mirror of https://github.com/hpcaitech/ColossalAI
254 lines
9.7 KiB
Python
254 lines
9.7 KiB
Python
import statistics
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from typing import Dict, List
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import jieba
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from bert_score import score
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from nltk.translate.bleu_score import sentence_bleu
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from nltk.translate.chrf_score import sentence_chrf
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from rouge_chinese import Rouge as Rouge_cn
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from rouge_score import rouge_scorer as Rouge_en
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from sklearn.metrics import f1_score, precision_score, recall_score
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from utils import preprocessing_text, remove_redundant_space
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def bleu_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
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"""Calculate BLEU Score Metric
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The calculation includes BLEU-1 for unigram, BLEU-2 for bigram,
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BLEU-3 for trigram and BLEU-4 for 4-gram. Unigram evaluates the
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accuracy in word level, other n-gram evaluate the fluency in
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sentence level.
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"""
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bleu_scores = {"bleu1": 0, "bleu2": 0, "bleu3": 0, "bleu4": 0}
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cumulative_bleu = [0] * 4
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weights = [(1. / 1., 0., 0., 0.), (1. / 2., 1. / 2., 0., 0.), (1. / 3., 1. / 3., 1. / 3., 0.),
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(1. / 4., 1. / 4., 1. / 4., 1. / 4.)]
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for pred, target in zip(preds, targets):
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if language == "cn":
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pred_list = ' '.join(jieba.cut(preprocessing_text(pred))).split()
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target_list = [(' '.join(jieba.cut(preprocessing_text(target)))).split()]
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elif language == "en":
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pred_list = preprocessing_text(pred).split()
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target_list = [preprocessing_text(target).split()]
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bleu = sentence_bleu(target_list, pred_list, weights=weights)
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cumulative_bleu = [a + b for a, b in zip(cumulative_bleu, bleu)]
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for i in range(len(cumulative_bleu)):
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bleu_scores[f"bleu{i+1}"] = cumulative_bleu[i] / len(preds)
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return bleu_scores
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def chrf_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
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"""Calculate CHRF Score Metric in sentence level.
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"""
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chrf_score = {"chrf": 0}
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cumulative_chrf = []
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for pred, target in zip(preds, targets):
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if language == "cn":
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pred_list = ' '.join(jieba.cut(preprocessing_text(pred))).split()
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target_list = ' '.join(jieba.cut(preprocessing_text(target))).split()
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elif language == "en":
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pred_list = preprocessing_text(pred).split()
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target_list = preprocessing_text(target).split()
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cumulative_chrf.append(sentence_chrf(target_list, pred_list))
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chrf_score["chrf"] = statistics.mean(cumulative_chrf)
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return chrf_score
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def rouge_cn_score(preds: List[str], targets: List[str]) -> Dict[str, float]:
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"""Calculate Chinese ROUGE Score Metric
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The calculation includes ROUGE-1 for unigram, ROUGE-2 for bigram
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and ROUGE-L. ROUGE-N evaluates the number of matching n-grams between
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the preds and targets. ROUGE-L measures the number of matching
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longest common subsequence (LCS) between preds and targets.
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"""
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rouge_scores = {"rouge1": 0, "rouge2": 0, "rougeL": 0}
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all_preds = []
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all_targets = []
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for pred, target in zip(preds, targets):
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pred_list = remove_redundant_space(' '.join(jieba.cut(preprocessing_text(pred))))
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target_list = remove_redundant_space(' '.join(jieba.cut(preprocessing_text(target))))
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all_preds.append(pred_list)
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all_targets.append(target_list)
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rouge_cn = Rouge_cn()
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rouge_avg = rouge_cn.get_scores(all_preds, all_targets, avg=True)
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rouge_scores["rouge1"] = rouge_avg["rouge-1"]["f"]
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rouge_scores["rouge2"] = rouge_avg["rouge-2"]["f"]
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rouge_scores["rougeL"] = rouge_avg["rouge-l"]["f"]
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return rouge_scores
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def rouge_en_score(preds: List[str], targets: List[str]) -> Dict[str, float]:
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"""Calculate English ROUGE Score Metric
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The calculation includes ROUGE-1 for unigram, ROUGE-2 for bigram
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and ROUGE-L. ROUGE-N evaluates the number of matching n-grams between
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the preds and targets. ROUGE-L measures the number of matching
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longest common subsequence (LCS) between preds and targets.
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"""
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rouge_scores = {"rouge1": 0, "rouge2": 0, "rougeL": 0}
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all_preds = []
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all_targets = []
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rouge_en = Rouge_en.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=False)
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for pred, target in zip(preds, targets):
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score = rouge_en.score(preprocessing_text(pred), preprocessing_text(target))
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rouge_scores["rouge1"] += score['rouge1'].fmeasure
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rouge_scores["rouge2"] += score['rouge2'].fmeasure
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rouge_scores["rougeL"] += score['rougeL'].fmeasure
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rouge_scores["rouge1"] = rouge_scores["rouge1"] / len(preds)
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rouge_scores["rouge2"] = rouge_scores["rouge2"] / len(preds)
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rouge_scores["rougeL"] = rouge_scores["rougeL"] / len(preds)
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return rouge_scores
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def rouge_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
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"""Calculate ROUGE Score Metric"""
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if language == "cn":
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return rouge_cn_score(preds, targets)
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elif language == "en":
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return rouge_en_score(preds, targets)
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def distinct_score(preds: List[str], language: str) -> Dict[str, float]:
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"""Calculate Distinct Score Metric
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This metric refers to https://arxiv.org/abs/1510.03055.
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It evaluates the diversity of generation text by counting
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the unique n-grams.
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"""
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distinct_score = {"distinct": 0}
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cumulative_distinct = []
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for pred in preds:
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if language == "cn":
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pred_seg_list = ' '.join(jieba.cut(pred)).split()
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count_segs = len(pred_seg_list)
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unique_segs = set(pred_seg_list)
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count_unique_chars = len(unique_segs)
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# prevent denominator from being 0
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cumulative_distinct.append(count_unique_chars / (count_segs + 1e-6))
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elif language == "en":
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# calculate distinct 1-gram, 2-gram, 3-gram
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unique_ngram = [set() for _ in range(0, 3)]
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all_ngram_count = [0 for _ in range(0, 3)]
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split_pred = preprocessing_text(pred).split()
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for n in range(0, 3):
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for i in range(0, len(split_pred) - n):
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ngram = ' '.join(split_pred[i:i + n + 1])
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unique_ngram[n].add(ngram)
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all_ngram_count[n] += 1
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# Sometimes the answer may contain only one word. For 2-gram and 3-gram, the gram count(denominator) may be zero.
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avg_distinct = [len(a) / (b + 1e-6) for a, b in zip(unique_ngram, all_ngram_count)]
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cumulative_distinct.append(statistics.mean(avg_distinct))
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distinct_score["distinct"] = statistics.mean(cumulative_distinct)
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return distinct_score
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def bert_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
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"""Calculate BERTScore Metric
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The BERTScore evaluates the semantic similarity between
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tokens of preds and targets with BERT.
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"""
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bert_score = {"bert_score": 0}
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pred_list = []
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target_list = []
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for pred, target in zip(preds, targets):
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pred_list.append(pred)
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target_list.append(target)
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if language == "cn":
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_, _, F = score(pred_list, target_list, lang="zh", verbose=True)
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elif language == "en":
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_, _, F = score(pred_list, target_list, lang="en", verbose=True)
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bert_score["bert_score"] = F.mean().item()
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return bert_score
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def calculate_precision_recall_f1(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
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"""Precision, Recall and F1-Score Calculation
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The calculation of precision, recall and f1-score is realized by counting
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the number f overlaps between the preds and target. The comparison length
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limited by the shorter one of preds and targets.
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"""
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precision_recall_f1 = {"precision": 0, "recall": 0, "f1_score": 0}
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precision_scores = []
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recall_scores = []
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f1_scores = []
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for pred, target in zip(preds, targets):
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if language == "cn":
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pred_list = [char for char in ' '.join(jieba.cut(preprocessing_text(pred))).split()]
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target_list = [char for char in ' '.join(jieba.cut(preprocessing_text(target))).split()]
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elif language == "en":
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pred_list = [char for char in preprocessing_text(pred).split()]
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target_list = [char for char in preprocessing_text(target).split()]
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target_labels = [1] * min(len(target_list), len(pred_list))
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pred_labels = [int(pred_list[i] == target_list[i]) for i in range(0, min(len(target_list), len(pred_list)))]
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precision_scores.append(precision_score(target_labels, pred_labels, zero_division=0))
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recall_scores.append(recall_score(target_labels, pred_labels, zero_division=0))
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f1_scores.append(f1_score(target_labels, pred_labels, zero_division=0))
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precision_recall_f1["precision"] = statistics.mean(precision_scores)
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precision_recall_f1["recall"] = statistics.mean(recall_scores)
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precision_recall_f1["f1_score"] = statistics.mean(f1_scores)
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return precision_recall_f1
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def precision(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
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"""Calculate Precision Metric
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Calculating precision by counting the number of overlaps between the preds and target.
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"""
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precision = {"precision": 0}
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precision["precision"] = calculate_precision_recall_f1(preds, targets, language)["precision"]
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return precision
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def recall(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
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"""Calculate Recall Metric
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Calculating recall by counting the number of overlaps between the preds and target.
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"""
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recall = {"recall": 0}
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recall["recall"] = calculate_precision_recall_f1(preds, targets, language)["recall"]
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return recall
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def F1_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
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"""Calculate F1-score Metric
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Calculating f1-score by counting the number of overlaps between the preds and target.
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"""
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f1 = {"f1_score": 0}
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f1["f1_score"] = calculate_precision_recall_f1(preds, targets, language)["f1_score"]
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return f1
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