Update README.md
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README.md
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@@ -37,7 +37,7 @@ def process_row(row: List, row_index: int):
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def process_table(table_content: Dict):
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table_str = process_header(table_content["header"]) + " "
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for i, row_example in enumerate(table_content["rows"]):
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table_str +=
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return table_str.strip()
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# load the dataset
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@@ -51,7 +51,7 @@ for sample in hinditableQA['train']:
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# create the input sequence: query + linearized input table
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table_content = {"header": list(input_table.columns)[1:], "rows": [list(row.values)[1:] for i, row in input_table.iterrows()]}
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linearized_inp_table = process_table(table_content)
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linearized_output_table = process_table({"name": None, "header": [
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"rows": [list(row.values) for i, row in answer.iterrows()]})
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source = query + " " + linearized_inp_table
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target = linearized_output_table
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def process_table(table_content: Dict):
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table_str = process_header(table_content["header"]) + " "
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for i, row_example in enumerate(table_content["rows"]):
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table_str += process_row(row_example, row_index=i + 1) + " "
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return table_str.strip()
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# load the dataset
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# create the input sequence: query + linearized input table
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table_content = {"header": list(input_table.columns)[1:], "rows": [list(row.values)[1:] for i, row in input_table.iterrows()]}
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linearized_inp_table = process_table(table_content)
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linearized_output_table = process_table({"name": None, "header": [translate_column(col) for col in list(answer.columns)],
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"rows": [list(row.values) for i, row in answer.iterrows()]})
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source = query + " " + linearized_inp_table
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target = linearized_output_table
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