英文:
F1-Score and Accuracy for Text-Similarity
问题
我正在尝试理解在微调问答模型时如何计算文本之间的F1分数和准确率。
假设我们有以下内容：
labels = [我很好, 他出生于1995年, 埃菲尔铁塔, 狗]
preds = [我很好, 出生于1995年, 埃菲尔, 狗]
在这种情况下，很明显预测结果相当准确，但我如何计算F1分数呢？"dog"和"dogs"并不完全匹配，但它们非常相似。
英文:
I am trying to understand how to calculate F1-Score and accuracy between texts while fine-tuning a QA model.
Let's assume we have this:
labels = [I am fine, He was born in 1995, The Eiffel tower, dogs]
preds = [I am fine, born in 1995, Eiffel, dog]
In this case, it is clear that the predictions are pretty accurate, but how can I measure the F1-Score here? Dog and dogs are not an exact match, but they are very similar.
答案1
得分: 0
一种常用的文本相似度度量标准是Levenshtein距离或编辑距离，它衡量将一个字符串转换为另一个字符串所需的最小单字符编辑（插入、删除或替换）次数。
尝试实现下面的代码。根据您的需求调整threshold。
import Levenshteindef text_similarity_evaluation(labels, preds, threshold=0.8):tp, fp, fn = 0, 0, 0for label, pred in zip(labels, preds):similarity_score = 1 - Levenshtein.distance(label, pred) / max(len(label), len(pred))if similarity_score >= threshold:tp += 1else:fp += 1fn = len(labels) - tpprecision = tp / (tp + fp)recall = tp / (tp + fn)f1_score = 2 * (precision * recall) / (precision + recall)return precision, recall, f1_score# 示例用法labels = ["I am fine", "He was born in 1995", "The Eiffel tower", "dogs"]preds = ["I am fine", "born in 1995", "Eiffel", "dog"]precision, recall, f1_score = text_similarity_evaluation(labels, preds, threshold=0.8)print("Precision:", precision)print("Recall:", recall)print("F1-Score:", f1_score)
英文:
One popular metric for text similarity is the Levenshtein distance or edit distance, which measures the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another.
Try implementing below code. Adjust threshold as per your requirement.
import Levenshteindef text_similarity_evaluation(labels, preds, threshold=0.8):tp, fp, fn = 0, 0, 0for label, pred in zip(labels, preds):similarity_score = 1 - Levenshtein.distance(label, pred) / max(len(label), len(pred))if similarity_score >= threshold:tp += 1else:fp += 1fn = len(labels) - tpprecision = tp / (tp + fp)recall = tp / (tp + fn)f1_score = 2 * (precision * recall) / (precision + recall)return precision, recall, f1_score# Example usagelabels = ["I am fine", "He was born in 1995", "The Eiffel tower", "dogs"]preds = ["I am fine", "born in 1995", "Eiffel", "dog"]precision, recall, f1_score = text_similarity_evaluation(labels, preds, threshold=0.8)print("Precision:", precision)print("Recall:", recall)print("F1-Score:", f1_score)
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