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 sys import tqdm # 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 deduplicate(embedding_matrix: np.ndarray, threshold: float, batch_size: int = 1024) -> 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 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 results = reach.nearest_neighbor_threshold( embedding_matrix, threshold=threshold, batch_size=batch_size, show_progressbar=True # Allow internal progress bar ) # Processing duplicates for i, similar_items in enumerate(results): 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 def deduplicate_across_datasets(embedding_matrix_1: np.ndarray, embedding_matrix_2: np.ndarray, threshold: float, batch_size: int = 1024) -> tuple[list[int], dict[int, int]]: """ Deduplicate embeddings across two datasets and return the indices of duplicates between them. """ # Building the index from Dataset 1 reach = Reach(vectors=embedding_matrix_1, items=[str(i) for i in range(len(embedding_matrix_1))]) duplicate_indices_in_test = [] duplicate_to_original_mapping = {} # Finding nearest neighbors between datasets results = reach.nearest_neighbor_threshold( embedding_matrix_2, threshold=threshold, batch_size=batch_size, show_progressbar=True # Allow internal progress bar ) # Processing duplicates for i, similar_items in enumerate(results): similar_indices = [int(item[0]) for item in similar_items if item[1] >= threshold] if similar_indices: duplicate_indices_in_test.append(i) duplicate_to_original_mapping[i] = similar_indices[0] return duplicate_indices_in_test, duplicate_to_original_mapping 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) ): # Deep Monkey-Patching of tqdm original_tqdm = tqdm.tqdm tqdm.tqdm = progress.tqdm for mod_name in list(sys.modules.keys()): if 'tqdm' in mod_name: sys.modules[mod_name].tqdm = progress.tqdm try: # Convert threshold to float threshold = float(threshold) if deduplication_type == "Single dataset": # Load Dataset 1 gr.print("Loading Dataset 1...") 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 gr.print("Extracting texts from Dataset 1...") texts = [example[dataset1_text_column] for example in ds] # Compute embeddings gr.print("Computing embeddings for Dataset 1...") embedding_matrix = model.encode(texts, show_progressbar=True) # Enable internal progress bar # Deduplicate gr.print("Deduplicating embeddings...") deduplicated_indices, duplicate_to_original_mapping = deduplicate( embedding_matrix, threshold ) # 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." return result_text elif deduplication_type == "Cross-dataset": # Load Dataset 1 gr.print("Loading Dataset 1...") if dataset1_name == default_dataset1_name and dataset1_split == default_dataset1_split: ds1 = ds_default1 else: ds1 = load_dataset(dataset1_name, split=dataset1_split) # Load Dataset 2 gr.print("Loading Dataset 2...") if dataset2_name == default_dataset2_name and dataset2_split == default_dataset2_split: ds2 = ds_default2 else: ds2 = load_dataset(dataset2_name, split=dataset2_split) # Extract texts from Dataset 1 gr.print("Extracting texts from Dataset 1...") texts1 = [example[dataset1_text_column] for example in ds1] # Extract texts from Dataset 2 gr.print("Extracting texts from Dataset 2...") texts2 = [example[dataset2_text_column] for example in ds2] # Compute embeddings for Dataset 1 gr.print("Computing embeddings for Dataset 1...") embedding_matrix1 = model.encode(texts1, show_progressbar=True) # Compute embeddings for Dataset 2 gr.print("Computing embeddings for Dataset 2...") embedding_matrix2 = model.encode(texts2, show_progressbar=True) # Deduplicate across datasets gr.print("Deduplicating embeddings across datasets...") duplicate_indices_in_ds2, duplicate_to_original_mapping = deduplicate_across_datasets( embedding_matrix1, embedding_matrix2, threshold ) num_duplicates = len(duplicate_indices_in_ds2) num_total_ds2 = len(texts2) num_unique_ds2 = num_total_ds2 - num_duplicates result_text = f"**Total documents in {dataset2_name}/{dataset2_split}:** {num_total_ds2}\n" result_text += f"**Number of duplicates found in {dataset2_name}/{dataset2_split}:** {num_duplicates}\n" result_text += f"**Number of unique documents in {dataset2_name}/{dataset2_split} after deduplication:** {num_unique_ds2}\n\n" # Show deduplicated examples if num_duplicates > 0: result_text += "**Examples of duplicates found in Dataset 2:**\n\n" num_examples = min(5, num_duplicates) for duplicate_idx in duplicate_indices_in_ds2[:num_examples]: original_idx = duplicate_to_original_mapping[duplicate_idx] original_text = texts1[original_idx] duplicate_text = texts2[duplicate_idx] differences = display_word_differences(original_text, duplicate_text) result_text += f"**Original text (Dataset 1):**\n{original_text}\n\n" result_text += f"**Duplicate text (Dataset 2):**\n{duplicate_text}\n\n" result_text += f"**Differences:**\n{differences}\n" result_text += "-" * 50 + "\n\n" else: result_text += "No duplicates found." return result_text finally: # Restore original tqdm tqdm.tqdm = original_tqdm for mod_name in list(sys.modules.keys()): if 'tqdm' in mod_name: sys.modules[mod_name].tqdm = original_tqdm 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") 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=output ) demo.launch() # 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 sys # import tqdm # # 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 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. # """ # # Update progress to indicate 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=True # Allow 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 # def deduplicate_across_datasets(embedding_matrix_1: np.ndarray, embedding_matrix_2: np.ndarray, threshold: float, batch_size: int = 1024, progress=None) -> tuple[list[int], dict[int, int]]: # """ # Deduplicate embeddings across two datasets and return the indices of duplicates between them. # """ # # Update progress to indicate building the index # progress(0, desc="Building search index from Dataset 1...") # reach = Reach(vectors=embedding_matrix_1, items=[str(i) for i in range(len(embedding_matrix_1))]) # duplicate_indices_in_test = [] # duplicate_to_original_mapping = {} # # Finding nearest neighbors between datasets # progress(0, desc="Finding nearest neighbors between datasets...") # results = reach.nearest_neighbor_threshold( # embedding_matrix_2, # threshold=threshold, # batch_size=batch_size, # show_progressbar=True # Allow internal progress bar # ) # total_items = len(embedding_matrix_2) # # Processing duplicates with a progress bar # for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates across datasets", total=total_items)): # similar_indices = [int(item[0]) for item in similar_items if item[1] >= threshold] # if similar_indices: # duplicate_indices_in_test.append(i) # duplicate_to_original_mapping[i] = similar_indices[0] # return duplicate_indices_in_test, duplicate_to_original_mapping # 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) # ): # # Monkey-patch tqdm # original_tqdm = tqdm.tqdm # original_reach_tqdm = Reach.__dict__['tqdm'] if 'tqdm' in Reach.__dict__ else None # tqdm.tqdm = progress.tqdm # sys.modules['tqdm'].tqdm = progress.tqdm # sys.modules['tqdm.auto'].tqdm = progress.tqdm # Reach.tqdm = progress.tqdm # Monkey-patch reach's tqdm # try: # # Convert threshold to float # threshold = float(threshold) # if deduplication_type == "Single dataset": # # Load Dataset 1 # progress(0, desc="Loading Dataset 1...") # 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 # progress(0, desc="Extracting texts from Dataset 1...") # texts = [example[dataset1_text_column] for example in ds] # # Compute embeddings # progress(0, desc="Computing embeddings for Dataset 1...") # embedding_matrix = model.encode(texts, show_progressbar=True) # Enable internal progress bar # # Deduplicate # result_text = deduplicate_and_prepare_results_single( # embedding_matrix, texts, threshold, progress # ) # return result_text # elif deduplication_type == "Cross-dataset": # # Load Dataset 1 # progress(0, desc="Loading Dataset 1...") # if dataset1_name == default_dataset1_name and dataset1_split == default_dataset1_split: # ds1 = ds_default1 # else: # ds1 = load_dataset(dataset1_name, split=dataset1_split) # # Load Dataset 2 # progress(0, desc="Loading Dataset 2...") # if dataset2_name == default_dataset2_name and dataset2_split == default_dataset2_split: # ds2 = ds_default2 # else: # ds2 = load_dataset(dataset2_name, split=dataset2_split) # # Extract texts from Dataset 1 # progress(0, desc="Extracting texts from Dataset 1...") # texts1 = [example[dataset1_text_column] for example in ds1] # # Extract texts from Dataset 2 # progress(0, desc="Extracting texts from Dataset 2...") # texts2 = [example[dataset2_text_column] for example in ds2] # # Compute embeddings for Dataset 1 # progress(0, desc="Computing embeddings for Dataset 1...") # embedding_matrix1 = model.encode(texts1, show_progressbar=True) # # Compute embeddings for Dataset 2 # progress(0, desc="Computing embeddings for Dataset 2...") # embedding_matrix2 = model.encode(texts2, show_progressbar=True) # # Deduplicate across datasets # result_text = deduplicate_and_prepare_results_cross( # embedding_matrix1, embedding_matrix2, texts1, texts2, threshold, progress, dataset2_name, dataset2_split # ) # return result_text # finally: # # Restore original tqdm # tqdm.tqdm = original_tqdm # sys.modules['tqdm'].tqdm = original_tqdm # sys.modules['tqdm.auto'].tqdm = original_tqdm # # Restore reach's original tqdm # if original_reach_tqdm is not None: # Reach.tqdm = original_reach_tqdm # else: # del Reach.tqdm # If it wasn't originally in Reach's __dict__ # def deduplicate_and_prepare_results_single(embedding_matrix, texts, threshold, progress): # # Deduplicate # 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." # return result_text # def deduplicate_and_prepare_results_cross(embedding_matrix1, embedding_matrix2, texts1, texts2, threshold, progress, dataset2_name, dataset2_split): # # Deduplicate across datasets # duplicate_indices_in_ds2, duplicate_to_original_mapping = deduplicate_across_datasets( # embedding_matrix1, embedding_matrix2, threshold, progress=progress # ) # num_duplicates = len(duplicate_indices_in_ds2) # num_total_ds2 = len(texts2) # num_unique_ds2 = num_total_ds2 - num_duplicates # result_text = f"**Total documents in {dataset2_name}/{dataset2_split}:** {num_total_ds2}\n" # result_text += f"**Number of duplicates found in {dataset2_name}/{dataset2_split}:** {num_duplicates}\n" # result_text += f"**Number of unique documents in {dataset2_name}/{dataset2_split} after deduplication:** {num_unique_ds2}\n\n" # # Show deduplicated examples # if num_duplicates > 0: # result_text += "**Examples of duplicates found in Dataset 2:**\n\n" # num_examples = min(5, num_duplicates) # for duplicate_idx in duplicate_indices_in_ds2[:num_examples]: # original_idx = duplicate_to_original_mapping[duplicate_idx] # original_text = texts1[original_idx] # duplicate_text = texts2[duplicate_idx] # differences = display_word_differences(original_text, duplicate_text) # result_text += f"**Original text (Dataset 1):**\n{original_text}\n\n" # result_text += f"**Duplicate text (Dataset 2):**\n{duplicate_text}\n\n" # result_text += f"**Differences:**\n{differences}\n" # result_text += "-" * 50 + "\n\n" # else: # result_text += "No duplicates found." # return result_text # 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") # 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=output # ) # demo.launch() # 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 sys # import tqdm # # Load the model at startup # model = StaticModel.from_pretrained("minishlab/M2V_base_output") # # Load the default datasets at startup # default_dataset1_name = "ag_news" # default_dataset1_split = "train" # default_dataset2_name = "ag_news" # default_dataset2_split = "test" # ds_default1 = load_dataset(default_dataset1_name, split=default_dataset1_split) # ds_default2 = load_dataset(default_dataset2_name, split=default_dataset2_split) # 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. # """ # 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 = {} # results = reach.nearest_neighbor_threshold( # embedding_matrix, # threshold=threshold, # batch_size=batch_size, # show_progressbar=True # Allow internal progress bar # ) # # Process duplicates # for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates", total=len(embedding_matrix))): # 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 # def deduplicate_across_datasets(embedding_matrix_1: np.ndarray, embedding_matrix_2: np.ndarray, threshold: float, batch_size: int = 1024, progress=None) -> tuple[list[int], dict[int, int]]: # """ # Deduplicate embeddings across two datasets and return the indices of duplicates between them. # """ # reach = Reach(vectors=embedding_matrix_1, items=[str(i) for i in range(len(embedding_matrix_1))]) # duplicate_indices_in_test = [] # duplicate_to_original_mapping = {} # results = reach.nearest_neighbor_threshold( # embedding_matrix_2, # threshold=threshold, # batch_size=batch_size, # show_progressbar=True # Allow internal progress bar # ) # for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates across datasets", total=len(embedding_matrix_2))): # similar_indices = [int(item[0]) for item in similar_items if item[1] >= threshold] # if similar_indices: # duplicate_indices_in_test.append(i) # duplicate_to_original_mapping[i] = similar_indices[0] # return duplicate_indices_in_test, duplicate_to_original_mapping # 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=0.8, # progress=gr.Progress(track_tqdm=True) # ): # # Monkey-patch tqdm # original_tqdm = tqdm.tqdm # original_reach_tqdm = Reach.__dict__['tqdm'] if 'tqdm' in Reach.__dict__ else None # tqdm.tqdm = progress.tqdm # sys.modules['tqdm'].tqdm = progress.tqdm # sys.modules['tqdm.auto'].tqdm = progress.tqdm # Reach.tqdm = progress.tqdm # Monkey-patch reach's tqdm # try: # # Convert threshold to float # threshold = float(threshold) # if deduplication_type == "Single dataset": # # Check if the dataset is the default one # 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 # texts = [example[dataset1_text_column] for example in ds] # # Compute embeddings # embedding_matrix = model.encode(texts, show_progressbar=True) # Enable internal progress bar # # Deduplicate # 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 # 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" # return result_text # elif deduplication_type == "Cross-dataset": # # Dataset 1 # if dataset1_name == default_dataset1_name and dataset1_split == default_dataset1_split: # ds1 = ds_default1 # else: # ds1 = load_dataset(dataset1_name, split=dataset1_split) # # Dataset 2 # if dataset2_name == default_dataset2_name and dataset2_split == default_dataset2_split: # ds2 = ds_default2 # else: # ds2 = load_dataset(dataset2_name, split=dataset2_split) # # Extract texts # texts1 = [example[dataset1_text_column] for example in ds1] # texts2 = [example[dataset2_text_column] for example in ds2] # # Compute embeddings # embedding_matrix1 = model.encode(texts1, show_progressbar=True) # Enable internal progress bar # embedding_matrix2 = model.encode(texts2, show_progressbar=True) # Enable internal progress bar # # Deduplicate across datasets # duplicate_indices_in_ds2, duplicate_to_original_mapping = deduplicate_across_datasets( # embedding_matrix1, embedding_matrix2, threshold, progress=progress) # num_duplicates = len(duplicate_indices_in_ds2) # num_total_ds2 = len(texts2) # num_unique_ds2 = num_total_ds2 - num_duplicates # result_text = f"**Total documents in {dataset2_name}/{dataset2_split}:** {num_total_ds2}\n" # result_text += f"**Number of duplicates found in {dataset2_name}/{dataset2_split}:** {num_duplicates}\n" # result_text += f"**Number of unique documents in {dataset2_name}/{dataset2_split} after deduplication:** {num_unique_ds2}\n\n" # # Show deduplicated examples # result_text += "**Examples of duplicates found in Dataset 2:**\n\n" # num_examples = min(5, num_duplicates) # for duplicate_idx in duplicate_indices_in_ds2[:num_examples]: # original_idx = duplicate_to_original_mapping[duplicate_idx] # original_text = texts1[original_idx] # duplicate_text = texts2[duplicate_idx] # differences = display_word_differences(original_text, duplicate_text) # result_text += f"**Original text (Dataset 1):**\n{original_text}\n\n" # result_text += f"**Duplicate text (Dataset 2):**\n{duplicate_text}\n\n" # result_text += f"**Differences:**\n{differences}\n" # result_text += "-" * 50 + "\n\n" # return result_text # finally: # # Restore original tqdm # tqdm.tqdm = original_tqdm # sys.modules['tqdm'].tqdm = original_tqdm # sys.modules['tqdm.auto'].tqdm = original_tqdm # # Restore reach's original tqdm # if original_reach_tqdm is not None: # Reach.tqdm = original_reach_tqdm # else: # del Reach.tqdm # If it wasn't originally in Reach's __dict__ # 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="ag_news", label="Dataset 1 Name") # dataset1_split = gr.Textbox(value="train", label="Dataset 1 Split") # dataset1_text_column = gr.Textbox(value="text", 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="ag_news", label="Dataset 2 Name") # dataset2_split = gr.Textbox(value="test", label="Dataset 2 Split") # dataset2_text_column = gr.Textbox(value="text", label="Text Column Name") # threshold = gr.Slider( # minimum=0.0, # maximum=1.0, # value=0.8, # label="Similarity Threshold" # ) # compute_button = gr.Button("Compute") # 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=output # ) # demo.launch() # # 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 sys # # import tqdm # # # Load the model at startup # # model = StaticModel.from_pretrained("minishlab/M2V_base_output") # # # Load the default datasets at startup # # default_dataset1_name = "ag_news" # # default_dataset1_split = "train" # # default_dataset2_name = "ag_news" # # default_dataset2_split = "test" # # ds_default1 = load_dataset(default_dataset1_name, split=default_dataset1_split) # # ds_default2 = load_dataset(default_dataset2_name, split=default_dataset2_split) # # 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. # # """ # # 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 = {} # # results = reach.nearest_neighbor_threshold( # # embedding_matrix, # # threshold=threshold, # # batch_size=batch_size, # # show_progressbar=True # Allow internal progress bar # # ) # # # Process duplicates # # for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates")): # # 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 # # def deduplicate_across_datasets(embedding_matrix_1: np.ndarray, embedding_matrix_2: np.ndarray, threshold: float, batch_size: int = 1024, progress=None) -> tuple[list[int], dict[int, int]]: # # """ # # Deduplicate embeddings across two datasets and return the indices of duplicates between them. # # """ # # reach = Reach(vectors=embedding_matrix_1, items=[str(i) for i in range(len(embedding_matrix_1))]) # # duplicate_indices_in_test = [] # # duplicate_to_original_mapping = {} # # results = reach.nearest_neighbor_threshold( # # embedding_matrix_2, # # threshold=threshold, # # batch_size=batch_size, # # show_progressbar=True # Allow internal progress bar # # ) # # # Process duplicates # # for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates across datasets")): # # similar_indices = [int(item[0]) for item in similar_items if item[1] >= threshold] # # if similar_indices: # # duplicate_indices_in_test.append(i) # # duplicate_to_original_mapping[i] = similar_indices[0] # # return duplicate_indices_in_test, duplicate_to_original_mapping # # 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=0.8, # # progress=gr.Progress(track_tqdm=True) # # ): # # # Monkey-patch tqdm # # original_tqdm = tqdm.tqdm # # tqdm.tqdm = progress.tqdm # # sys.modules['tqdm'].tqdm = progress.tqdm # # sys.modules['tqdm.auto'].tqdm = progress.tqdm # # try: # # # Convert threshold to float # # threshold = float(threshold) # # if deduplication_type == "Single dataset": # # # Check if the dataset is the default one # # 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 # # texts = [example[dataset1_text_column] for example in ds] # # # Compute embeddings # # embedding_matrix = model.encode(texts, show_progressbar=True) # Enable internal progress bar # # # Deduplicate # # 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 # # 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" # # return result_text # # elif deduplication_type == "Cross-dataset": # # # Dataset 1 # # if dataset1_name == default_dataset1_name and dataset1_split == default_dataset1_split: # # ds1 = ds_default1 # # else: # # ds1 = load_dataset(dataset1_name, split=dataset1_split) # # # Dataset 2 # # if dataset2_name == default_dataset2_name and dataset2_split == default_dataset2_split: # # ds2 = ds_default2 # # else: # # ds2 = load_dataset(dataset2_name, split=dataset2_split) # # # Extract texts # # texts1 = [example[dataset1_text_column] for example in ds1] # # texts2 = [example[dataset2_text_column] for example in ds2] # # # Compute embeddings # # embedding_matrix1 = model.encode(texts1, show_progressbar=True) # Enable internal progress bar # # embedding_matrix2 = model.encode(texts2, show_progressbar=True) # Enable internal progress bar # # # Deduplicate across datasets # # duplicate_indices_in_ds2, duplicate_to_original_mapping = deduplicate_across_datasets(embedding_matrix1, embedding_matrix2, threshold, progress=progress) # # num_duplicates = len(duplicate_indices_in_ds2) # # num_total_ds2 = len(texts2) # # num_unique_ds2 = num_total_ds2 - num_duplicates # # result_text = f"**Total documents in {dataset2_name}/{dataset2_split}:** {num_total_ds2}\n" # # result_text += f"**Number of duplicates found in {dataset2_name}/{dataset2_split}:** {num_duplicates}\n" # # result_text += f"**Number of unique documents in {dataset2_name}/{dataset2_split} after deduplication:** {num_unique_ds2}\n\n" # # # Show deduplicated examples # # result_text += "**Examples of duplicates found in Dataset 2:**\n\n" # # num_examples = min(5, num_duplicates) # # for duplicate_idx in duplicate_indices_in_ds2[:num_examples]: # # original_idx = duplicate_to_original_mapping[duplicate_idx] # # original_text = texts1[original_idx] # # duplicate_text = texts2[duplicate_idx] # # differences = display_word_differences(original_text, duplicate_text) # # result_text += f"**Original text (Dataset 1):**\n{original_text}\n\n" # # result_text += f"**Duplicate text (Dataset 2):**\n{duplicate_text}\n\n" # # result_text += f"**Differences:**\n{differences}\n" # # result_text += "-" * 50 + "\n\n" # # return result_text # # finally: # # # Restore original tqdm # # tqdm.tqdm = original_tqdm # # sys.modules['tqdm'].tqdm = original_tqdm # # sys.modules['tqdm.auto'].tqdm = original_tqdm # # 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="ag_news", label="Dataset 1 Name") # # dataset1_split = gr.Textbox(value="train", label="Dataset 1 Split") # # dataset1_text_column = gr.Textbox(value="text", 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="ag_news", label="Dataset 2 Name") # # dataset2_split = gr.Textbox(value="test", label="Dataset 2 Split") # # dataset2_text_column = gr.Textbox(value="text", label="Text Column Name") # # threshold = gr.Slider( # # minimum=0.0, # # maximum=1.0, # # value=0.8, # # label="Similarity Threshold" # # ) # # compute_button = gr.Button("Compute") # # 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=output # # ) # # demo.launch()