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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_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
):
"""Deduplicate 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 differences between two texts."""
diff = ndiff(x.split(), y.split())
return " ".join(word for word in diff if word.startswith(("+", "-")))
def load_dataset_texts(dataset_name, dataset_split, text_column):
"""Load texts from a specified dataset."""
ds = load_dataset(dataset_name, split=dataset_split)
return [example[text_column] for example in ds]
def perform_deduplication(
deduplication_type,
dataset1_name,
dataset1_split,
dataset1_text_column,
dataset2_name="",
dataset2_split="",
dataset2_text_column="",
threshold=default_threshold,
progress=gr.Progress(track_tqdm=True),
):
try:
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 = compute_embeddings(texts1, batch_size=64, progress=progress, desc="Dataset 1 embeddings")
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 = compute_embeddings(texts2, batch_size=64, progress=progress, desc="Dataset 2 embeddings")
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: 150px; overflow: auto; }") 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_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):
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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