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Update app.py
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app.py
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@@ -27,46 +27,68 @@ markdown_text = """
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- Input one budget line per time.
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- Accuracy of the model is ~88%.
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"""
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# HTML formatted table
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html_table = """
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"""
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iface = gr.Interface(
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- Input one budget line per time.
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- Accuracy of the model is ~88%.
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"""
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html_table = """
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<h2 style="text-align: center;">COFOG Budget Classification</h2>
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<p style='text-align: justify'>
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This classifier was developed utilizing the pre-trained BERT
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(Bidirectional Encoder Representations from Transformers) model
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with an uncased configuration, with over 1500 manually
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labeled dataset comprising budget line items extracted from
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various budgetary documents. To balance the data, additional data
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was generated using GPT-4 where categories were not available
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in budget documents. The model training was executed
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on a Google Colab environment, specifically utilizing a Tesla T4 GPU.
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Detailed metrics of the training process are as follows:
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<code>TrainOutput(global_step=395, training_loss=1.1497593360611156,
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metrics={'train_runtime': 650.0119, 'train_samples_per_second':
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9.638, 'train_steps_per_second': 0.608, 'total_flos': 1648509163714560.0,
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'train_loss': 1.1497593360611156, 'epoch': 5.0})</code>. The model
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is designed to predict the primary classification level
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of the Classification of the Functions of Government (COFOG),
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with the predictions from the first level serving as contextual
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input for subsequent second-level classification. The project
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is conducted with an exclusive focus on academic and research
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objectives.
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</p>
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<table style="margin-left: auto; margin-right: auto;">
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<tr>
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<th>Epoch</th>
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<th>Training Loss</th>
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<th>Validation Loss</th>
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<th>Accuracy</th>
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</tr>
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<tr>
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<td>1</td>
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<td>No log</td>
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<td>2.095209</td>
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<td>0.340764</td>
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</tr>
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<tr>
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<td>2</td>
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<td>No log</td>
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<td>1.419945</td>
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<td>0.662420</td>
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</tr>
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<tr>
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<td>3</td>
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<td>No log</td>
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<td>0.683810</td>
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<td>0.850318</td>
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</tr>
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<tr>
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<td>4</td>
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<td>No log</td>
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<td>0.460408</td>
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<td>0.872611</td>
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</tr>
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<tr>
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<td>5</td>
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<td>No log</td>
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<td>0.422096</td>
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<td>0.888535</td>
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</tr>
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</table>
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</div>
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"""
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iface = gr.Interface(
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