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--- |
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base_model: distilbert-base-uncased |
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library_name: transformers |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: medical_condition_classification |
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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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# medical_condition_classification |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an [Drugs.com](https://archive.ics.uci.edu/dataset/462/drug+review+dataset+drugs+com) dataset. |
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It achieves the following results on the test data set: |
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- Loss: 0.8930 |
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- Accuracy: 0.7951 |
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## Model description |
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The Goal of the model is to predict the medical condition based on the review of the drug. There're `751` classes. |
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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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The training, evaluation & testing data can be found under [samsaara/medical_condition_classification](https://huggingface.co/datasets/samsaara/medical_condition_classification) of the 🤗 `Datasets` and the process itself can be found in the `modeling.ipynb` notebook. |
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By default, the dataset has `train, test` splits. `train` is then further divided into `train, validation` splits with `0.8, 0.2` ratio. Final results shown are on the `test` dataset. |
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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: 3e-05 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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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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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:------:|:-----:|:---------------:|:--------:| |
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| 1.8625 | 0.4329 | 2000 | 1.7199 | 0.6397 | |
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| 1.459 | 0.8658 | 4000 | 1.3696 | 0.6890 | |
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| 1.1737 | 1.2987 | 6000 | 1.2131 | 0.7172 | |
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| 1.042 | 1.7316 | 8000 | 1.1014 | 0.7329 | |
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| 0.8431 | 2.1645 | 10000 | 1.0322 | 0.7510 | |
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| 0.8012 | 2.5974 | 12000 | 0.9889 | 0.7587 | |
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| 0.7312 | 3.0303 | 14000 | 0.9497 | 0.7727 | |
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| 0.6561 | 3.4632 | 16000 | 0.9338 | 0.7805 | |
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| 0.6132 | 3.8961 | 18000 | 0.9073 | 0.7875 | |
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| 0.5195 | 4.3290 | 20000 | 0.9011 | 0.7929 | |
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| 0.5015 | 4.7619 | 22000 | 0.8930 | 0.7951 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.4.1 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
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