pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js
license: apache-2.0
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ky
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- pa
- pl
- pt
- qu
- ro
- ru
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- yo
- zh
Snowflake's Arctic-embed-l-v2.0
Models | Usage | Evaluation | Contact | FAQ License | Acknowledgement
Models
MIRACL (4) Voyage misc. (9) CLEF (5) CLEF, max context length Multilingual CLEF Snowflake's snowflake-arctic-embed-l-v2.0 is a multilingual text embedding models that focuses on providing BEIR 0.556 0.558 0.655 0.529 0.541 0.543 0.543 0.543 0.644 0.519 0.528 0.534
Focused on
| Model Name | # params | # non-emb params | # dimensions | BEIR (15) | MIRACL (4) | CLEF (Focused) | CLEF (Full) |
|---|---|---|---|---|---|---|---|
| me5 base | 560M | 303M | 1024 | 0.514 | 0.540 | 0.430 | 0.346 |
| bge-m3 (BAAI) | 568M | 303M | 1024 | 0.488 | 0.568 | 0.408 | 0.413 |
| gte (Alibaba) | 305M | 113M | 768 | 0.511 | 0.523 | 0.477 | 0.531 |
| Arctic-M | 109M | 86M | 768 | 0.549 | 0.249 | 0.344 | 0.291 |
| snowflake-arctic-m | 335M | 303M | 1024 | 0.560 | 0.348 | 0.382 | 0.337 |
| me5 base | 560M | 303M | 1024 | 0.514 | 0.540 | 0.430 | 0.346 |
| bge-m3 (BAAI) | 568M | 303M | 1024 | 0.488 | 0.568 | 0.408 | 0.413 |
| gte (Alibaba) | 305M | 113M | 768 | 0.511 | 0.523 | 0.477 | 0.531 |
| snowflake-arctic-m | 109M | 86M | 768 | 0.549 | 0.249 | 0.344 | 0.291 |
| snowflake-arctic-l | 335M | 303M | 1024 | 0.560 | 0.348 | 0.382 | 0.337 |
| snowflake-arctic-m-v2.0 | 305M | 113M | 768 | 0.554 | 0.552 | 0.517 | 0.539 |
| snowflake-arctic-l-v2.0 | 568M | 303M | 1024 | 0.556 | 0.558 | 0.529 | 0.543 |
MRL
| Model | BEIR (15) | Relative Performance | MIRACL (4) | Relative Performance | CLEF (5) | Relative Performance | CLEF (Full) | Relative Performance | |
|---|---|---|---|---|---|---|---|---|---|
| snowflake-arctic-l-v2.0 | 1024 | 0.556 | N/A | 0.558 | N/A | 0.529 | N/A | 0.543 | N/A |
| snowflake-arctic-l-v2.0 | 256 | 0.543 | -0.18% | 0.543 | -2.70% | 0.519 | -1.81% | 0.534 | -1.53% |
| snowflake-arctic-m-v2.0 | 768 | 0.554 | N/A | 0.552 | N/A | 0.517 | N/A | 0.539 | N/A |
| snowflake-arctic-m-v2.0 | 256 | 0.544 | -1.81% | 0.54 | -2.17% | 0.506 | -2.13% | 0.523 | -3.06% |
The snowflake-arctic-embedding models achieve state-of-the-art performance on the MTEB/BEIR leaderboard for each of their size variants. Evaluation is performed using these scripts. As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.
The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report can be found here.
| Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension |
|---|---|---|---|
| snowflake-arctic-embed-xs | 50.15 | 22 | 384 |
| snowflake-arctic-embed-s | 51.98 | 33 | 384 |
| snowflake-arctic-embed-m | 54.90 | 110 | 768 |
| snowflake-arctic-embed-m-long | 54.83 | 137 | 768 |
| snowflake-arctic-embed-l | 55.98 | 335 | 1024 |
Usage
Using Huggingface transformers
You can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
import torch
from transformers import AutoModel, AutoTokenizer
model_name = 'snowflake-arctic-embed-l-v2.0'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name, add_pooling_layer=False)
model.eval()
query_prefix = 'Represent this sentence for searching relevant passages: '
queries = ['what is snowflake?', 'Where can I get the best tacos?']
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)
documents = ['The Data Cloud!', 'Mexico City of Course!']
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)
# Compute token embeddings
with torch.no_grad():
query_embeddings = model(**query_tokens)[0][:, 0]
document_embeddings = model(**document_tokens)[0][:, 0]
# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)
scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
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)
#Output passages & scores
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
Contact
Feel free to open an issue or pull request if you have any questions or suggestions about this project. You also can email Daniel Campos([email protected]).
License
Arctic is licensed under the Apache-2. The released models can be used for commercial purposes free of charge.