hello I need to know what needsto be done here
The choice between difflib and libraries like spaCy for text similarity depends on your specific use case and requirements. Let's delve into the differences between them and the factors to consider when deciding which library to use.
spaCy: spaCy is a powerful and widely used NLP library that offers various functionalities beyond just text similarity. It's designed for efficient processing of text and provides pre-trained models for various NLP tasks like part-of-speech tagging, named entity recognition, dependency parsing, and more. For text similarity, spaCy's models take into account contextual information and semantic meaning, resulting in accurate and context-aware similarity scores.
Advantages of spaCy for text similarity:
Contextual Understanding: spaCy's models capture semantic meaning, word relationships, and context, which often leads to more accurate similarity assessments.
Advanced Features: spaCy provides various NLP tools, allowing you to perform a wide range of text-related tasks beyond similarity calculation.
Pre-trained Models: You can use spaCy's pre-trained models that are trained on large datasets, improving accuracy.
Disadvantages:
Installation and Resource Requirements: Installing and loading spaCy models might require additional resources, making it potentially heavier in terms of memory and processing.
Learning Curve: For more advanced usage, understanding spaCy's features and methods might involve a learning curve.
import json
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from difflib import SequenceMatcher
# Example text_list created earlier
text_list = ["Hello?", "How can I assist you?", "Hi there!", "What can I help you with?"]
# Example question texts extracted earlier
cleaned_question_texts_by_label = {
"a": ["who are you?", "what's your name?"],
"b": ["how are you?", "where are you from?"]
}
# Create TF-IDF vectorizer
tfidf_vectorizer = TfidfVectorizer()
# Create a dictionary to store the filtered results
filtered_results = {
"total_questions": sum(len(questions) for questions in cleaned_question_texts_by_label.values()),
"num_true": 0,
"num_false": 0,
"true_percentage": 0.0,
}
# Iterate through each question from each label
for label, question_texts in cleaned_question_texts_by_label.items():
filtered_results[label] = []
for idx, question in enumerate(question_texts, start=1):
question_result = {}
question_result["question"] = question
matched_parts = []
max_similarity_score = 0.0
for text in text_list:
# Calculate similarity score using difflib's SequenceMatcher
similarity_score = SequenceMatcher(None, question, text).ratio()
# Check if similarity score is above a certain threshold
if similarity_score > 0.6:
# Calculate TF-IDF cosine similarity
tfidf_matrix = tfidf_vectorizer.fit_transform([question, text])
cosine_sim = cosine_similarity(tfidf_matrix[0], tfidf_matrix[1])[0][0]
if cosine_sim > 0.6:
matched_parts.append({"part": text, "similarity": cosine_sim})
max_similarity_score = max(max_similarity_score, cosine_sim)
question_result["asked"] = bool(matched_parts)
question_result["matched_parts"] = matched_parts
question_result["max_similarity"] = max_similarity_score
filtered_results[label].append(question_result)
# Update true and false counts
if question_result["asked"]:
filtered_results["num_true"] += 1
else:
filtered_results["num_false"] += 1
# Calculate true percentage
filtered_results["true_percentage"] = (filtered_results["num_true"] / filtered_results["total_questions"]) * 100
# Save the updated filtered_results dictionary to the JSON file
with open('filtered_results.json', 'w') as json_file:
json.dump(filtered_results, json_file, indent=4)
