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--- |
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base_model: NlpHUST/ner-vietnamese-electra-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: ner-education-hcmut |
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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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# ner-education-hcmut |
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This model is a fine-tuned version of [NlpHUST/ner-vietnamese-electra-base](https://huggingface.co/NlpHUST/ner-vietnamese-electra-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0985 |
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- Location: {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} |
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- Miscellaneous: {'precision': 0.6069651741293532, 'recall': 0.7176470588235294, 'f1': 0.6576819407008085, 'number': 170} |
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- Organization: {'precision': 0.4166666666666667, 'recall': 0.5769230769230769, 'f1': 0.48387096774193544, 'number': 26} |
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- Person: {'precision': 0.75, 'recall': 0.6, 'f1': 0.6666666666666665, 'number': 10} |
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- Overall Precision: 0.5863 |
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- Overall Recall: 0.6887 |
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- Overall F1: 0.6334 |
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- Overall Accuracy: 0.9702 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Location | Miscellaneous | Organization | Person | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| No log | 1.0 | 269 | 0.1088 | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 0.46311475409836067, 'recall': 0.6647058823529411, 'f1': 0.5458937198067633, 'number': 170} | {'precision': 0.3333333333333333, 'recall': 0.46153846153846156, 'f1': 0.3870967741935484, 'number': 26} | {'precision': 0.6666666666666666, 'recall': 0.6, 'f1': 0.631578947368421, 'number': 10} | 0.4573 | 0.6321 | 0.5307 | 0.9631 | |
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| 0.1453 | 2.0 | 538 | 0.0948 | {'precision': 1.0, 'recall': 0.5, 'f1': 0.6666666666666666, 'number': 6} | {'precision': 0.5525114155251142, 'recall': 0.711764705882353, 'f1': 0.622107969151671, 'number': 170} | {'precision': 0.42424242424242425, 'recall': 0.5384615384615384, 'f1': 0.47457627118644075, 'number': 26} | {'precision': 0.75, 'recall': 0.6, 'f1': 0.6666666666666665, 'number': 10} | 0.5475 | 0.6792 | 0.6063 | 0.9654 | |
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| 0.1453 | 3.0 | 807 | 0.0985 | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 0.6069651741293532, 'recall': 0.7176470588235294, 'f1': 0.6576819407008085, 'number': 170} | {'precision': 0.4166666666666667, 'recall': 0.5769230769230769, 'f1': 0.48387096774193544, 'number': 26} | {'precision': 0.75, 'recall': 0.6, 'f1': 0.6666666666666665, 'number': 10} | 0.5863 | 0.6887 | 0.6334 | 0.9702 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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