import gradio as gr from datasets import load_dataset import numpy as np from model2vec import StaticModel from reach import Reach from difflib import ndiff import concurrent.futures # Load the model at startup model = StaticModel.from_pretrained("minishlab/M2V_base_output") # Update default dataset to 'sst2' and set default threshold to 0.9 default_dataset1_name = "sst2" default_dataset1_split = "train" default_dataset2_name = "sst2" default_dataset2_split = "validation" default_text_column = "sentence" default_threshold = 0.9 # Load the default datasets at startup ds_default1 = load_dataset(default_dataset1_name, split=default_dataset1_split) ds_default2 = load_dataset(default_dataset2_name, split=default_dataset2_split) def batch_iterable(iterable, batch_size): """Helper function to create batches from an iterable.""" for i in range(0, len(iterable), batch_size): yield iterable[i:i + batch_size] def display_word_differences(x: str, y: str) -> str: diff = ndiff(x.split(), y.split()) return " ".join([word for word in diff if word.startswith(('+', '-'))]) def perform_deduplication( deduplication_type, dataset1_name, dataset1_split, dataset1_text_column, dataset2_name="", dataset2_split="", dataset2_text_column="", threshold=default_threshold, progress=gr.Progress(track_tqdm=True) ): try: # Convert threshold to float threshold = float(threshold) # Initialize status message status = "" if deduplication_type == "Single dataset": # Load Dataset 1 status = "Loading Dataset 1..." yield status, "" if dataset1_name == default_dataset1_name and dataset1_split == default_dataset1_split: ds = ds_default1 else: ds = load_dataset(dataset1_name, split=dataset1_split) # Extract texts status = "Extracting texts from Dataset 1..." yield status, "" texts = [example[dataset1_text_column] for example in ds] # Compute embeddings status = "Computing embeddings for Dataset 1..." yield status, "" embeddings = [] batch_size = 64 total_batches = (len(texts) + batch_size - 1) // batch_size def compute_embeddings(): for batch_texts in progress.tqdm(batch_iterable(texts, batch_size), desc="Computing embeddings for Dataset 1", total=total_batches): batch_embeddings = model.encode(batch_texts, show_progressbar=False) embeddings.append(batch_embeddings) return np.concatenate(embeddings, axis=0) with concurrent.futures.ThreadPoolExecutor() as executor: future = executor.submit(compute_embeddings) while not future.done(): pass # Wait for embeddings to be computed embedding_matrix = future.result() # Deduplicate status = "Deduplicating embeddings..." yield status, "" deduplicated_indices, duplicate_to_original_mapping = deduplicate( embedding_matrix, threshold, progress=progress ) # Prepare the results num_duplicates = len(duplicate_to_original_mapping) num_total = len(texts) num_deduplicated = len(deduplicated_indices) result_text = f"**Total documents:** {num_total}\n" result_text += f"**Number of duplicates found:** {num_duplicates}\n" result_text += f"**Number of unique documents after deduplication:** {num_deduplicated}\n\n" # Show deduplicated examples if num_duplicates > 0: result_text += "**Examples of duplicates found:**\n\n" num_examples = min(5, num_duplicates) for duplicate_idx, original_idx in list(duplicate_to_original_mapping.items())[:num_examples]: original_text = texts[original_idx] duplicate_text = texts[duplicate_idx] differences = display_word_differences(original_text, duplicate_text) result_text += f"**Original text:**\n{original_text}\n\n" result_text += f"**Duplicate text:**\n{duplicate_text}\n\n" result_text += f"**Differences:**\n{differences}\n" result_text += "-" * 50 + "\n\n" else: result_text += "No duplicates found." # Final status status = "Deduplication completed." yield status, result_text elif deduplication_type == "Cross-dataset": # Similar code for cross-dataset deduplication # Implement similar logic as above for cross-dataset pass except Exception as e: yield f"An error occurred: {e}", "" raise e def deduplicate(embedding_matrix: np.ndarray, threshold: float, batch_size: int = 1024, progress=None) -> tuple[np.ndarray, dict[int, int]]: """ Deduplicate embeddings and return the deduplicated indices and a mapping of removed indices to their corresponding original indices. """ # Building the index progress(0, desc="Building search index...") reach = Reach(vectors=embedding_matrix, items=[str(i) for i in range(len(embedding_matrix))]) deduplicated_indices = set(range(len(embedding_matrix))) duplicate_to_original_mapping = {} # Finding nearest neighbors progress(0, desc="Finding nearest neighbors...") results = reach.nearest_neighbor_threshold( embedding_matrix, threshold=threshold, batch_size=batch_size, show_progressbar=False # Disable internal progress bar ) # Processing duplicates with a progress bar total_items = len(embedding_matrix) for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates", total=total_items)): if i not in deduplicated_indices: continue similar_indices = [int(item[0]) for item in similar_items if int(item[0]) != i] for sim_idx in similar_indices: if sim_idx in deduplicated_indices: deduplicated_indices.remove(sim_idx) duplicate_to_original_mapping[sim_idx] = i return np.array(list(deduplicated_indices)), duplicate_to_original_mapping with gr.Blocks() as demo: gr.Markdown("# Semantic Deduplication") deduplication_type = gr.Radio( choices=["Single dataset", "Cross-dataset"], label="Deduplication Type", value="Single dataset" ) with gr.Row(): dataset1_name = gr.Textbox(value=default_dataset1_name, label="Dataset 1 Name") dataset1_split = gr.Textbox(value=default_dataset1_split, label="Dataset 1 Split") dataset1_text_column = gr.Textbox(value=default_text_column, label="Text Column Name") dataset2_inputs = gr.Column(visible=False) with dataset2_inputs: gr.Markdown("### Dataset 2") with gr.Row(): dataset2_name = gr.Textbox(value=default_dataset2_name, label="Dataset 2 Name") dataset2_split = gr.Textbox(value=default_dataset2_split, label="Dataset 2 Split") dataset2_text_column = gr.Textbox(value=default_text_column, label="Text Column Name") threshold = gr.Slider( minimum=0.0, maximum=1.0, value=default_threshold, label="Similarity Threshold" ) compute_button = gr.Button("Compute") status_output = gr.Markdown() result_output = gr.Markdown() # Function to update the visibility of dataset2_inputs def update_visibility(deduplication_type_value): if deduplication_type_value == "Cross-dataset": return gr.update(visible=True) else: return gr.update(visible=False) deduplication_type.change( update_visibility, inputs=deduplication_type, outputs=dataset2_inputs ) compute_button.click( fn=perform_deduplication, inputs=[ deduplication_type, dataset1_name, dataset1_split, dataset1_text_column, dataset2_name, dataset2_split, dataset2_text_column, threshold ], outputs=[status_output, result_output] ) demo.launch()