---
license: apache-2.0
base_model: bert-base-uncased
tags:
- 'biology '
- NLP
- text-classification
- drugs
- BERT
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-drug-review-to-condition
  results: []
language:
- en
library_name: transformers
datasets:
- Zakia/drugscom_reviews
---

# bert-drug-review-to-condition

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4308
- Accuracy: 0.9209
- Precision: 0.9061
- Recall: 0.9209
- F1: 0.9106

## Model description

Fine-tuning of Bert model with drug-related data for the purpose of text classification

## Intended uses & limitations

Personal project.

## Training and evaluation data

Kallumadi,Surya and Grer,Felix. (2018). Drug Reviews (Drugs.com). UCI Machine Learning Repository. https://doi.org/10.24432/C5SK5S.

## Training procedure
Multiclass classification
The model predicts the 'condition' feature from the 'review' feature, only the first 21 conditions are selected.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 113  | 1.1375          | 0.7747   | 0.7301    | 0.7747 | 0.7450 |
| No log        | 2.0   | 226  | 0.5595          | 0.8854   | 0.8675    | 0.8854 | 0.8728 |
| No log        | 3.0   | 339  | 0.4308          | 0.9209   | 0.9061    | 0.9209 | 0.9106 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1