tranhoangnguyen03/finetuned_reranker_skus

Generate training data

# Function to convert dataframe to list of InputExample
def df_to_input_examples(df):
    return [
        InputExample(texts=[row['query'],
                            row['document']],
                            label=float(row['relevance_score']))
        for _, row in df.iterrows()
    ]

train_samples = df_to_input_examples(train_df)
val_samples = df_to_input_examples(val_df)

# Create a DataLoader for training
train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=16)

Create Evaluator class

# Custom evaluator for CrossEncoder
class CrossEncoderEvaluator:
    def __init__(self, eval_samples):
        self.eval_samples = eval_samples

    def __call__(self, model, **kwargs):  # Add **kwargs to catch extra arguments
        predictions = model.predict([[sample.texts[0], sample.texts[1]] for sample in self.eval_samples])
        labels = [sample.label for sample in self.eval_samples]

        pearson_corr, _ = pearsonr(predictions, labels)
        spearman_corr, _ = spearmanr(predictions, labels)

        return (pearson_corr + spearman_corr) / 2  # Average of Pearson and Spearman correlations

# Prepare the evaluator
evaluator = CrossEncoderEvaluator(val_samples)

Train the model

# Initialize the cross-encoder model
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2', num_labels=1)

# Train the model
model.fit(
    train_dataloader=train_dataloader,
    evaluator=evaluator,
    epochs=100,
    warmup_steps=100,
    evaluation_steps=500,
    output_path='fine_tuned_reranker'
)

Usage

# Load the fine-tuned reranker
reranker_model = CrossEncoder('fine_tuned_reranker')

def search_and_rerank(query, documents, top_k=10):
    # Prepare pairs for reranking
    pairs = [(query, doc) for doc in documents]

    # Rerank using fine-tuned cross-encoder
    rerank_scores = reranker_model.predict(pairs)

    # Sort results by reranker scores
    reranked_results = sorted(
        zip(documents, rerank_scores.tolist()),
        key=lambda x: x[1], reverse=True
    )

    return reranked_results

query = "OPPO 8GB 128G"
documents = [
"OPPO Reno11F 5G 8GB-256GB",
"OPPO Reno11F 5G 8GB-32GB",
"OPPO Reno11F 5G 16GB-128GB",
"Samsung galaxy 128GB",
"Samsung S24 128GB",
# ...
]

start_time = time.time()
results = search_and_rerank(query, documents, len(documents)-1)
end_time = time.time()

execution_time = (end_time - start_time)*1000
print(f"Execution time: {execution_time:.4f} mili seconds")

print(f"Query: \t\t\t\t{query}")
for res in results:
    print(f"Score: {res[-1]:.4f} | Document: {res[0]}")

Credit goes to: [email protected]