Updated app with code for deduplication

app.py

@@ -1,7 +1,182 @@
+# import gradio as gr
+
+# def greet(name):
+# return "Hello " + name + "!!"
+
+# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# demo.launch()
+
+
 import gradio as gr
+from datasets import load_dataset
+import numpy as np
+from model2vec import StaticModel
+from reach import Reach
+from tqdm import tqdm
+
+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.
+ """
+ reach = Reach(vectors=embedding_matrix, items=[str(i) for i in range(len(embedding_matrix))])
+
+ # Use a set for deduplicated indices and keep track of duplicates
+ deduplicated_indices = set(range(len(embedding_matrix))) # Start with all indices as deduplicated
+ duplicate_to_original_mapping = {}
+
+ results = reach.nearest_neighbor_threshold(
+ embedding_matrix, 
+ threshold=threshold, 
+ batch_size=batch_size, 
+ show_progressbar=True
+ )
+
+ # Process duplicates
+ for i, similar_items in enumerate(tqdm(results)):
+ if i not in deduplicated_indices:
+ continue # Skip already marked duplicates
+
+ # Similar items are returned as (index, score), we are only interested in the index
+ similar_indices = [int(item[0]) for item in similar_items if int(item[0]) != i]
+
+ # Mark similar documents as duplicates and map them to the original
+ for sim_idx in similar_indices:
+ if sim_idx in deduplicated_indices:
+ deduplicated_indices.remove(sim_idx)
+ duplicate_to_original_mapping[sim_idx] = i # Map duplicate to original
+
+ 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.
+ """
+ reach = Reach(vectors=embedding_matrix_1, items=[str(i) for i in range(len(embedding_matrix_1))])
+
+ # Keep track of duplicates in the second dataset
+ duplicate_indices_in_test = []
+ duplicate_to_original_mapping = {}
+
+ # Find nearest neighbors from the test set in the train set
+ results = reach.nearest_neighbor_threshold(
+ embedding_matrix_2, 
+ threshold=threshold, 
+ batch_size=batch_size, 
+ show_progressbar=True
+ )
+
+ # Process duplicates
+ for i, similar_items in enumerate(tqdm(results)):
+ # Similar items are returned as (index, score), we are only interested in the index
+ similar_indices = [int(item[0]) for item in similar_items if item[1] >= threshold] # Keep those above the threshold
+
+ # If we find a similar item in the train set, mark it as a duplicate
+ if similar_indices:
+ duplicate_indices_in_test.append(i)
+ duplicate_to_original_mapping[i] = similar_indices[0] # Map duplicate in test to original in train
+
+ return duplicate_indices_in_test, duplicate_to_original_mapping
 
-def greet(name):
- return "Hello " + name + "!!"
+def perform_deduplication(
+ deduplication_type,
+ dataset1_name,
+ dataset1_split,
+ dataset2_name,
+ dataset2_split,
+ threshold
+):
+ # Convert threshold to float
+ threshold = float(threshold)
+ 
+ if deduplication_type == "Single dataset":
+ # Load the dataset
+ ds = load_dataset(dataset1_name, split=dataset1_split)
+ 
+ # Extract texts
+ texts = [example['text'] for example in ds]
+ 
+ # Compute embeddings
+ model = StaticModel.from_pretrained("minishlab/M2V_base_output")
+ embedding_matrix = model.encode(texts, show_progressbar=True)
+ 
+ # Deduplicate
+ 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"
+ result_text += f"**Deduplicated indices:** {deduplicated_indices.tolist()}\n\n"
+ result_text += f"**Duplicate to original mapping:** {duplicate_to_original_mapping}\n"
+ 
+ return result_text
+ 
+ elif deduplication_type == "Cross-dataset":
+ # Load datasets
+ ds1 = load_dataset(dataset1_name, split=dataset1_split)
+ ds2 = load_dataset(dataset2_name, split=dataset2_split)
+ 
+ # Extract texts
+ texts1 = [example['text'] for example in ds1]
+ texts2 = [example['text'] for example in ds2]
+ 
+ # Compute embeddings
+ model = StaticModel.from_pretrained("minishlab/M2V_base_output")
+ embedding_matrix1 = model.encode(texts1, show_progressbar=True)
+ embedding_matrix2 = model.encode(texts2, show_progressbar=True)
+ 
+ # Deduplicate 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"
+ result_text += f"**Duplicate indices in {dataset2_name}/{dataset2_split}:** {duplicate_indices_in_ds2}\n\n"
+ result_text += f"**Duplicate to original mapping:** {duplicate_to_original_mapping}\n"
+ 
+ return result_text
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+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")
+ 
+ dataset2_row = gr.Row(visible=False)
+ with dataset2_row:
+ dataset2_name = gr.Textbox(value="ag_news", label="Dataset 2 Name")
+ dataset2_split = gr.Textbox(value="test", label="Dataset 2 Split")
+ 
+ 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_row
+ def update_visibility(deduplication_type):
+ if deduplication_type == "Cross-dataset":
+ return {dataset2_row: gr.update(visible=True)}
+ else:
+ return {dataset2_row: gr.update(visible=False)}
+ 
+ deduplication_type.change(update_visibility, inputs=deduplication_type, outputs=[dataset2_row])
+ 
+ compute_button.click(
+ fn=perform_deduplication,
+ inputs=[deduplication_type, dataset1_name, dataset1_split, dataset2_name, dataset2_split, threshold],
+ outputs=output
+ )
+ 
+demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+reach < 5
+model2vec
+numpy
+datasets
+tqdm
+