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---
base_model: distilbert-base-uncased
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: medical_condition_classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# medical_condition_classification
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.
It achieves the following results on the test data set:
- Loss: 0.8930
- Accuracy: 0.7951
## Model description
The Goal of the model is to predict the medical condition based on the review of the drug. There're `751` classes.
## Intended uses & limitations
More information needed
## Training and evaluation data
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.
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.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 1.8625 | 0.4329 | 2000 | 1.7199 | 0.6397 |
| 1.459 | 0.8658 | 4000 | 1.3696 | 0.6890 |
| 1.1737 | 1.2987 | 6000 | 1.2131 | 0.7172 |
| 1.042 | 1.7316 | 8000 | 1.1014 | 0.7329 |
| 0.8431 | 2.1645 | 10000 | 1.0322 | 0.7510 |
| 0.8012 | 2.5974 | 12000 | 0.9889 | 0.7587 |
| 0.7312 | 3.0303 | 14000 | 0.9497 | 0.7727 |
| 0.6561 | 3.4632 | 16000 | 0.9338 | 0.7805 |
| 0.6132 | 3.8961 | 18000 | 0.9073 | 0.7875 |
| 0.5195 | 4.3290 | 20000 | 0.9011 | 0.7929 |
| 0.5015 | 4.7619 | 22000 | 0.8930 | 0.7951 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1
- Datasets 3.0.1
- Tokenizers 0.20.1
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