sparsembed 0.1.1

Sparse Embeddings for Neural Search.

SparsEmbed - Splade

Neural search

This repository presents an unofficial replication of both models Splade and SparseEmbed with are state of the art models in information retrieval:

• SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking authored by Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant, SIGIR 2021.

• SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval authored by Thibault Formal, Carlos Lassance, Benjamin Piwowarski, Stéphane Clinchant, SIGIR 2022.

• SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval authored by Weize Kong, Jeffrey M. Dudek, Cheng Li, Mingyang Zhang, and Mike Bendersky, SIGIR 2023.

Note: This project is currently a work in progress. Splade Model is ready to use but I'm working on SparseEmbed. 🔨🧹

Installation

We can install sparsembed using:

pip install sparsembed

If we plan to evaluate our model while training install:

pip install "sparsembed[eval]"

Retriever

Splade

We can initialize a Splade Retriever directly from the splade_v2_max checkpoint available on HuggingFace. Retrievers are based on PyTorch sparse matrices, stored in memory and accelerated with GPU. We can reduce the number of activated tokens via the n_tokens parameter in order to reduce the memory usage of those sparse matrices.

from sparsembed import model, retrieve
from transformers import AutoModelForMaskedLM, AutoTokenizer

device = "cuda" # cpu

batch_size = 10

# List documents to index:
documents = [
 {'id': 0,
  'title': 'Paris',
  'url': 'https://en.wikipedia.org/wiki/Paris',
  'text': 'Paris is the capital and most populous city of France.'},
 {'id': 1,
  'title': 'Paris',
  'url': 'https://en.wikipedia.org/wiki/Paris',
  'text': "Since the 17th century, Paris has been one of Europe's major centres of science, and arts."},
 {'id': 2,
  'title': 'Paris',
  'url': 'https://en.wikipedia.org/wiki/Paris',
  'text': 'The City of Paris is the centre and seat of government of the region and province of Île-de-France.'
}]

model = model.Splade(
    model=AutoModelForMaskedLM.from_pretrained("naver/splade_v2_max").to(device),
    tokenizer=AutoTokenizer.from_pretrained("naver/splade_v2_max"),
    device=device
)

retriever = retrieve.SpladeRetriever(
    key="id", # Key identifier of each document.
    on=["title", "text"], # Fields to search.
    model=model # Splade retriever.
)

retriever = retriever.add(
    documents=documents,
    batch_size=batch_size,
    k_tokens=256, # Number of activated tokens.
)

retriever(
    ["paris", "Toulouse"], # Queries 
    k_tokens=20, # Maximum number of activated tokens.
    k=100, # Number of documents to retrieve.
    batch_size=batch_size
)

[[{'id': 0, 'similarity': 11.481657981872559},
  {'id': 2, 'similarity': 11.294965744018555},
  {'id': 1, 'similarity': 10.059721946716309}],
 [{'id': 0, 'similarity': 0.7379149198532104},
  {'id': 2, 'similarity': 0.6973429918289185},
  {'id': 1, 'similarity': 0.5428210496902466}]]

SparsEmbed

We can also initialize a retriever dedicated to SparseEmbed model. The checkpoint naver/splade_v2_max is not a SparseEmbed trained model so we should train one before using it as a retriever.

from sparsembed import model, retrieve
from transformers import AutoModelForMaskedLM, AutoTokenizer

device = "cuda" # cpu

batch_size = 10

# List documents to index:
documents = [
 {'id': 0,
  'title': 'Paris',
  'url': 'https://en.wikipedia.org/wiki/Paris',
  'text': 'Paris is the capital and most populous city of France.'},
 {'id': 1,
  'title': 'Paris',
  'url': 'https://en.wikipedia.org/wiki/Paris',
  'text': "Since the 17th century, Paris has been one of Europe's major centres of science, and arts."},
 {'id': 2,
  'title': 'Paris',
  'url': 'https://en.wikipedia.org/wiki/Paris',
  'text': 'The City of Paris is the centre and seat of government of the region and province of Île-de-France.'
}]

model = model.SparsEmbed(
    model=AutoModelForMaskedLM.from_pretrained("naver/splade_v2_max").to(device),
    tokenizer=AutoTokenizer.from_pretrained("naver/splade_v2_max"),
    device=device
)

retriever = retrieve.SparsEmbedRetriever(
    key="id", # Key identifier of each document.
    on=["title", "text"], # Fields to search.
    model=model # Splade retriever.
)

retriever = retriever.add(
    documents=documents,
    batch_size=batch_size,
    k_tokens=256, # Number of activated tokens.
)

retriever(
    ["paris", "Toulouse"], # Queries 
    k_tokens=20, # Maximum number of activated tokens.
    k=100, # Number of documents to retrieve.
    batch_size=batch_size
)

Training

Let's fine-tune Splade and SparsEmbed.

Dataset

Your training dataset must be made out of triples (anchor, positive, negative) where anchor is a query, positive is a document that is directly linked to the anchor and negative is a document that is not relevant for the anchor.

X = [
    ("anchor 1", "positive 1", "negative 1"),
    ("anchor 2", "positive 2", "negative 2"),
    ("anchor 3", "positive 3", "negative 3"),
]

Models

Both Splade and SparseEmbed models can be initialized from the AutoModelForMaskedLM pretrained models.

from transformers import AutoModelForMaskedLM, AutoTokenizer

model = model.Splade(
    model=AutoModelForMaskedLM.from_pretrained("naver/splade_v2_max").to(device),
    tokenizer=AutoTokenizer.from_pretrained("naver/splade_v2_max"),
    device=device,
)

from transformers import AutoModelForMaskedLM, AutoTokenizer

model = model.SparsEmbed(
    model=AutoModelForMaskedLM.from_pretrained("naver/splade_v2_max").to(device),
    tokenizer=AutoTokenizer.from_pretrained("naver/splade_v2_max"),
    embedding_size=64,
    k_tokens=96,
    device=device,
)

Splade

The following PyTorch code snippet illustrates the training loop to fine-tune Splade:

from transformers import AutoModelForMaskedLM, AutoTokenizer, optimization
from sparsembed import model, utils, train, retrieve, losses
import torch

device = "cuda" # cpu or cuda
batch_size = 8
epochs = 1 # Number of times the model will train over the whole dataset.

model = model.Splade(
    model=AutoModelForMaskedLM.from_pretrained("naver/splade_v2_max").to(device),
    tokenizer=AutoTokenizer.from_pretrained("naver/splade_v2_max"),
    device=device
)

optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)

scheduler = optimization.get_linear_schedule_with_warmup(
    optimizer=optimizer, 
    num_warmup_steps=6000,
    num_training_steps=4_000_000,
)

flops_scheduler = losses.FlopsScheduler(weight=1e-4, steps=50_000) 

X = [
    ("anchor 1", "positive 1", "negative 1"),
    ("anchor 2", "positive 2", "negative 2"),
    ("anchor 3", "positive 3", "negative 3"),
]

for anchor, positive, negative in utils.iter(
        X,
        epochs=epochs,
        batch_size=batch_size,
        shuffle=True
    ):
        loss = train.train_splade(
            model=model,
            optimizer=optimizer,
            anchor=anchor,
            positive=positive,
            negative=negative,
            flops_loss_weight=flops_scheduler.get(),
        )

        scheduler.step()
        flops_scheduler.step()

# Save the model.
model.save_pretrained("checkpoint")

# Beir benchmark for evaluation.
documents, queries, qrels = utils.load_beir("scifact", split="test")

retriever = retrieve.SpladeRetriever(
    key="id",
    on=["title", "text"],
    model=model
)

retriever = retriever.add(
    documents=documents,
    batch_size=batch_size,
    k_tokens=96,
)

utils.evaluate(
    retriever=retriever,
    batch_size=batch_size,
    qrels=qrels,
    queries=queries,
    k=100,
    k_tokens=96,
    metrics=["map", "ndcg@10", "ndcg@10", "recall@10", "hits@10"]
)

After having saved the model with save_pretrained, we can load the checkpoint using:

from sparsembed import model

device = "cuda"

model = model.Splade(
    model_name_or_path="checkpoint",
    device=device,
)

SparsEmbed

The following PyTorch code snippet illustrates the training loop to fine-tune SparseEmbed:

from transformers import AutoModelForMaskedLM, AutoTokenizer, optimization
from sparsembed import model, utils, train, retrieve, losses
import torch

device = "cuda" # cpu or cuda
batch_size = 8
epochs = 1 # Number of times the model will train over the whole dataset.

model = model.SparsEmbed(
    model=AutoModelForMaskedLM.from_pretrained("distilbert-base-uncased").to(device),
    tokenizer=AutoTokenizer.from_pretrained("distilbert-base-uncased"),
    device=device
)

optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)

scheduler = optimization.get_linear_schedule_with_warmup(
    optimizer=optimizer, 
    num_warmup_steps=6000, # Number of warmup steps.
    num_training_steps=4_000_000 # Length training set.
)

flops_scheduler = losses.FlopsScheduler(weight=1e-4, steps=50_000)

X = [
    ("anchor 1", "positive 1", "negative 1"),
    ("anchor 2", "positive 2", "negative 2"),
    ("anchor 3", "positive 3", "negative 3"),
]

for anchor, positive, negative in utils.iter(
        X,
        epochs=epochs,
        batch_size=batch_size,
        shuffle=True
    ):
        loss = train.train_sparsembed(
            model=model,
            optimizer=optimizer,
            k_tokens=96,
            anchor=anchor,
            positive=positive,
            negative=negative,
            flops_loss_weight=flops_scheduler.get(),
            sparse_loss_weight=0.1,
        )

        scheduler.step()
        flops_scheduler.step()

# Save the model.
model.save_pretrained("checkpoint")

# Beir benchmark for evaluation.
documents, queries, qrels = utils.load_beir("scifact", split="test")

retriever = retrieve.SparsEmbedRetriever(
    key="id",
    on=["title", "text"],
    model=model
)

retriever = retriever.add(
    documents=documents,
    k_tokens=96,
    batch_size=batch_size
)

utils.evaluate(
    retriever=retriever,
    batch_size=batch_size,
    qrels=qrels,
    queries=queries,
    k=100,
    k_tokens=96,
    metrics=["map", "ndcg@10", "ndcg@10", "recall@10", "hits@10"]
)

After having saved the model with save_pretrained, we can load the checkpoint using:

from sparsembed import model

device = "cuda"

model = model.SparsEmbed(
    model_name_or_path="checkpoint",
    device=device,
)

Utils

We can get the activated tokens / embeddings of a sentence with:

model.encode(["deep learning, information retrieval, sparse models"])

We can evaluate similarities between pairs of queries and documents without the use of a retriever:

model.scores(
    queries=["Query A", "Query B"], 
    documents=["Document A", "Document B"],
    batch_size=32,
)

tensor([5.1449, 9.1194])

Wen can visualize activated tokens:

model.decode(**model.encode(["deep learning, information retrieval, sparse models"]))

['deep sparse model retrieval information models depth fuzzy learning dense poor memory recall processing reading lacy include remember knowledge training heavy retrieve guide vague type small learn data']

Benchmarks

Work in progress.