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---
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
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