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 # Load the model model = StaticModel.from_pretrained("minishlab/M2V_base_output") # Default parameters default_dataset_name = "sst2" default_dataset_split = "train" default_text_column = "sentence" default_threshold = 0.9 def deduplicate_embeddings( embeddings_a: np.ndarray, embeddings_b: np.ndarray = None, threshold: float = 0.9, batch_size: int = 1024, progress=None ) -> tuple[np.ndarray, dict[int, int]]: """Deduplicate embeddings within one dataset or across two datasets.""" if embeddings_b is None: reach = Reach(vectors=embeddings_a, items=[str(i) for i in range(len(embeddings_a))]) duplicate_to_original = {} results = reach.nearest_neighbor_threshold( embeddings_a, threshold=threshold, batch_size=batch_size, show_progressbar=False ) for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates", total=len(embeddings_a))): for sim_idx, _ in similar_items: sim_idx = int(sim_idx) if sim_idx != i and sim_idx not in duplicate_to_original: duplicate_to_original[sim_idx] = i deduplicated_indices = set(range(len(embeddings_a))) - set(duplicate_to_original.keys()) return deduplicated_indices, duplicate_to_original else: reach = Reach(vectors=embeddings_a, items=[str(i) for i in range(len(embeddings_a))]) duplicate_indices_in_b = [] duplicate_to_original = {} results = reach.nearest_neighbor_threshold( embeddings_b, threshold=threshold, batch_size=batch_size, show_progressbar=False ) for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates", total=len(embeddings_b))): if similar_items: duplicate_indices_in_b.append(i) duplicate_to_original[i] = int(similar_items[0][0]) return duplicate_indices_in_b, duplicate_to_original def display_word_differences(x: str, y: str) -> str: """Display word-level differences between two texts, avoiding Markdown issues.""" diff = ndiff(x.split(), y.split()) formatted_diff = "\n".join(word for word in diff if word.startswith(("+", "-"))) return f"```\n{formatted_diff}\n```" def load_dataset_texts(dataset_name: str, dataset_split: str, text_column: str) -> list[str]: """Load texts from a specified dataset and split.""" ds = load_dataset(dataset_name, split=dataset_split) return [example[text_column] for example in ds] def perform_deduplication( deduplication_type: str, dataset1_name: str, dataset1_split: str, dataset1_text_column: str, dataset2_name: str = "", dataset2_split: str = "", dataset2_text_column: str = "", threshold: float = default_threshold, progress: gr.Progress = gr.Progress(track_tqdm=True) ): """Perform deduplication on one or two datasets.""" try: threshold = float(threshold) # Load and process Dataset 1 yield "Loading Dataset 1...", "" texts1 = load_dataset_texts(dataset1_name, dataset1_split, dataset1_text_column) yield "Computing embeddings for Dataset 1...", "" embeddings1 = model.encode(texts1, show_progressbar=True) if deduplication_type == "Single dataset": # Deduplicate within Dataset 1 yield "Deduplicating within Dataset 1...", "" deduplicated_indices, duplicate_mapping = deduplicate_embeddings( embeddings1, threshold=threshold, progress=progress ) num_duplicates = len(duplicate_mapping) result_text = ( f"**Total documents:** {len(texts1)}\n\n" f"**Duplicates found:** {num_duplicates}\n\n" f"**Unique documents after deduplication:** {len(deduplicated_indices)}\n\n" ) if num_duplicates > 0: result_text += "**Sample duplicates:**\n\n" for dup_idx, orig_idx in list(duplicate_mapping.items())[:5]: orig_text = texts1[orig_idx] dup_text = texts1[dup_idx] differences = display_word_differences(orig_text, dup_text) result_text += ( f"**Original:**\n{orig_text}\n\n" f"**Duplicate:**\n{dup_text}\n\n" f"**Differences:**\n{differences}\n" + "-" * 50 + "\n\n" ) else: result_text += "No duplicates found." yield "Deduplication completed.", result_text else: # Load and process Dataset 2 yield "Loading Dataset 2...", "" texts2 = load_dataset_texts(dataset2_name, dataset2_split, dataset2_text_column) yield "Computing embeddings for Dataset 2...", "" embeddings2 = model.encode(texts2, show_progressbar=True) # Deduplicate Dataset 2 against Dataset 1 yield "Deduplicating Dataset 2 against Dataset 1...", "" duplicate_indices, duplicate_mapping = deduplicate_embeddings( embeddings1, embeddings_b=embeddings2, threshold=threshold, progress=progress ) num_duplicates = len(duplicate_indices) result_text = ( f"**Total documents in {dataset2_name}/{dataset2_split}:** {len(texts2)}\n\n" f"**Duplicates found in Dataset 2:** {num_duplicates}\n\n" f"**Unique documents after deduplication:** {len(texts2) - num_duplicates}\n\n" ) if num_duplicates > 0: result_text += "**Sample duplicates from Dataset 2:**\n\n" for idx in duplicate_indices[:5]: orig_text = texts1[duplicate_mapping[idx]] dup_text = texts2[idx] differences = display_word_differences(orig_text, dup_text) result_text += ( f"**Original (Dataset 1):**\n{orig_text}\n\n" f"**Duplicate (Dataset 2):**\n{dup_text}\n\n" f"**Differences:**\n{differences}\n" + "-" * 50 + "\n\n" ) else: result_text += "No duplicates found." yield "Deduplication completed.", result_text except Exception as e: yield f"An error occurred: {e}", "" raise e # Gradio app with stop button support with gr.Blocks(css="#status_output { height: 50px; overflow: auto; }") as demo: gr.Markdown("# Semantic Deduplication") gr.Markdown(""" This demo showcases a semantic deduplication process where we identify duplicate texts within a single dataset or across two datasets. The deduplication is based on cosine similarity between the embeddings of the texts. You can adjust the similarity threshold to control the strictness of the 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_dataset_name, label="Dataset 1 Name") dataset1_split = gr.Textbox(value=default_dataset_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_dataset_name, label="Dataset 2 Name") dataset2_split = gr.Textbox(value=default_dataset_split, label="Dataset 2 Split") dataset2_text_column = gr.Textbox(value=default_text_column, label="Text Column Name") threshold = gr.Slider(0.0, 1.0, value=default_threshold, label="Similarity Threshold") compute_button = gr.Button("Compute") stop_button = gr.Button("Stop") status_output = gr.Markdown(elem_id="status_output") result_output = gr.Markdown() def update_visibility(choice: str): return gr.update(visible=choice == "Cross-dataset") 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], ) # Stop button functionality stop_button.click(lambda: demo.stop(), None, None) 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 # # Load the model # model = StaticModel.from_pretrained("minishlab/M2V_base_output") # # Default parameters # default_dataset_name = "sst2" # default_dataset_split = "train" # default_text_column = "sentence" # default_threshold = 0.9 # def deduplicate_embeddings( # embeddings_a: np.ndarray, # embeddings_b: np.ndarray = None, # threshold: float = 0.9, # batch_size: int = 1024, # progress=None # ) -> tuple[np.ndarray, dict[int, int]]: # """ # Deduplicate embeddings within one dataset or across two datasets. # :param embeddings_a: Embeddings of Dataset 1. # :param embeddings_b: Optional, embeddings of Dataset 2. # :param threshold: Similarity threshold for deduplication. # :param batch_size: Batch size for similarity computation. # :param progress: Gradio progress tracker for feedback. # :return: Deduplicated indices and a mapping of removed indices to their original counterparts. # """ # if embeddings_b is None: # reach = Reach(vectors=embeddings_a, items=[str(i) for i in range(len(embeddings_a))]) # duplicate_to_original = {} # results = reach.nearest_neighbor_threshold( # embeddings_a, threshold=threshold, batch_size=batch_size, show_progressbar=False # ) # for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates", total=len(embeddings_a))): # for sim_idx, _ in similar_items: # sim_idx = int(sim_idx) # if sim_idx != i and sim_idx not in duplicate_to_original: # duplicate_to_original[sim_idx] = i # deduplicated_indices = set(range(len(embeddings_a))) - set(duplicate_to_original.keys()) # return deduplicated_indices, duplicate_to_original # else: # reach = Reach(vectors=embeddings_a, items=[str(i) for i in range(len(embeddings_a))]) # duplicate_indices_in_b = [] # duplicate_to_original = {} # results = reach.nearest_neighbor_threshold( # embeddings_b, threshold=threshold, batch_size=batch_size, show_progressbar=False # ) # for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates", total=len(embeddings_b))): # if similar_items: # duplicate_indices_in_b.append(i) # duplicate_to_original[i] = int(similar_items[0][0]) # return duplicate_indices_in_b, duplicate_to_original # def display_word_differences(x: str, y: str) -> str: # """ # Display the word-level differences between two texts, formatted to avoid # misinterpretation of Markdown syntax. # :param x: First text. # :param y: Second text. # :return: A string showing word-level differences, wrapped in a code block. # """ # diff = ndiff(x.split(), y.split()) # # Wrap differences in a code block to prevent interpretation as Markdown # formatted_diff = "\n".join(word for word in diff if word.startswith(("+", "-"))) # return f"```\n{formatted_diff}\n```" # # def display_word_differences(x: str, y: str) -> str: # # """ # # Display the word-level differences between two texts. # # :param x: First text. # # :param y: Second text. # # :return: A string showing word-level differences. # # """ # # diff = ndiff(x.split(), y.split()) # # return " ".join(word for word in diff if word.startswith(("+", "-"))) # def load_dataset_texts(dataset_name: str, dataset_split: str, text_column: str) -> list[str]: # """ # Load texts from a specified dataset and split. # :param dataset_name: Name of the dataset. # :param dataset_split: Split of the dataset (e.g., 'train', 'validation'). # :param text_column: Name of the text column. # :return: A list of texts from the dataset. # """ # ds = load_dataset(dataset_name, split=dataset_split) # return [example[text_column] for example in ds] # def perform_deduplication( # deduplication_type: str, # dataset1_name: str, # dataset1_split: str, # dataset1_text_column: str, # dataset2_name: str = "", # dataset2_split: str = "", # dataset2_text_column: str = "", # threshold: float = default_threshold, # progress: gr.Progress = gr.Progress(track_tqdm=True) # ): # """ # Perform deduplication on one or two datasets based on the deduplication type. # :param deduplication_type: 'Single dataset' or 'Cross-dataset'. # :param dataset1_name: Name of the first dataset. # :param dataset1_split: Split of the first dataset. # :param dataset1_text_column: Text column of the first dataset. # :param dataset2_name: Optional, name of the second dataset (for cross-dataset deduplication). # :param dataset2_split: Optional, split of the second dataset. # :param dataset2_text_column: Optional, text column of the second dataset. # :param threshold: Similarity threshold for deduplication. # :param progress: Gradio progress tracker. # :return: Status updates and result text for the Gradio interface. # """ # try: # threshold = float(threshold) # # Load and process Dataset 1 # yield "Loading Dataset 1...", "" # texts1 = load_dataset_texts(dataset1_name, dataset1_split, dataset1_text_column) # yield "Computing embeddings for Dataset 1...", "" # embeddings1 = model.encode(texts1, show_progressbar=True) # if deduplication_type == "Single dataset": # # Deduplicate within Dataset 1 # yield "Deduplicating within Dataset 1...", "" # deduplicated_indices, duplicate_mapping = deduplicate_embeddings( # embeddings1, threshold=threshold, progress=progress # ) # num_duplicates = len(duplicate_mapping) # result_text = ( # f"**Total documents:** {len(texts1)}\n\n" # f"**Duplicates found:** {num_duplicates}\n\n" # f"**Unique documents after deduplication:** {len(deduplicated_indices)}\n\n" # ) # if num_duplicates > 0: # result_text += "**Sample duplicates:**\n\n" # for dup_idx, orig_idx in list(duplicate_mapping.items())[:5]: # orig_text = texts1[orig_idx] # dup_text = texts1[dup_idx] # differences = display_word_differences(orig_text, dup_text) # result_text += ( # f"**Original:**\n{orig_text}\n\n" # f"**Duplicate:**\n{dup_text}\n\n" # f"**Differences:**\n{differences}\n" # + "-" * 50 + "\n\n" # ) # else: # result_text += "No duplicates found." # yield "Deduplication completed.", result_text # else: # # Load and process Dataset 2 # yield "Loading Dataset 2...", "" # texts2 = load_dataset_texts(dataset2_name, dataset2_split, dataset2_text_column) # yield "Computing embeddings for Dataset 2...", "" # embeddings2 = model.encode(texts2, show_progressbar=True) # # Deduplicate Dataset 2 against Dataset 1 # yield "Deduplicating Dataset 2 against Dataset 1...", "" # duplicate_indices, duplicate_mapping = deduplicate_embeddings( # embeddings1, embeddings_b=embeddings2, threshold=threshold, progress=progress # ) # num_duplicates = len(duplicate_indices) # result_text = ( # f"**Total documents in {dataset2_name}/{dataset2_split}:** {len(texts2)}\n\n" # f"**Duplicates found in Dataset 2:** {num_duplicates}\n\n" # f"**Unique documents after deduplication:** {len(texts2) - num_duplicates}\n\n" # ) # if num_duplicates > 0: # result_text += "**Sample duplicates from Dataset 2:**\n\n" # for idx in duplicate_indices[:5]: # orig_text = texts1[duplicate_mapping[idx]] # dup_text = texts2[idx] # differences = display_word_differences(orig_text, dup_text) # result_text += ( # f"**Original (Dataset 1):**\n{orig_text}\n\n" # f"**Duplicate (Dataset 2):**\n{dup_text}\n\n" # f"**Differences:**\n{differences}\n" # + "-" * 50 + "\n\n" # ) # else: # result_text += "No duplicates found." # yield "Deduplication completed.", result_text # except Exception as e: # yield f"An error occurred: {e}", "" # raise e # with gr.Blocks(css="#status_output { height: 50px; overflow: auto; }") as demo: # gr.Markdown("# Semantic Deduplication") # gr.Markdown(""" # This demo showcases semantic deduplication using Model2Vec for HuggingFace datasets. # It can be used to identify duplicate texts within a single dataset or across two datasets. # You can adjust the similarity threshold to control the strictness of the deduplication.\n # NOTE: this demo runs on a free CPU backend, so it may be slow for large datasets. For faster results, please run the code locally. # """) # deduplication_type = gr.Radio( # choices=["Single dataset", "Cross-dataset"], # label="Deduplication Type", # value="Single dataset", # ) # with gr.Row(): # dataset1_name = gr.Textbox(value=default_dataset_name, label="Dataset 1 Name") # dataset1_split = gr.Textbox(value=default_dataset_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_dataset_name, label="Dataset 2 Name") # dataset2_split = gr.Textbox(value=default_dataset_split, label="Dataset 2 Split") # dataset2_text_column = gr.Textbox(value=default_text_column, label="Text Column Name") # threshold = gr.Slider(0.0, 1.0, value=default_threshold, label="Similarity Threshold") # compute_button = gr.Button("Compute") # status_output = gr.Markdown(elem_id="status_output") # result_output = gr.Markdown() # def update_visibility(choice: str): # return gr.update(visible=choice == "Cross-dataset") # 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()