Upload model_evaluation.py

model_evaluation.py

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+import pandas as pd
+from datasets import Dataset
+from transformers import pipeline, GPT2Tokenizer
+from sentence_transformers import SentenceTransformer, util
+
+# Define paths and models
+filename = "output_country_details.txt"
+retrieval_model_name = 'output/sentence-transformer-finetuned/'       #using a prefine-tuned model 
+gpt2_model_name = "gpt2"
+csv_file_path = "train_dataset.csv"
+output_csv_file_path = "updated_train_dataset.csv"
+val_csv_file_path = "val_dataset.csv"
+output_val_csv_file_path = "updated_val_csv.csv"
+
+tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
+
+# Initialize models
+try:
+    retrieval_model = SentenceTransformer(retrieval_model_name)
+    gpt_model = pipeline("text-generation", model=gpt2_model_name)
+    print("Models loaded successfully.")
+except Exception as e:
+    print(f"Failed to load models: {e}")
+
+def load_and_preprocess_text(filename):
+    """
+    Load and preprocess text data from a file.
+
+    Parameters:
+    - filename (str): Path to the text file.
+
+    Returns:
+    - list[str]: A list of preprocessed text segments.
+    """
+    try:
+        with open(filename, 'r', encoding='utf-8') as file:
+            segments = [line.strip() for line in file if line.strip()]
+        print("Text loaded and preprocessed successfully.")
+        return segments
+    except Exception as e:
+        print(f"Failed to load or preprocess text: {e}")
+        return []
+
+segments = load_and_preprocess_text(filename)
+
+def find_relevant_segment(user_query, segments):
+    """
+    Find the most relevant text segment based on a user query.
+
+    Parameters:
+    - user_query (str): The user's query.
+    - segments (list[str]): List of text segments to search within.
+
+    Returns:
+    - str: The most relevant text segment.
+    """
+    try:
+        query_embedding = retrieval_model.encode(user_query)
+        segment_embeddings = retrieval_model.encode(segments)
+        similarities = util.pytorch_cos_sim(query_embedding, segment_embeddings)[0]
+        best_idx = similarities.argmax()
+        return segments[best_idx]
+    except Exception as e:
+        print(f"Error finding relevant segment: {e}")
+        return ""
+
+def generate_response(question):
+    """
+    Generate a response to a given question by finding a relevant text segment and
+    using it to generate a more complete answer.
+
+    Parameters:
+    - question (str): The user's question.
+
+    Returns:
+    - str: Generated response.
+    """
+    relevant_segment = find_relevant_segment(question, segments)
+    return generate_response_with_context(question, relevant_segment)
+
+def generate_response_with_context(user_query, relevant_segment):
+    """
+    Generate a response based on a user query and a relevant segment.
+
+    Parameters:
+    - user_query (str): The user's query.
+    - relevant_segment (str): A relevant fact or detail.
+
+    Returns:
+    - str: Formatted response incorporating the relevant segment.
+    """
+    try:
+        prompt = f"Thank you for your question! Here is an additional fact about your topic: {relevant_segment}"
+        max_tokens = len(tokenizer(prompt)['input_ids']) + 50
+        response = gpt_model(prompt, max_length=max_tokens, temperature=0.25)[0]['generated_text']
+        return clean_up_response(response, relevant_segment)
+    except Exception as e:
+        print(f"Error generating response: {e}")
+        return ""
+
+def clean_up_response(response, segment):
+    """
+    Clean up the generated response to ensure it is tidy and presentable.
+
+    Parameters:
+    - response (str): The initial response generated by the model.
+    - segment (str): The segment used to generate the response.
+
+    Returns:
+    - str: A cleaned and formatted response.
+    """
+    sentences = response.split('.')
+    cleaned_sentences = [sentence.strip() for sentence in sentences if sentence.strip() and sentence.strip() not in segment]
+    cleaned_response = '. '.join(cleaned_sentences).strip()
+    if cleaned_response and not cleaned_response.endswith((".", "!", "?")):
+        cleaned_response += "."
+    return cleaned_response
+
+def process_dataset(csv_file_path, output_csv_file_path):
+    """
+    Process the dataset by generating responses and evaluating their similarities.
+
+    Parameters:
+    - csv_file_path (str): Path to the CSV file containing the dataset.
+    - output_csv_file_path (str): Path where the updated dataset will be saved.
+
+    Prints:
+    - Path to the saved results and the average similarity score.
+    """
+    df = pd.read_csv(csv_file_path)
+    dataset = Dataset.from_pandas(df)
+    updated_dataset = add_model_answers(dataset)
+    similarities = evaluate_similarity(updated_dataset)
+    updated_dataset = updated_dataset.add_column("similarity", similarities)
+    results_df = updated_dataset.to_pandas()
+    results_df.to_csv(output_csv_file_path, index=False)
+    average_similarity = sum(similarities) / len(similarities) if similarities else 0
+    print(f"Results saved to {output_csv_file_path}")
+    print(f"Average Similarity Score: {average_similarity:.3f}")
+
+def add_model_answers(dataset):
+    """
+    Add generated answers to the dataset.
+
+    Parameters:
+    - dataset (datasets.Dataset): The Hugging Face dataset object.
+
+    Returns:
+    - datasets.Dataset: Updated dataset with added answers.
+    """
+    answers = [generate_response(q) for q in dataset['Question']]
+    dataset = dataset.add_column("Answer", answers)
+    return dataset
+
+def evaluate_similarity(dataset):
+    """
+    Evaluate the similarity of generated answers against ground truth answers.
+
+    Parameters:
+    - dataset (datasets.Dataset): The dataset containing both answers and ground truths.
+
+    Returns:
+    - list[float]: List of similarity scores.
+    """
+    similarities = [util.pytorch_cos_sim(retrieval_model.encode(ans), retrieval_model.encode(gt))[0][0].item()
+                    for ans, gt in zip(dataset['Answer'], dataset['GroundTruth'])]
+    return similarities
+
+# Process datasets
+process_dataset(csv_file_path, output_csv_file_path)
+process_dataset(val_csv_file_path, output_val_csv_file_path)
+
+