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app.py
CHANGED
@@ -129,13 +129,15 @@ for col in original_df.columns:
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original_df[col] = original_df[col].apply(formatter) # For numerical values
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original_df.rename(columns=column_names, inplace=True)
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original_df.sort_values(by='Common Voice WER', inplace=True)
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# Compute delta between average WER and CV WER
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original_df['Detla Avg. C.V. WER'] = original_df['Average WER ⬇️'] - original_df['Common Voice WER ⬇️']
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original_df['Detla Avg. C.V. WER'] = pd.to_numeric(original_df['Detla Avg. C.V. WER'], errors='coerce') # Convert to numerical data type
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original_df['Detla Avg. C.V. WER'] = original_df[col].apply(lambda x: round(x, 2) if not pd.isna(x) else x) # Round and handle NaN values
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COLS = [c.name for c in fields(AutoEvalColumn)]
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TYPES = [c.type for c in fields(AutoEvalColumn)]
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original_df[col] = original_df[col].apply(formatter) # For numerical values
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original_df.rename(columns=column_names, inplace=True)
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# Compute delta between average WER and CV WER
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original_df['Detla Avg. C.V. WER'] = original_df['Average WER ⬇️'] - original_df['Common Voice WER ⬇️']
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original_df['Detla Avg. C.V. WER'] = pd.to_numeric(original_df['Detla Avg. C.V. WER'], errors='coerce') # Convert to numerical data type
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original_df['Detla Avg. C.V. WER'] = original_df[col].apply(lambda x: round(x, 2) if not pd.isna(x) else x) # Round and handle NaN values
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original_df.sort_values(by='Detla Avg. C.V. WER', inplace=True)
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COLS = [c.name for c in fields(AutoEvalColumn)]
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TYPES = [c.type for c in fields(AutoEvalColumn)]
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