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| import nltk | |
| import numpy as np | |
| from config import metric | |
| from utils import refine_candidate_text | |
| from texts.bart_score import ( | |
| bart_score, | |
| check_bart_score, | |
| ) | |
| def compute_metrics(evaluation_predictions): | |
| """ | |
| Function to compute evaluation metrics for model predictions. | |
| Parameters: | |
| evaluation_predictions (tuple): A tuple containing two elements: | |
| - predictions (array-like): The raw prediction scores from the model. | |
| - labels (array-like): The true labels for the evaluation data. | |
| Returns: | |
| dict: A dictionary containing the computed evaluation metrics. | |
| """ | |
| # Unpack predictions and labels from the input tuple | |
| raw_predictions, true_labels = evaluation_predictions | |
| # Convert raw prediction scores to predicted class labels | |
| predicted_labels = np.argmax(raw_predictions, axis=1) | |
| # Compute and return the evaluation metrics | |
| return metric.compute( | |
| prediction_scores=predicted_labels, | |
| references=true_labels, | |
| average="macro", | |
| ) | |
| def extract_by_best_similarity(input_text, raw_text): | |
| """ | |
| Extracts the best candidate string from the raw text based on the highest | |
| similarity score compared to the input text. The similarity score is | |
| calculated using the BART score. | |
| Args: | |
| input_text (str): The original text. | |
| raw_text (str): The raw text containing multiple candidate strings. | |
| Returns: | |
| str: The best candidate string with the highest similarity score. | |
| Returns the input text if no suitable candidate is found. | |
| """ | |
| # Refine the raw text | |
| refined_raw_text = refine_candidate_text(input_text, raw_text) | |
| # Tokenize the refined raw text into sentences | |
| raw_candidates = nltk.sent_tokenize(refined_raw_text) | |
| # Split sentences further by newlines to get individual candidates | |
| candidate_list = [] | |
| for sentence in raw_candidates: | |
| candidate_list.extend(sentence.split("\n")) | |
| # Initialize variables to track the best similarity score | |
| # and the best candidate | |
| best_similarity = -9999 | |
| best_candidate = "" | |
| # Iterate over each candidate to find the best one based on the BART score | |
| for candidate in candidate_list: | |
| refined_candidate = refine_candidate_text(input_text, candidate) | |
| if check_bart_score(input_text, refined_candidate): | |
| score = bart_score(input_text, refined_candidate)[0] | |
| if score > best_similarity: | |
| best_similarity = score | |
| best_candidate = refined_candidate | |
| # Print the best candidate found | |
| print(f"best_candidate = {best_candidate}") | |
| # Return the best candidate if found, otherwise return the input text | |
| if best_candidate == "": | |
| return input_text | |
| return best_candidate | |