metadata

task_categories:
  - question-answering
language:
  - en
tags:
  - TREC-RAG
  - RAG
  - MSMARCO
  - MSMARCOV2.1
  - Snowflake
  - arctic
  - arctic-embed
  - arctic-embed-v1.5
  - MRL
pretty_name: TREC-RAG-Embedding-Baseline
size_categories:
  - 100M<n<1B
configs:
  - config_name: corpus
    data_files:
      - split: train
        path: corpus/*

Snowflake Arctic Embed M V1.5 Embeddings for MSMARCO V2.1 for TREC-RAG

This dataset contains the embeddings for the MSMARCO-V2.1 dataset which is used as the corpora for TREC RAG All embeddings are created using Snowflake's Arctic Embed M v1.5 and are intended to serve as a simple baseline for dense retrieval-based methods. It's worth noting that Snowflake's Arctic Embed M v1.5 is optimized for efficient embeddings and thus supports embedding truncation and quantization. More details on model release can be found in this blog along with methods for quantization and compression. Note, that the embeddings are not normalized so you will need to normalize them before usage.

Retrieval Performance

Retrieval performance for the TREC DL21-23, MSMARCOV2-Dev and Raggy Queries can be found below with BM25 as a baseline. For both systems, retrieval is at the segment level and Doc Score = Max (passage score). Retrieval is done via a dot product and happens in BF16. Since the M-v1.5 model supports Vector Truncation we do so to 256 dimensions

NDCG@10

Dataset	BM25	Arctic-M-V1.5 (768 Dimensions)	Arctic-M-V1.5 (256 Dimensions)
Deep Learning 2021	0.5778	0.6936	0.69392
Deep Learning 2022	0.3576	0.55199	0.55608
Deep Learning 2023	0.3356	0.46963	0.45196
msmarcov2-dev	N/A	0.346	0.34074
msmarcov2-dev2	N/A	0.34518	0.34339
Raggy Queries	0.4227	0.57439	0.56686

Recall@100

Dataset	BM25	Arctic-M-V1.5 (768 Dimensions)	Arctic-M-V1.5 (256 Dimensions)
Deep Learning 2021	0.3811	0.43	0.42245
Deep Learning 2022	0.233	0.32125	0.3165
Deep Learning 2023	0.3049	0.37622	0.36089
msmarcov2-dev	0.6683	0.85435	0.84985
msmarcov2-dev2	0.6771	0.8576	0.8526
Raggy Queries	0.2807	0.36915	0.36149

Recall@1000

Dataset	BM25	Arctic-M-V1.5 (768 Dimensions)	Arctic-M-V1.5 (256 Dimensions)
Deep Learning 2021	0.7115	0.74895	0.73511
Deep Learning 2022	0.479	0.55413	0.54499
Deep Learning 2023	0.5852	0.62262	0.61199
msmarcov2-dev	0.8528	0.94156	0.94014
msmarcov2-dev2	0.8577	0.94277	0.94047
Raggy Queries	0.5745	0.64527	0.63826

Loading the dataset

Loading the document embeddings

You can either load the dataset like this:

from datasets import load_dataset
docs = load_dataset("Snowflake/msmarco-v2.1-snowflake-arctic-embed-m-v1.5", split="train")

Or you can also stream it without downloading it before:

from datasets import load_dataset
docs = load_dataset("Snowflake/msmarco-v2.1-snowflake-arctic-embed-m-v1.5",  split="train", streaming=True)
for doc in docs:
    doc_id = j['docid']
    url = doc['url']
    text = doc['text']
    emb = doc['embedding']

Note, The full dataset corpus is ~ 620GB so it will take a while to download and may not fit on some devices/

Search

A full search example (on the first 1,000 paragraphs):

from datasets import load_dataset
import torch
from transformers import AutoModel, AutoTokenizer
import numpy as np


top_k = 100
docs_stream = load_dataset("Snowflake/msmarco-v2.1-snowflake-arctic-embed-m-v1.5",split="train", streaming=True)

docs = []
doc_embeddings = []

for doc in docs_stream:
    docs.append(doc)
    doc_embeddings.append(doc['embedding'])
    if len(docs) >= top_k:
        break

doc_embeddings = np.asarray(doc_embeddings)

tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-m-v1.5')
model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-v1.5', add_pooling_layer=False)
model.eval()

query_prefix = 'Represent this sentence for searching relevant passages: '
queries  = ['how do you clean smoke off walls']
queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)

# Compute token embeddings
with torch.no_grad():
    query_embeddings = model(**query_tokens)[0][:, 0]


# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
doc_embeddings = torch.nn.functional.normalize(doc_embeddings, p=2, dim=1)

# Compute dot score between query embedding and document embeddings
dot_scores = np.matmul(query_embeddings, doc_embeddings.transpose())[0]
top_k_hits = np.argpartition(dot_scores, -top_k)[-top_k:].tolist()

# Sort top_k_hits by dot score
top_k_hits.sort(key=lambda x: dot_scores[x], reverse=True)

# Print results
print("Query:", queries[0])
for doc_id in top_k_hits:
    print(docs[doc_id]['doc_id'])
    print(docs[doc_id]['text'])
    print(docs[doc_id]['url'], "\n")