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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()
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