Transformers and Quadrant: Revolutionizing Data Integration for NLP Tasks

Community Article Published February 21, 2024

Introduction

In today's era of big data, handling and processing textual information efficiently is crucial for various NLP tasks. By combining state-of-the-art transformer models with vector databases like Quadrant, researchers can seamlessly integrate datasets, enabling swift access to embeddings for downstream applications. This fusion not only streamlines the data pipeline but also enhances the efficacy of NLP tasks, from semantic similarity searches to question answering systems. Transformers, like BERT, capture contextual relationships in text, while Quadrant offers efficient storage and querying of high-dimensional vectors. Together, they bridge the gap between textual data processing and vector-based storage, paving the way for innovative NLP applications. This article explores the fusion of transformers with Quadrant, showcasing its significance through a practical tutorial.

Definitions

Transformers:

Transformers refer to a class of deep learning models primarily used for NLP tasks. These models, based on the transformer architecture, leverage self-attention mechanisms to capture contextual information from input sequences, enabling superior performance in tasks such as text classification, sentiment analysis, and machine translation.

Quadrant:

Quadrant is a vector database designed for efficient storage and retrieval of high-dimensional embeddings. Utilizing advanced indexing techniques and optimized algorithms, Quadrant offers rapid search capabilities, making it an ideal choice for applications requiring similarity searches, clustering, and recommendation systems based on vector representations.

Integration of Transformers with Quadrant

In this code Implementation we witness the seamless integration of transformers with Quadrant for transforming a text-based dataset from the HuggingFace Hub into a local Quadrant vector database. Let's delve into the key steps of this integration:

Install and Import Packages

%pip install qdrant-client>=1.1.1
%pip install -U sentence-transformers==2.2.2
%pip install -U datasets==2.16.1

## Import Libraies
import time
import math
import torch
from itertools import islice
from tqdm import tqdm
from qdrant_client import models, QdrantClient
from sentence_transformers import SentenceTransformer
from datasets import load_dataset, concatenate_datasets

# Determine device based on GPU availability
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

Load Dataset and Preprocessing

# Load the dataset
dataset = load_dataset("m-newhauser/senator-tweets")

# If the embeddings column already exists, remove it (so we can practice generating it!)
for split in dataset:
    if 'embeddings' in dataset[split].column_names:
        dataset[split] = dataset[split].remove_columns('embeddings')

# Take a peak at the dataset
print(dataset)
dataset["train"].to_pandas().head()

Load the embedding model

model = SentenceTransformer(
          'sentence-transformers/all-MiniLM-L6-v2', 
          device=device
)

Generate the embeddings

def generate_embeddings(split, batch_size=32):
    embeddings = []
    split_name = [name for name, data_split in dataset.items() if data_split is split][0]
    
    with tqdm(total=len(split), desc=f"Generating embeddings for {split_name} split") as pbar:
        for i in range(0, len(split), batch_size):
            batch_sentences = split['text'][i:i+batch_size]
            batch_embeddings = model.encode(batch_sentences)
            embeddings.extend(batch_embeddings)
            pbar.update(len(batch_sentences))
            
    return embeddings

# Generate and append embeddings to the train split
train_embeddings = generate_embeddings(dataset['train'])
dataset["train"] = dataset["train"].add_column("embeddings", train_embeddings)

# Generate and append embeddings to the test split
test_embeddings = generate_embeddings(dataset['test'])
dataset["test"] = dataset["test"].add_column("embeddings", test_embeddings)

Create a local Qdrant vector database

# Combine train and test splits into a single dataset
combined_dataset = concatenate_datasets([dataset['train'], dataset['test']])

# Create an in-memory Qdrant instance
client = QdrantClient(":memory:")

# Create a Qdrant collection for the embeddings
client.create_collection(
    collection_name="senator-tweets",
    vectors_config=models.VectorParams(
        size=model.get_sentence_embedding_dimension(),
        distance=models.Distance.COSINE,
    ),
)

Upsert the embeddings into Qdrant

# Create function to upsert embeddings in batches
def batched(iterable, n):
    iterator = iter(iterable)
    while batch := list(islice(iterator, n)):
        yield batch

batch_size = 100

# Upsert the embeddings in batches
for batch in batched(combined_dataset, batch_size):
    ids = [point.pop("id") for point in batch]
    vectors = [point.pop("embeddings") for point in batch]

    client.upsert(
        collection_name="senator-tweets",
        points=models.Batch(
            ids=ids,
            vectors=vectors,
            payloads=batch,
        ),
    )

Run Queries

# Let's see what senators are saying about immigration policy
hits = client.search(
    collection_name="senator-tweets",
    query_vector=model.encode("Immigration policy").tolist(),
    limit=5
)
for hit in hits:
  print(hit.payload, "score:", hit.score)

Conclusion

The integration of transformers with Quadrant represents a paradigm shift in the realm of NLP and data management. By combining the strengths of transformer models in capturing semantic information from text with Quadrant's efficient storage and retrieval capabilities, researchers and developers can unlock new possibilities in text-based applications. From semantic similarity searches to recommendation systems, the fusion of transformers with Quadrant offers a powerful toolkit for tackling diverse NLP challenges. As demonstrated in the tutorial, this integration enables swift transformation of raw textual data into a structured vector database, laying the foundation for advanced NLP pipelines and applications.

In conclusion, the synergy between transformers and Quadrant holds immense potential for revolutionizing how we handle and process textual data, driving innovation in the field of natural language processing. As the landscape of NLP continues to evolve, this integration promises to play a pivotal role in shaping the future of intelligent text analysis and information retrieval.

“Stay connected and support my work through various platforms:

Medium: You can read my latest articles and insights on Medium at https://medium.com/@andysingal

Paypal: Enjoyed my article? Buy me a coffee! https://paypal.me/alphasingal?country.x=US&locale.x=en_US"

Requests and questions: If you have a project in mind that you’d like me to work on or if you have any questions about the concepts I’ve explained, don’t hesitate to let me know. I’m always looking for new ideas for future Notebooks and I love helping to resolve any doubts you might have.

Resources:

Upvote