---
model-index:
- name: marqo-chimera-arctic-bge-m
  results:
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      config: default
      name: MTEB AskUbuntuDupQuestions (default)
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
      split: test
      type: mteb/askubuntudupquestions-reranking
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    - type: map
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      value: 73.62001494965483
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    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB MindSmallReranking (default)
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
      split: test
      type: mteb/mind_small
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      value: -18.517207734097
    - type: nAUC_mrr_std
      value: -1.3667482921703122
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SciDocsRR (default)
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
      split: test
      type: mteb/scidocs-reranking
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    - type: map
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    - type: mrr
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      value: 82.72754486008768
    - type: nAUC_mrr_std
      value: 78.20855667229367
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB StackOverflowDupQuestions (default)
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
      split: test
      type: mteb/stackoverflowdupquestions-reranking
    metrics:
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    - type: map
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    - type: mrr
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    - type: nAUC_mrr_max
      value: 11.10510095877716
    - type: nAUC_mrr_std
      value: 7.335464870025126
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB ArguAna (default)
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
      split: test
      type: mteb/arguana
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    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB CQADupstackRetrieval (default)
      revision: dataset revision not available
      split: test
      type: BeIR/cqadupstack
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    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB Touche2020 (default)
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
      split: test
      type: mteb/touche2020
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      value: 26.735
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    - type: recall_at_5
      value: 12.334
    task:
      type: Retrieval
tags:
- mteb
- feature-extraction
- marqo
- retrieval
license: mit
language:
- en
library_name: transformers
---

<h1 align="center">Marqo's Chimera arctic-bge-m</h1>
<h4 align="center">
   <p>
       <a href=#this-model>This Model</a>  |
       <a href=#usage>Usage</a>  |
       <a href="#faq">FAQ</a> |
       <a href="#about-marqo">About Marqo</a> |
       <a href="#acknowledgement">Acknowledgement</a>
   <p>
</h4>

## This Model

This is a chimera model which concatenates embeddings from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) and [BAAI/bge-base-en-v1.5](https://huggingface.co/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

```python
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](https://www.marqo.ai/) 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](https://github.com/marqo-ai/marqo) or as a managed cloud service on [Marqo Cloud](https://cloud.marqo.ai).

## Acknowledgement

We want to acknowledge the original creators of the [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) and [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) models which are used to create this model.