README.md

metadata

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        task:
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        task:
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          name: MTEB Touche2020 (default)
          revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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          type: mteb/touche2020
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            value: 9.228
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            value: 12.598
        task:
          type: Retrieval
tags:
  - mteb
  - feature-extraction
  - marqo
  - retrieval
license: mit
language:
  - en
library_name: transformers

Marqo's Chimera arctic-bge-m

This Model | Usage | FAQ | About Marqo | Acknowledgement

This Model

This is a chimera model which concatenates embeddings from Snowflake/snowflake-arctic-embed-m-v1.5 and BAAI/bge-base-en-v1.5. This model produces an embedding with 1536 dimensions (768+768) and has a total of 218M parameters (109+109). Embeddings from each model are unit normalized prior to concatenation.

Usage

import torch
from torch.nn.functional import normalize
from transformers import AutoModel, AutoTokenizer


# Load the model and tokenizer.
tokenizer = AutoTokenizer.from_pretrained("Marqo/marqo-chimera-arctic-bge-m")
model = AutoModel.from_pretrained("Marqo/marqo-chimera-arctic-bge-m", trust_remote_code=True)
model.eval()

# Model constants.
query_prefix = 'Represent this sentence for searching relevant passages: '

# Your queries and docs.
queries = [
    "What is vector search?",
    "Where can I get the best pizza?"
]

documents = [
    "Marqo is an end-to-end platform for embedding training and retrieval.",
    "Definitely Naples! The birthplace of pizza, and it’s as authentic as it gets."
]


# Add query prefix and tokenize queries and docs.
queries_with_prefix = [f"{query_prefix}{q}" for q in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)
document_tokens =  tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)

# Use the model to generate text embeddings.
with torch.inference_mode():
    query_embeddings = model(**query_tokens)
    document_embeddings = model(**document_tokens)

# Remember to normalize embeddings.
query_embeddings = normalize(query_embeddings)
document_embeddings = normalize(document_embeddings)

# Scores via dotproduct.
scores = query_embeddings @ document_embeddings.T

# Pretty-print the results.
for query, query_scores in zip(queries, scores):
    doc_score_pairs = list(zip(documents, query_scores))
    doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
    print(f'Query: "{query}"')
    for document, score in doc_score_pairs:
        print(f'Score: {score:.4f} | Document: "{document}"')
    print()


# Query: "What is vector search?"
# Score: 0.4997 | Document: "Marqo is an end-to-end platform for embedding training and retrieval."
# Score: 0.2509 | Document: "Definitely Naples! The birthplace of pizza, and it’s as authentic as it gets."

# Query: "Where can I get the best pizza?"
# Score: 0.7444 | Document: "Definitely Naples! The birthplace of pizza, and it’s as authentic as it gets."
# Score: 0.3303 | Document: "Marqo is an end-to-end platform for embedding training and retrieval."

FAQ

Q: Do I need to prefix queries?

A: Yes, this model has the same rules for prefixing as its constituent models. Queries in asymmetric retrieval should be prefixed with "Represent this sentence for searching relevant passages: ".

About Marqo

Marqo is an end-to-end platform for training embeddings models and building vector search. Marqo is available as an open-source offering on our GitHub or as a managed cloud service on Marqo Cloud.

Acknowledgement

We want to acknowledge the original creators of the Snowflake/snowflake-arctic-embed-m-v1.5 and BAAI/bge-base-en-v1.5 models which are used to create this model.