# Define the table and queries table = aggregation_predictions_string = for x in predicted_aggregation_indices] answers = for coordinates in predicted_answer_coordinates: if len(coordinates) = 1: # only a single cell: answers.append(table.iat]) else: # multiple cells cell_values = for coordinate in coordinates: cell_values.append(table.iat) answers.append(", ".join(cell_values)) display(table) print("") for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string): print(query) if predicted_agg = "NONE": print("Predicted answer: " + answer) else: print("Predicted answer: " + predicted_agg + " > " + answer) # Load pretrained model: TAPAS finetuned on WikiTable Questions model = om_pretrained("google/tapas-base-finetuned-wtq") # Load pretrained tokenizer: TAPAS finetuned on WikiTable Questions tokenizer = om_pretrained("google/tapas-base-finetuned-wtq")
I am trying to run the HuggingFace Transformers based TAPAS model:įrom transformers import TapasTokenizer, TapasForQuestionAnswering import pandas as pd
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