0.6176470588235295, 'number': 38}                | {'precision': 0.7333333333333333, 'recall': 0.6875, 'f1': 0.7096774193548386, 'number': 16} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}                               | {'precision': 0.9333333333333333, 'recall': 0.9032258064516129, 'f1': 0.9180327868852459, 'number': 31} | {'precision': 0.75, 'recall': 0.8571428571428571, 'f1': 0.7999999999999999, 'number': 28}               | 0.8282            | 0.7988         | 0.8133     | 0.9469           |
+| 0.4074        | 6.0   | 846  | 0.2722          | {'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20} | {'precision': 0.8095238095238095, 'recall': 0.7391304347826086, 'f1': 0.7727272727272727, 'number': 23} | {'precision': 0.6774193548387096, 'recall': 0.5526315789473685, 'f1': 0.6086956521739131, 'number': 38} | {'precision': 0.7222222222222222, 'recall': 0.8125, 'f1': 0.7647058823529411, 'number': 16} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}                               | {'precision': 0.9333333333333333, 'recall': 0.9032258064516129, 'f1': 0.9180327868852459, 'number': 31} | {'precision': 0.7333333333333333, 'recall': 0.7857142857142857, 'f1': 0.7586206896551724, 'number': 28} | 0.8072            | 0.7929         | 0.8000     | 0.9494           |
+| 0.4074        | 7.0   | 987  | 0.3018          | {'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20} | {'precision': 0.7727272727272727, 'recall': 0.7391304347826086, 'f1': 0.7555555555555555, 'number': 23} | {'precision': 0.7352941176470589, 'recall': 0.6578947368421053, 'f1': 0.6944444444444445, 'number': 38} | {'precision': 0.8125, 'recall': 0.8125, 'f1': 0.8125, 'number': 16}                         | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}                               | {'precision': 0.9333333333333333, 'recall': 0.9032258064516129, 'f1': 0.9180327868852459, 'number': 31} | {'precision': 0.7666666666666667, 'recall': 0.8214285714285714, 'f1': 0.793103448275862, 'number': 28}  | 0.8274            | 0.8225         | 0.8249     | 0.9502           |
+| 0.0884        | 8.0   | 1128 | 0.3299          | {'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20} | {'precision': 0.8095238095238095, 'recall': 0.7391304347826086, 'f1': 0.7727272727272727, 'number': 23} | {'precision': 0.6774193548387096, 'recall': 0.5526315789473685, 'f1': 0.6086956521739131, 'number': 38} | {'precision': 0.8333333333333334, 'recall': 0.9375, 'f1': 0.8823529411764706, 'number': 16} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}                               | {'precision': 0.9354838709677419, 'recall': 0.9354838709677419, 'f1': 0.9354838709677419, 'number': 31} | {'precision': 0.7931034482758621, 'recall': 0.8214285714285714, 'f1': 0.8070175438596492, 'number': 28} | 0.8313            | 0.8166         | 0.8239     | 0.9511           |
+| 0.0884        | 9.0   | 1269 | 0.3286          | {'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20} | {'precision': 0.8571428571428571, 'recall': 0.782608695652174, 'f1': 0.8181818181818182, 'number': 23}  | {'precision': 0.7333333333333333, 'recall': 0.5789473684210527, 'f1': 0.6470588235294117, 'number': 38} | {'precision': 0.8235294117647058, 'recall': 0.875, 'f1': 0.8484848484848485, 'number': 16}  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}                               | {'precision': 0.9333333333333333, 'recall': 0.9032258064516129, 'f1': 0.9180327868852459, 'number': 31} | {'precision': 0.8, 'recall': 0.8571428571428571, 'f1': 0.8275862068965518, 'number': 28}                | 0.8476            | 0.8225         | 0.8348     | 0.9527           |
+| 0.0884        | 10.0  | 1410 | 0.3314          | {'precision': 0.8695652173913043, 'recall': 1.0, 'f1': 0.9302325581395349, 'number': 20} | {'precision': 0.8571428571428571, 'recall': 0.782608695652174, 'f1': 0.8181818181818182, 'number': 23}  | {'precision': 0.7, 'recall': 0.5526315789473685, 'f1': 0.6176470588235295, 'number': 38}                | {'precision': 0.8235294117647058, 'recall': 0.875, 'f1': 0.8484848484848485, 'number': 16}  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}                               | {'precision': 0.9333333333333333, 'recall': 0.9032258064516129, 'f1': 0.9180327868852459, 'number': 31} | {'precision': 0.7666666666666667, 'recall': 0.8214285714285714, 'f1': 0.793103448275862, 'number': 28}  | 0.8354            | 0.8107         | 0.8228     | 0.9519           |
+
+
+### Framework versions
+
+- Transformers 4.44.0
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

model.safetensors

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