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roberta-large-ner-ghtk-cs-add-label-new-data-3090-29Aug-1
Kudod/roberta-large-ner-ghtk-cs-add-label-new-data-3090-29Aug-1
roberta-large-ner-ghtk-cs-add-label-new-data-3090-29Aug-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. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Aug 29, 2024
- Updated
- Aug 29, 2024
- SHA
- 97535b4c
Base models
Model card
From the Hugging Face model README
<!-- 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-add-label-new-data-3090-29Aug-1
This model is a fine-tuned version of Kudod/roberta-large-ner-ghtk-cs-6-label-old-data-3090-15Aug-2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2619
- Tk: {'precision': 1.0, 'recall': 0.6896551724137931, 'f1': 0.8163265306122449, 'number': 116}
- Gày: {'precision': 0.7380952380952381, 'recall': 0.9117647058823529, 'f1': 0.8157894736842106, 'number': 34}
- Gày trừu tượng: {'precision': 0.9090909090909091, 'recall': 0.9016393442622951, 'f1': 0.9053497942386831, 'number': 488}
- Iờ: {'precision': 0.6744186046511628, 'recall': 0.7631578947368421, 'f1': 0.7160493827160495, 'number': 38}
- Ã đơn: {'precision': 0.8454106280193237, 'recall': 0.8620689655172413, 'f1': 0.8536585365853658, 'number': 203}
- Đt: {'precision': 0.9252934898612594, 'recall': 0.9874715261958997, 'f1': 0.9553719008264463, 'number': 878}
- Đt trừu tượng: {'precision': 0.805668016194332, 'recall': 0.8540772532188842, 'f1': 0.8291666666666667, 'number': 233}
- Overall Precision: 0.8926
- Overall Recall: 0.9151
- Overall F1: 0.9037
- Overall Accuracy: 0.9655
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 | Iờ | Ã đơn | Đt | Đt trừu tượng | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 475 | 0.1739 | {'precision': 0.9655172413793104, 'recall': 0.7241379310344828, 'f1': 0.8275862068965517, 'number': 116} | {'precision': 0.7631578947368421, 'recall': 0.8529411764705882, 'f1': 0.8055555555555555, 'number': 34} | {'precision': 0.9106382978723404, 'recall': 0.8770491803278688, 'f1': 0.8935281837160751, 'number': 488} | {'precision': 0.6666666666666666, 'recall': 0.3157894736842105, 'f1': 0.42857142857142855, 'number': 38} | {'precision': 0.8164251207729468, 'recall': 0.8325123152709359, 'f1': 0.824390243902439, 'number': 203} | {'precision': 0.9071949947862357, 'recall': 0.9908883826879271, 'f1': 0.9471965160587914, 'number': 878} | {'precision': 0.8625592417061612, 'recall': 0.7811158798283262, 'f1': 0.8198198198198199, 'number': 233} | 0.8915 | 0.8915 | 0.8915 | 0.9600 |
| 0.0852 | 2.0 | 950 | 0.1570 | {'precision': 1.0, 'recall': 0.7413793103448276, 'f1': 0.8514851485148515, 'number': 116} | {'precision': 0.5555555555555556, 'recall': 0.7352941176470589, 'f1': 0.6329113924050633, 'number': 34} | {'precision': 0.8913043478260869, 'recall': 0.9241803278688525, 'f1': 0.9074446680080483, 'number': 488} | {'precision': 0.6206896551724138, 'recall': 0.9473684210526315, 'f1': 0.75, 'number': 38} | {'precision': 0.8743455497382199, 'recall': 0.8226600985221675, 'f1': 0.8477157360406091, 'number': 203} | {'precision': 0.9199157007376185, 'recall': 0.9943052391799544, 'f1': 0.9556650246305419, 'number': 878} | {'precision': 0.7915057915057915, 'recall': 0.8798283261802575, 'f1': 0.8333333333333334, 'number': 233} | 0.8801 | 0.9261 | 0.9025 | 0.9636 |
| 0.0519 | 3.0 | 1425 | 0.1889 | {'precision': 0.978494623655914, 'recall': 0.7844827586206896, 'f1': 0.8708133971291866, 'number': 116} | {'precision': 0.7647058823529411, 'recall': 0.7647058823529411, 'f1': 0.7647058823529412, 'number': 34} | {'precision': 0.9100418410041841, 'recall': 0.8913934426229508, 'f1': 0.9006211180124224, 'number': 488} | {'precision': 0.8076923076923077, 'recall': 0.5526315789473685, 'f1': 0.6562500000000001, 'number': 38} | {'precision': 0.868421052631579, 'recall': 0.812807881773399, 'f1': 0.8396946564885496, 'number': 203} | {'precision': 0.9498910675381264, 'recall': 0.9931662870159453, 'f1': 0.9710467706013363, 'number': 878} | {'precision': 0.8482142857142857, 'recall': 0.8154506437768241, 'f1': 0.8315098468271336, 'number': 233} | 0.9170 | 0.9045 | 0.9107 | 0.9653 |
| 0.0427 | 4.0 | 1900 | 0.2090 | {'precision': 0.989010989010989, 'recall': 0.7758620689655172, 'f1': 0.8695652173913042, 'number': 116} | {'precision': 0.7073170731707317, 'recall': 0.8529411764705882, 'f1': 0.7733333333333334, 'number': 34} | {'precision': 0.9135802469135802, 'recall': 0.9098360655737705, 'f1': 0.9117043121149898, 'number': 488} | {'precision': 0.5373134328358209, 'recall': 0.9473684210526315, 'f1': 0.6857142857142856, 'number': 38} | {'precision': 0.8110599078341014, 'recall': 0.8669950738916257, 'f1': 0.8380952380952381, 'number': 203} | {'precision': 0.9432933478735005, 'recall': 0.9851936218678815, 'f1': 0.9637883008356545, 'number': 878} | {'precision': 0.6261127596439169, 'recall': 0.9055793991416309, 'f1': 0.7403508771929823, 'number': 233} | 0.8585 | 0.9302 | 0.8929 | 0.9592 |
| 0.0306 | 5.0 | 2375 | 0.1654 | {'precision': 1.0, 'recall': 0.8275862068965517, 'f1': 0.9056603773584906, 'number': 116} | {'precision': 0.75, 'recall': 0.8823529411764706, 'f1': 0.8108108108108107, 'number': 34} | {'precision': 0.8977955911823647, 'recall': 0.9180327868852459, 'f1': 0.9078014184397163, 'number': 488} | {'precision': 0.8333333333333334, 'recall': 0.7894736842105263, 'f1': 0.8108108108108109, 'number': 38} | {'precision': 0.8578680203045685, 'recall': 0.8325123152709359, 'f1': 0.845, 'number': 203} | {'precision': 0.9487459105779716, 'recall': 0.9908883826879271, 'f1': 0.9693593314763231, 'number': 878} | {'precision': 0.7586206896551724, 'recall': 0.8497854077253219, 'f1': 0.8016194331983805, 'number': 233} | 0.8998 | 0.9251 | 0.9123 | 0.9665 |
| 0.0224 | 6.0 | 2850 | 0.2263 | {'precision': 0.9878048780487805, 'recall': 0.6982758620689655, 'f1': 0.8181818181818181, 'number': 116} | {'precision': 0.6976744186046512, 'recall': 0.8823529411764706, 'f1': 0.7792207792207793, 'number': 34} | {'precision': 0.9096509240246407, 'recall': 0.9077868852459017, 'f1': 0.9087179487179488, 'number': 488} | {'precision': 0.6666666666666666, 'recall': 0.7368421052631579, 'f1': 0.7, 'number': 38} | {'precision': 0.8309859154929577, 'recall': 0.8719211822660099, 'f1': 0.8509615384615384, 'number': 203} | {'precision': 0.9362850971922246, 'recall': 0.9874715261958997, 'f1': 0.9611973392461197, 'number': 878} | {'precision': 0.6947368421052632, 'recall': 0.8497854077253219, 'f1': 0.7644787644787645, 'number': 233} | 0.8778 | 0.9166 | 0.8968 | 0.9620 |
| 0.014 | 7.0 | 3325 | 0.2339 | {'precision': 1.0, 'recall': 0.8793103448275862, 'f1': 0.9357798165137614, 'number': 116} | {'precision': 0.6888888888888889, 'recall': 0.9117647058823529, 'f1': 0.7848101265822784, 'number': 34} | {'precision': 0.9083333333333333, 'recall': 0.8934426229508197, 'f1': 0.9008264462809917, 'number': 488} | {'precision': 0.6122448979591837, 'recall': 0.7894736842105263, 'f1': 0.6896551724137931, 'number': 38} | {'precision': 0.7850877192982456, 'recall': 0.8817733990147784, 'f1': 0.8306264501160093, 'number': 203} | {'precision': 0.9375, 'recall': 0.9908883826879271, 'f1': 0.9634551495016611, 'number': 878} | {'precision': 0.8738317757009346, 'recall': 0.8025751072961373, 'f1': 0.8366890380313199, 'number': 233} | 0.8969 | 0.9221 | 0.9093 | 0.9631 |
| 0.0105 | 8.0 | 3800 | 0.2414 | {'precision': 1.0, 'recall': 0.7758620689655172, 'f1': 0.8737864077669902, 'number': 116} | {'precision': 0.7692307692307693, 'recall': 0.8823529411764706, 'f1': 0.8219178082191781, 'number': 34} | {'precision': 0.8987854251012146, 'recall': 0.9098360655737705, 'f1': 0.9042769857433809, 'number': 488} | {'precision': 0.7073170731707317, 'recall': 0.7631578947368421, 'f1': 0.7341772151898733, 'number': 38} | {'precision': 0.8592964824120602, 'recall': 0.8423645320197044, 'f1': 0.8507462686567163, 'number': 203} | {'precision': 0.9364224137931034, 'recall': 0.989749430523918, 'f1': 0.9623477297895903, 'number': 878} | {'precision': 0.7951807228915663, 'recall': 0.8497854077253219, 'f1': 0.8215767634854771, 'number': 233} | 0.8975 | 0.9201 | 0.9087 | 0.9653 |
| 0.0041 | 9.0 | 4275 | 0.2618 | {'precision': 1.0, 'recall': 0.6896551724137931, 'f1': 0.8163265306122449, 'number': 116} | {'precision': 0.7209302325581395, 'recall': 0.9117647058823529, 'f1': 0.8051948051948051, 'number': 34} | {'precision': 0.9111570247933884, 'recall': 0.9036885245901639, 'f1': 0.9074074074074073, 'number': 488} | {'precision': 0.6888888888888889, 'recall': 0.8157894736842105, 'f1': 0.746987951807229, 'number': 38} | {'precision': 0.8262910798122066, 'recall': 0.8669950738916257, 'f1': 0.8461538461538461, 'number': 203} | {'precision': 0.9235668789808917, 'recall': 0.9908883826879271, 'f1': 0.9560439560439561, 'number': 878} | {'precision': 0.8130081300813008, 'recall': 0.8583690987124464, 'f1': 0.8350730688935282, 'number': 233} | 0.8909 | 0.9191 | 0.9048 | 0.9646 |
| 0.0021 | 10.0 | 4750 | 0.2619 | {'precision': 1.0, 'recall': 0.6896551724137931, 'f1': 0.8163265306122449, 'number': 116} | {'precision': 0.7380952380952381, 'recall': 0.9117647058823529, 'f1': 0.8157894736842106, 'number': 34} | {'precision': 0.9090909090909091, 'recall': 0.9016393442622951, 'f1': 0.9053497942386831, 'number': 488} | {'precision': 0.6744186046511628, 'recall': 0.7631578947368421, 'f1': 0.7160493827160495, 'number': 38} | {'precision': 0.8454106280193237, 'recall': 0.8620689655172413, 'f1': 0.8536585365853658, 'number': 203} | {'precision': 0.9252934898612594, 'recall': 0.9874715261958997, 'f1': 0.9553719008264463, 'number': 878} | {'precision': 0.805668016194332, 'recall': 0.8540772532188842, 'f1': 0.8291666666666667, 'number': 233} | 0.8926 | 0.9151 | 0.9037 | 0.9655 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Type
- xlm-roberta
- License
- mit
- Created
- Aug 29, 2024
- Updated
- Aug 29, 2024