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---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: roberta-large-ner-ghtk-cs-6-label-new-data-3090-14Sep-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-cs-6-label-new-data-3090-14Sep-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: 0.1772
- Tk: {'precision': 0.7345132743362832, 'recall': 0.7155172413793104, 'f1': 0.7248908296943231, 'number': 116}
- Gày: {'precision': 0.7380952380952381, 'recall': 0.9117647058823529, 'f1': 0.8157894736842106, 'number': 34}
- Gày trừu tượng: {'precision': 0.9118852459016393, 'recall': 0.9118852459016393, 'f1': 0.9118852459016393, 'number': 488}
- Ã đơn: {'precision': 0.8514851485148515, 'recall': 0.8472906403940886, 'f1': 0.8493827160493828, 'number': 203}
- Đt: {'precision': 0.9291084854994629, 'recall': 0.9851936218678815, 'f1': 0.9563294637921502, 'number': 878}
- Đt trừu tượng: {'precision': 0.8259109311740891, 'recall': 0.8755364806866953, 'f1': 0.85, 'number': 233}
- Overall Precision: 0.8898
- Overall Recall: 0.9221
- Overall F1: 0.9057
- Overall Accuracy: 0.9665
## 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 | Gày | Gày trừu tượng | Ã đơn | Đt | Đt trừu tượng | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log | 1.0 | 467 | 0.1542 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116} | {'precision': 0.5384615384615384, 'recall': 0.8235294117647058, 'f1': 0.6511627906976744, 'number': 34} | {'precision': 0.7964285714285714, 'recall': 0.9139344262295082, 'f1': 0.8511450381679388, 'number': 488} | {'precision': 0.8045977011494253, 'recall': 0.6896551724137931, 'f1': 0.7427055702917771, 'number': 203} | {'precision': 0.8341323106423778, 'recall': 0.9908883826879271, 'f1': 0.9057782404997397, 'number': 878} | {'precision': 0.7639484978540773, 'recall': 0.7639484978540773, 'f1': 0.7639484978540771, 'number': 233} | 0.8021 | 0.8514 | 0.8260 | 0.9463 |
| 0.263 | 2.0 | 934 | 0.1126 | {'precision': 0.6966292134831461, 'recall': 0.5344827586206896, 'f1': 0.6048780487804878, 'number': 116} | {'precision': 0.6041666666666666, 'recall': 0.8529411764705882, 'f1': 0.7073170731707317, 'number': 34} | {'precision': 0.8535645472061657, 'recall': 0.9077868852459017, 'f1': 0.8798411122144986, 'number': 488} | {'precision': 0.9135802469135802, 'recall': 0.729064039408867, 'f1': 0.810958904109589, 'number': 203} | {'precision': 0.9209694415173867, 'recall': 0.9954441913439636, 'f1': 0.9567597153804049, 'number': 878} | {'precision': 0.7540983606557377, 'recall': 0.7896995708154506, 'f1': 0.7714884696016772, 'number': 233} | 0.8652 | 0.8914 | 0.8781 | 0.9574 |
| 0.1028 | 3.0 | 1401 | 0.1177 | {'precision': 0.868421052631579, 'recall': 0.5689655172413793, 'f1': 0.6875000000000001, 'number': 116} | {'precision': 0.6122448979591837, 'recall': 0.8823529411764706, 'f1': 0.7228915662650602, 'number': 34} | {'precision': 0.8669275929549902, 'recall': 0.9077868852459017, 'f1': 0.8868868868868869, 'number': 488} | {'precision': 0.8507462686567164, 'recall': 0.8423645320197044, 'f1': 0.8465346534653465, 'number': 203} | {'precision': 0.9059561128526645, 'recall': 0.9874715261958997, 'f1': 0.9449591280653951, 'number': 878} | {'precision': 0.8034188034188035, 'recall': 0.8068669527896996, 'f1': 0.8051391862955032, 'number': 233} | 0.8703 | 0.9042 | 0.8869 | 0.9600 |
| 0.077 | 4.0 | 1868 | 0.1431 | {'precision': 0.8666666666666667, 'recall': 0.33620689655172414, 'f1': 0.484472049689441, 'number': 116} | {'precision': 0.7435897435897436, 'recall': 0.8529411764705882, 'f1': 0.7945205479452054, 'number': 34} | {'precision': 0.87109375, 'recall': 0.9139344262295082, 'f1': 0.892, 'number': 488} | {'precision': 0.8972972972972973, 'recall': 0.8177339901477833, 'f1': 0.8556701030927836, 'number': 203} | {'precision': 0.8969072164948454, 'recall': 0.9908883826879271, 'f1': 0.9415584415584417, 'number': 878} | {'precision': 0.6175637393767706, 'recall': 0.9356223175965666, 'f1': 0.7440273037542662, 'number': 233} | 0.8403 | 0.9057 | 0.8718 | 0.9591 |
| 0.053 | 5.0 | 2335 | 0.1367 | {'precision': 0.7065217391304348, 'recall': 0.5603448275862069, 'f1': 0.625, 'number': 116} | {'precision': 0.8181818181818182, 'recall': 0.7941176470588235, 'f1': 0.8059701492537314, 'number': 34} | {'precision': 0.8993963782696177, 'recall': 0.9159836065573771, 'f1': 0.9076142131979696, 'number': 488} | {'precision': 0.8440860215053764, 'recall': 0.7733990147783252, 'f1': 0.8071979434447302, 'number': 203} | {'precision': 0.9205508474576272, 'recall': 0.989749430523918, 'f1': 0.9538968166849615, 'number': 878} | {'precision': 0.7114093959731543, 'recall': 0.9098712446351931, 'f1': 0.7984934086629002, 'number': 233} | 0.8668 | 0.9103 | 0.8881 | 0.9625 |
| 0.0404 | 6.0 | 2802 | 0.1269 | {'precision': 0.7959183673469388, 'recall': 0.6724137931034483, 'f1': 0.7289719626168225, 'number': 116} | {'precision': 0.7209302325581395, 'recall': 0.9117647058823529, 'f1': 0.8051948051948051, 'number': 34} | {'precision': 0.9168399168399168, 'recall': 0.9036885245901639, 'f1': 0.9102167182662538, 'number': 488} | {'precision': 0.8967391304347826, 'recall': 0.812807881773399, 'f1': 0.8527131782945736, 'number': 203} | {'precision': 0.9555555555555556, 'recall': 0.979498861047836, 'f1': 0.9673790776152982, 'number': 878} | {'precision': 0.7619047619047619, 'recall': 0.8927038626609443, 'f1': 0.8221343873517787, 'number': 233} | 0.9010 | 0.9134 | 0.9071 | 0.9664 |
| 0.0232 | 7.0 | 3269 | 0.1361 | {'precision': 0.7818181818181819, 'recall': 0.7413793103448276, 'f1': 0.7610619469026548, 'number': 116} | {'precision': 0.7209302325581395, 'recall': 0.9117647058823529, 'f1': 0.8051948051948051, 'number': 34} | {'precision': 0.896, 'recall': 0.9180327868852459, 'f1': 0.9068825910931175, 'number': 488} | {'precision': 0.875, 'recall': 0.8620689655172413, 'f1': 0.8684863523573201, 'number': 203} | {'precision': 0.9505494505494505, 'recall': 0.9851936218678815, 'f1': 0.9675615212527963, 'number': 878} | {'precision': 0.8504273504273504, 'recall': 0.8540772532188842, 'f1': 0.8522483940042828, 'number': 233} | 0.9034 | 0.9242 | 0.9136 | 0.9693 |
| 0.0192 | 8.0 | 3736 | 0.1610 | {'precision': 0.7478991596638656, 'recall': 0.7672413793103449, 'f1': 0.7574468085106383, 'number': 116} | {'precision': 0.7435897435897436, 'recall': 0.8529411764705882, 'f1': 0.7945205479452054, 'number': 34} | {'precision': 0.9102040816326531, 'recall': 0.9139344262295082, 'f1': 0.9120654396728016, 'number': 488} | {'precision': 0.8442211055276382, 'recall': 0.8275862068965517, 'f1': 0.8358208955223881, 'number': 203} | {'precision': 0.9361471861471862, 'recall': 0.9851936218678815, 'f1': 0.9600443951165373, 'number': 878} | {'precision': 0.8326359832635983, 'recall': 0.8540772532188842, 'f1': 0.8432203389830507, 'number': 233} | 0.8935 | 0.9201 | 0.9066 | 0.9661 |
| 0.01 | 9.0 | 4203 | 0.1725 | {'precision': 0.7368421052631579, 'recall': 0.603448275862069, 'f1': 0.6635071090047393, 'number': 116} | {'precision': 0.7045454545454546, 'recall': 0.9117647058823529, 'f1': 0.794871794871795, 'number': 34} | {'precision': 0.9087221095334685, 'recall': 0.9180327868852459, 'f1': 0.9133537206931702, 'number': 488} | {'precision': 0.8613861386138614, 'recall': 0.8571428571428571, 'f1': 0.8592592592592593, 'number': 203} | {'precision': 0.9261241970021413, 'recall': 0.9851936218678815, 'f1': 0.9547461368653422, 'number': 878} | {'precision': 0.8326359832635983, 'recall': 0.8540772532188842, 'f1': 0.8432203389830507, 'number': 233} | 0.8904 | 0.9155 | 0.9028 | 0.9658 |
| 0.0082 | 10.0 | 4670 | 0.1772 | {'precision': 0.7345132743362832, 'recall': 0.7155172413793104, 'f1': 0.7248908296943231, 'number': 116} | {'precision': 0.7380952380952381, 'recall': 0.9117647058823529, 'f1': 0.8157894736842106, 'number': 34} | {'precision': 0.9118852459016393, 'recall': 0.9118852459016393, 'f1': 0.9118852459016393, 'number': 488} | {'precision': 0.8514851485148515, 'recall': 0.8472906403940886, 'f1': 0.8493827160493828, 'number': 203} | {'precision': 0.9291084854994629, 'recall': 0.9851936218678815, 'f1': 0.9563294637921502, 'number': 878} | {'precision': 0.8259109311740891, 'recall': 0.8755364806866953, 'f1': 0.85, 'number': 233} | 0.8898 | 0.9221 | 0.9057 | 0.9665 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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