Training complete
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README.md
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---
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license: apache-2.0
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base_model: google-bert/bert-large-cased
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tags:
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- generated_from_trainer
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model-index:
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- name: google-bert-large-cased-finetuned-ner-vlsp2021-3090-15June-1
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# google-bert-large-cased-finetuned-ner-vlsp2021-3090-15June-1
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This model is a fine-tuned version of [google-bert/bert-large-cased](https://huggingface.co/google-bert/bert-large-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1555
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- Atetime: {'precision': 0.7904411764705882, 'recall': 0.864321608040201, 'f1': 0.8257321171387422, 'number': 995}
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- Ddress: {'precision': 0.8076923076923077, 'recall': 0.7241379310344828, 'f1': 0.7636363636363636, 'number': 29}
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- Erson: {'precision': 0.8804057661505605, 'recall': 0.8738738738738738, 'f1': 0.8771276595744681, 'number': 1887}
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- Ersontype: {'precision': 0.5577190542420027, 'recall': 0.5967261904761905, 'f1': 0.5765636232925951, 'number': 672}
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- Honenumber: {'precision': 0.375, 'recall': 0.6666666666666666, 'f1': 0.4800000000000001, 'number': 9}
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- Iscellaneous: {'precision': 0.4172661870503597, 'recall': 0.36477987421383645, 'f1': 0.38926174496644295, 'number': 159}
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- Mail: {'precision': 0.9846153846153847, 'recall': 1.0, 'f1': 0.9922480620155039, 'number': 64}
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- Ocation: {'precision': 0.6956237753102548, 'recall': 0.8236658932714617, 'f1': 0.7542492917847026, 'number': 1293}
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- P: {'precision': 0.6363636363636364, 'recall': 0.6363636363636364, 'f1': 0.6363636363636364, 'number': 11}
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- Rl: {'precision': 0.875, 'recall': 0.9333333333333333, 'f1': 0.9032258064516129, 'number': 15}
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- Roduct: {'precision': 0.4963820549927641, 'recall': 0.5523349436392915, 'f1': 0.5228658536585367, 'number': 621}
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- Overall Precision: 0.7268
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- Overall Recall: 0.7798
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- Overall F1: 0.7524
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- Overall Accuracy: 0.9680
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Atetime | Ddress | Erson | Ersontype | Honenumber | Iscellaneous | Mail | Ocation | P | Rl | Roduct | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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| 0.1537 | 1.0 | 3263 | 0.1310 | {'precision': 0.7152777777777778, 'recall': 0.828140703517588, 'f1': 0.767582673497904, 'number': 995} | {'precision': 0.40476190476190477, 'recall': 0.5862068965517241, 'f1': 0.4788732394366197, 'number': 29} | {'precision': 0.8104639684106614, 'recall': 0.8701642819289878, 'f1': 0.8392537694863277, 'number': 1887} | {'precision': 0.44173441734417346, 'recall': 0.4851190476190476, 'f1': 0.4624113475177305, 'number': 672} | {'precision': 0.11764705882352941, 'recall': 0.2222222222222222, 'f1': 0.15384615384615383, 'number': 9} | {'precision': 0.2848101265822785, 'recall': 0.2830188679245283, 'f1': 0.28391167192429023, 'number': 159} | {'precision': 0.9411764705882353, 'recall': 1.0, 'f1': 0.9696969696969697, 'number': 64} | {'precision': 0.6284970722186076, 'recall': 0.7470997679814385, 'f1': 0.6826855123674911, 'number': 1293} | {'precision': 0.625, 'recall': 0.45454545454545453, 'f1': 0.5263157894736842, 'number': 11} | {'precision': 0.5714285714285714, 'recall': 0.8, 'f1': 0.6666666666666666, 'number': 15} | {'precision': 0.4410029498525074, 'recall': 0.48148148148148145, 'f1': 0.46035411855273284, 'number': 621} | 0.6520 | 0.7301 | 0.6889 | 0.9625 |
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| 0.0882 | 2.0 | 6526 | 0.1432 | {'precision': 0.731433506044905, 'recall': 0.8512562814070351, 'f1': 0.786809103576405, 'number': 995} | {'precision': 0.45, 'recall': 0.6206896551724138, 'f1': 0.5217391304347826, 'number': 29} | {'precision': 0.876265466816648, 'recall': 0.825649178590355, 'f1': 0.8502046384720328, 'number': 1887} | {'precision': 0.6190476190476191, 'recall': 0.4836309523809524, 'f1': 0.5430242272347536, 'number': 672} | {'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 9} | {'precision': 0.304635761589404, 'recall': 0.2893081761006289, 'f1': 0.29677419354838713, 'number': 159} | {'precision': 0.9846153846153847, 'recall': 1.0, 'f1': 0.9922480620155039, 'number': 64} | {'precision': 0.610655737704918, 'recall': 0.8066511987625676, 'f1': 0.6951016327890702, 'number': 1293} | {'precision': 0.875, 'recall': 0.6363636363636364, 'f1': 0.7368421052631579, 'number': 11} | {'precision': 0.875, 'recall': 0.9333333333333333, 'f1': 0.9032258064516129, 'number': 15} | {'precision': 0.5594989561586639, 'recall': 0.43156199677938806, 'f1': 0.48727272727272725, 'number': 621} | 0.7060 | 0.7291 | 0.7174 | 0.9651 |
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| 0.0618 | 3.0 | 9789 | 0.1293 | {'precision': 0.7963709677419355, 'recall': 0.7939698492462312, 'f1': 0.7951685958731756, 'number': 995} | {'precision': 0.375, 'recall': 0.5172413793103449, 'f1': 0.4347826086956522, 'number': 29} | {'precision': 0.8490759753593429, 'recall': 0.8765235824059353, 'f1': 0.8625814863102998, 'number': 1887} | {'precision': 0.4895287958115183, 'recall': 0.5565476190476191, 'f1': 0.5208913649025069, 'number': 672} | {'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 9} | {'precision': 0.36666666666666664, 'recall': 0.34591194968553457, 'f1': 0.3559870550161812, 'number': 159} | {'precision': 0.9846153846153847, 'recall': 1.0, 'f1': 0.9922480620155039, 'number': 64} | {'precision': 0.7187060478199718, 'recall': 0.7904098994586234, 'f1': 0.7528545119705341, 'number': 1293} | {'precision': 0.6666666666666666, 'recall': 0.5454545454545454, 'f1': 0.6, 'number': 11} | {'precision': 0.875, 'recall': 0.9333333333333333, 'f1': 0.9032258064516129, 'number': 15} | {'precision': 0.5252525252525253, 'recall': 0.5024154589371981, 'f1': 0.5135802469135803, 'number': 621} | 0.7169 | 0.7493 | 0.7327 | 0.9658 |
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| 0.0413 | 4.0 | 13052 | 0.1444 | {'precision': 0.7855839416058394, 'recall': 0.8653266331658291, 'f1': 0.8235294117647058, 'number': 995} | {'precision': 0.6428571428571429, 'recall': 0.6206896551724138, 'f1': 0.6315789473684211, 'number': 29} | {'precision': 0.8776709401709402, 'recall': 0.8706942236354, 'f1': 0.874168661878159, 'number': 1887} | {'precision': 0.5483405483405484, 'recall': 0.5654761904761905, 'f1': 0.5567765567765568, 'number': 672} | {'precision': 0.2727272727272727, 'recall': 0.6666666666666666, 'f1': 0.3870967741935484, 'number': 9} | {'precision': 0.4251968503937008, 'recall': 0.33962264150943394, 'f1': 0.37762237762237755, 'number': 159} | {'precision': 0.9411764705882353, 'recall': 1.0, 'f1': 0.9696969696969697, 'number': 64} | {'precision': 0.6886422976501305, 'recall': 0.8159319412219644, 'f1': 0.7469026548672566, 'number': 1293} | {'precision': 0.6363636363636364, 'recall': 0.6363636363636364, 'f1': 0.6363636363636364, 'number': 11} | {'precision': 0.8235294117647058, 'recall': 0.9333333333333333, 'f1': 0.8749999999999999, 'number': 15} | {'precision': 0.47619047619047616, 'recall': 0.5636070853462157, 'f1': 0.5162241887905604, 'number': 621} | 0.7179 | 0.7736 | 0.7447 | 0.9668 |
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| 0.0245 | 5.0 | 16315 | 0.1555 | {'precision': 0.7904411764705882, 'recall': 0.864321608040201, 'f1': 0.8257321171387422, 'number': 995} | {'precision': 0.8076923076923077, 'recall': 0.7241379310344828, 'f1': 0.7636363636363636, 'number': 29} | {'precision': 0.8804057661505605, 'recall': 0.8738738738738738, 'f1': 0.8771276595744681, 'number': 1887} | {'precision': 0.5577190542420027, 'recall': 0.5967261904761905, 'f1': 0.5765636232925951, 'number': 672} | {'precision': 0.375, 'recall': 0.6666666666666666, 'f1': 0.4800000000000001, 'number': 9} | {'precision': 0.4172661870503597, 'recall': 0.36477987421383645, 'f1': 0.38926174496644295, 'number': 159} | {'precision': 0.9846153846153847, 'recall': 1.0, 'f1': 0.9922480620155039, 'number': 64} | {'precision': 0.6956237753102548, 'recall': 0.8236658932714617, 'f1': 0.7542492917847026, 'number': 1293} | {'precision': 0.6363636363636364, 'recall': 0.6363636363636364, 'f1': 0.6363636363636364, 'number': 11} | {'precision': 0.875, 'recall': 0.9333333333333333, 'f1': 0.9032258064516129, 'number': 15} | {'precision': 0.4963820549927641, 'recall': 0.5523349436392915, 'f1': 0.5228658536585367, 'number': 621} | 0.7268 | 0.7798 | 0.7524 | 0.9680 |
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### Framework versions
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- Transformers 4.40.2
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- Pytorch 2.3.1+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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