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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
# Load the model
model = StaticModel.from_pretrained("minishlab/M2V_base_output")
# Default parameters
default_dataset_name = "sst2"
default_dataset1_split = "train" # Default for the first dataset is "train"
default_dataset2_split = "test" # Default for the second dataset is "test"
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())
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.
:param dataset_name: Name of the dataset.
:param dataset_split: Split of the dataset (e.g., 'train', 'validation', 'test').
: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
# 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 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=["Cross-dataset", "Single dataset"], # Swapped "Cross-dataset" to the left
label="Deduplication Type",
value="Cross-dataset", # Set "Cross-dataset" as the default value
)
with gr.Row():
dataset1_name = gr.Textbox(value=default_dataset_name, label="Dataset 1 Name")
dataset1_split = gr.Textbox(value=default_dataset1_split, label="Dataset 1 Split") # Default split is "train"
dataset1_text_column = gr.Textbox(value=default_text_column, label="Text Column Name")
dataset2_inputs = gr.Column(visible=True) # Make dataset2_inputs visible by default
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_dataset2_split, label="Dataset 2 Split") # Default split is "test"
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")
with gr.Row(): # Placing the button in the same row for better alignment
compute_button = gr.Button("Deduplicate")
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()
# 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())
# 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.
# :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
# # 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 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=["Cross-dataset", "Single dataset"], # Swapped "Cross-dataset" to the left
# label="Deduplication Type",
# value="Cross-dataset", # Set "Cross-dataset" as the default value
# )
# 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=True) # Make dataset2_inputs visible by default
# 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")
# with gr.Row(): # Placing the button in the same row for better alignment
# compute_button = gr.Button("Deduplicate")
# 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()
# # 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())
# # 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.
# # :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
# # # 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 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="Cross-dataset", # Set "Cross-dataset" as the default value
# # )
# # 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=True) # Make dataset2_inputs visible by default
# # 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("Deduplicate")
# # 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()
# # # 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())
# # # 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.
# # # :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
# # # # 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 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("Deduplicate")
# # # 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()
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