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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: 0.0681 |
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- Location: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} |
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- Miscellaneous: {'precision': 0.6911764705882353, 'recall': 0.7230769230769231, 'f1': 0.7067669172932332, 'number': 65} |
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- Organization: {'precision': 0.4166666666666667, 'recall': 0.5, 'f1': 0.45454545454545453, 'number': 10} |
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- Person: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} |
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- Overall Precision: 0.6190 |
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- Overall Recall: 0.6118 |
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- Overall F1: 0.6154 |
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- Overall Accuracy: 0.9851 |
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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 | 207 | 0.0705 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.49333333333333335, 'recall': 0.5692307692307692, 'f1': 0.5285714285714285, 'number': 65} | {'precision': 0.36363636363636365, 'recall': 0.4, 'f1': 0.380952380952381, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.4556 | 0.4824 | 0.4686 | 0.9806 | |
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| No log | 2.0 | 414 | 0.0672 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.6133333333333333, 'recall': 0.7076923076923077, 'f1': 0.657142857142857, 'number': 65} | {'precision': 0.4666666666666667, 'recall': 0.7, 'f1': 0.56, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.5638 | 0.6235 | 0.5922 | 0.9838 | |
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| 0.0564 | 3.0 | 621 | 0.0681 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.6911764705882353, 'recall': 0.7230769230769231, 'f1': 0.7067669172932332, 'number': 65} | {'precision': 0.4166666666666667, 'recall': 0.5, 'f1': 0.45454545454545453, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.6190 | 0.6118 | 0.6154 | 0.9851 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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