add rouge score tutorial

Author: x4nth055

README.md

@@ -61,6 +61,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
         - [How to Fine Tune BERT for Semantic Textual Similarity using Transformers in Python](https://www.thepythoncode.com/article/finetune-bert-for-semantic-textual-similarity-in-python). ([code](machine-learning/nlp/semantic-textual-similarity))
         - [How to Calculate the BLEU Score in Python](https://www.thepythoncode.com/article/bleu-score-in-python). ([code](machine-learning/nlp/bleu-score))
         - [Word Error Rate in Python](https://www.thepythoncode.com/article/calculate-word-error-rate-in-python). ([code](machine-learning/nlp/wer-score))
+        - [How to Calculate ROUGE Score in Python](https://www.thepythoncode.com/article/calculate-rouge-score-in-python). ([code](machine-learning/nlp/rouge-score))
     - ### [Computer Vision](https://www.thepythoncode.com/topic/computer-vision)
         - [How to Detect Human Faces in Python using OpenCV](https://www.thepythoncode.com/article/detect-faces-opencv-python). ([code](machine-learning/face_detection))
         - [How to Make an Image Classifier in Python using TensorFlow and Keras](https://www.thepythoncode.com/article/image-classification-keras-python). ([code](machine-learning/image-classifier))

machine-learning/nlp/rouge-score/README.md

@@ -0,0 +1 @@
+# [How to Calculate ROUGE Score in Python](https://www.thepythoncode.com/article/calculate-rouge-score-in-python)

machine-learning/nlp/rouge-score/requirements.txt

@@ -0,0 +1 @@
+rouge-score

machine-learning/nlp/rouge-score/rouge.py

@@ -0,0 +1,22 @@
+from rouge_score import rouge_scorer
+
+scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
+
+# Single reference
+candidate_summary = "the cat was found under the bed"
+reference_summary = "the cat was under the bed"
+scores = scorer.score(reference_summary, candidate_summary)
+for key in scores:
+   print(f'{key}: {scores[key]}')
+
+# Multiple references
+candidate_summary = "the cat was found under the bed"
+reference_summaries = ["the cat was under the bed", "found a cat under the bed"]
+scores = {key: [] for key in ['rouge1', 'rouge2', 'rougeL']}
+for ref in reference_summaries:
+   temp_scores = scorer.score(ref, candidate_summary)
+   for key in temp_scores:
+       scores[key].append(temp_scores[key])
+
+for key in scores:
+   print(f'{key}:\n{scores[key]}')