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--- |
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base_model: medicalai/ClinicalBERT |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: working |
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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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# Herbal Multilabel Classification |
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This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on a custom dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0108 |
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- F1: 0.9834 |
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- Roc Auc: 0.9930 |
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- Accuracy: 0.9853 |
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## Model description |
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It is a multilabel classification model that deals with 10 herbal plants |
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(Jackfruit, Sambong, Lemon, Jasmine, Mango, Mint, Ampalaya, Malunggay, Guava, Lagundi) |
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which are abundant in the Philippines. |
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The model classifies a herbal(s) that is/are applicable based on the input symptom |
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of the user. |
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## Intended uses & limitations |
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The model is created for the purpose of completing a University course. |
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It will be integrated to a React Native mobile application for the |
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project. |
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The model performs well when the input of the user contains a symptom that has been trained |
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to the model from the dataset. However, other words/inputs that do not present a significance to |
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the purpose of the model would generate an underwhelming and inaccurate result. |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| No log | 1.0 | 136 | 0.0223 | 0.9834 | 0.9930 | 0.9853 | |
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| No log | 2.0 | 272 | 0.0163 | 0.9881 | 0.9959 | 0.9926 | |
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| No log | 3.0 | 408 | 0.0137 | 0.9834 | 0.9930 | 0.9853 | |
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| 0.0216 | 4.0 | 544 | 0.0120 | 0.9834 | 0.9930 | 0.9853 | |
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| 0.0216 | 5.0 | 680 | 0.0108 | 0.9834 | 0.9930 | 0.9853 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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