You can deploy this model and start earning money today!

Views

No views yet

SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0

Name: snowflake-arctic-embed-l-v2.0-ko
Availability: InStock

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-l-v2.0 on the clustered datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search.

The Snowflake/snowflake-arctic-embed-l-v2.0 model has been further trained with Korean data to enhance its performance in Korean retrieval tasks. It is a powerful model that achieves state-of-the-art (SOTA) performance across multiple retrieval benchmarks.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: Snowflake/snowflake-arctic-embed-l-v2.0
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Datasets:
- AI Hub Dataset
  - 행정 문서 대상 기계 독해
  - 기계 독해
  - 뉴스 기사 기계독해
  - 도서 자료 기계독해
  - 숫자 연산 기계독해
  - 금융 법률 문서 기계독해
Language: Korean, English

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

First install the Sentence Transformers library and xformers library

bash

pip install -U sentence-transformers
pip install xformers

Then you can load this model and run inference.

Using Sentence Transformers

python

from sentence_transformers import SentenceTransformer

# Load the model
# Please use bf16 when inferring with half precision
model_name = 'dragonkue/snowflake-arctic-embed-l-v2.0-ko'
model = SentenceTransformer(model_name)

# Define the queries and documents
queries = ['대한민국의 수도는 어디인가?', '한글을 만든 사람은 누구인가?']
documents = ['대한민국의 수도는 서울이다.', '한글은 세종대왕이 창제하였다.']

# Compute embeddings: use `prompt_name="query"` to encode queries!
query_embeddings = model.encode(queries, prompt_name="query") 
document_embeddings = model.encode(documents)

# Compute cosine similarity scores
scores = model.similarity(query_embeddings, document_embeddings)

# Output 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("Query:", query)
    for document, score in doc_score_pairs:
        print(score, document)

Using Huggingface Transformers

You can use the transformers package to use Snowflake's 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).

python

import torch
from transformers import AutoModel, AutoTokenizer

# Load the model
# Please use bf16 when inferring with half precision
model_name = 'dragonkue/snowflake-arctic-embed-l-v2.0-ko'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name, add_pooling_layer=False)
model.eval()

# Define the queries and documents
query_prefix = 'query: '
queries  = ['대한민국의 수도는 어디인가?', '한글을 만든 사람은 누구인가?']
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=8192)

documents = ['대한민국의 수도는 서울이다.', '한글은 세종대왕이 창제하였다.']
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=8192)

# 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)

Evaluation

This evaluation references the KURE GitHub repository. (https://github.com/nlpai-lab/KURE)
We conducted an evaluation on all Korean Retrieval Benchmarks registered in MTEB.

Korean Retrieval Benchmark

Ko-StrategyQA: A Korean ODQA multi-hop retrieval dataset, translated from StrategyQA.
AutoRAGRetrieval: A Korean document retrieval dataset constructed by parsing PDFs from five domains: finance, public, medical, legal, and commerce.
MIRACLRetrieval: A Korean document retrieval dataset based on Wikipedia.
PublicHealthQA: A retrieval dataset focused on medical and public health domains in Korean.
BelebeleRetrieval: A Korean document retrieval dataset based on FLORES-200.
MrTidyRetrieval: A Wikipedia-based Korean document retrieval dataset.
MultiLongDocRetrieval: A long-document retrieval dataset covering various domains in Korean.
XPQARetrieval: A cross-domain Korean document retrieval dataset.

Metrics

Standard metric : NDCG@10

Information Retrieval

Achieves state-of-the-art (SOTA) performance across various benchmarks.
For each benchmark, the highest score is highlighted in bold, and the second-highest score is italicized.

Model	Average	MrTidyRetrieval	MIRACLRetrieval	XPQARetrieval	BelebeleRetrieval	PublicHealthQA	AutoRAGRetrieval	Ko-StrategyQA
dragonkue/snowflake-arctic-embed-l-v2.0-ko	0.740433	0.57121	0.66846	0.4436	0.95177	0.83374	0.90927	0.80498
dragonkue/BGE-m3-ko	0.729993	0.60992	0.68331	0.38131	0.95027	0.81545	0.87379	0.7959
nlpai-lab/KURE-v1	0.727739	0.59092	0.68157	0.38158	0.95019	0.81925	0.87076	0.7999
BAAI/bge-m3	0.724169	0.64708	0.70146	0.36075	0.93164	0.80412	0.83008	0.79405
Snowflake/snowflake-arctic-embed-l-v2.0	0.724104	0.59071	0.66077	0.43018	0.9271	0.81679	0.83863	0.80455
intfloat/multilingual-e5-large	0.721607	0.64211	0.66486	0.3571	0.94499	0.82534	0.81337	0.80348
nlpai-lab/KoE5	0.711356	0.58411	0.62347	0.35086	0.94251	0.83507	0.84339	0.80008
BAAI/bge-multilingual-gemma2	0.704274	0.47521	0.70315	0.37446	0.95001	0.87102	0.76535	0.79072
jinaai/jina-embeddings-v3	0.701314	0.55759	0.63716	0.41272	0.91203	0.83059	0.76104	0.79807
SamilPwC-AXNode-GenAI/PwC-Embedding_expr	0.699483	0.56656	0.63214	0.36388	0.91669	0.83462	0.78493	0.79756
intfloat/multilingual-e5-large-instruct	0.69837	0.52877	0.59914	0.39712	0.936	0.84967	0.77996	0.79793
nomic-ai/nomic-embed-text-v2-moe	0.693773	0.53766	0.65913	0.36871	0.93636	0.78448	0.80682	0.76325
intfloat/multilingual-e5-base	0.689429	0.58082	0.6227	0.3607	0.92868	0.77203	0.79752	0.76355
intfloat/e5-mistral-7b-instruct	0.683734	0.52444	0.58709	0.39159	0.92403	0.88733	0.67849	0.79317
Alibaba-NLP/gte-Qwen2-7B-instruct	0.680323	0.46571	0.53375	0.37866	0.94808	0.85844	0.76682	0.8108
Qwen/Qwen3-Embedding-0.6B	0.676200	0.48987	0.60021	0.33440	0.91601	0.80290	0.82405	0.76596
Alibaba-NLP/gte-multilingual-base	0.663766	0.56464	0.62697	0.30702	0.8796	0.74584	0.77108	0.75121
openai/text-embedding-3-large	0.662239	0.44728	0.56248	0.37423	0.89451	0.85617	0.76466	0.73634
upskyy/bge-m3-korean	0.6567	0.55011	0.59892	0.31695	0.8731	0.77559	0.72946	0.75277
Salesforce/SFR-Embedding-2_R	0.65591	0.40347	0.55798	0.37371	0.91747	0.8605	0.70782	0.77042
ibm-granite/granite-embedding-278m-multilingual	0.641935	nan	0.59216	0.23058	0.83231	0.77668	0.70226	0.71762
jhgan/ko-sroberta-multitask	0.526301	0.29475	0.36698	0.27961	0.81636	0.69212	0.58332	0.65097

Capabilities Beyond Benchmarks

This model is designed to handle various retrieval scenarios that are not directly measured in benchmarks:

Supports phrase-based queries in addition to full-sentence queries.

Example: "What products does Samsung sell?" or "Samsung's products"
Trained to handle diverse query formats, regardless of phrasing variations.

Example: "Tell me about Samsung.", "I'm curious about Samsung.", "What is Samsung?"
Optimized for Markdown table search, allowing retrieval of answers embedded within tables when present in documents.
Efficient clustering without hard negatives:
- Samples within the same batch are clustered together.
- Uses efficient embedding formation for clustering by truncating embeddings from the Snowflake/snowflake-arctic-embed-l-v2.0 model to 256 dimensions.
- The clustering approach is inspired by the findings in the following papers:
  - Embedding And Clustering Your Data Can Improve Contrastive Pretraining
  - CONTEXTUAL DOCUMENT EMBEDDINGS
Strong performance across different domains:
- The Arctic-Embed 2.0: Multilingual Retrieval Without Compromise paper states:
  "While models like mE5, mGTE, and BGE-M3 excel on MIRACL, their performance on CLEF is notably weaker compared to ours and closed-source offerings, suggesting the potential of overfitting to MIRACL or its Wikipedia-based domain."
- Based on my own experience, Snowflake/snowflake-arctic-embed-l-v2.0 has consistently outperformed BGE-M3 in different domains, further validating this observation.

Bias, Risks and Limitations

To prevent excessive GPU usage costs, the model was trained with a maximum sequence length of 1300 tokens. As a result, its performance may degrade on benchmarks like MultiLongDocRetrieval (MLDR).

The previous model, BGE-m3-ko, was trained with a token length of 1024, which imposed limitations on its MLDR benchmark performance.

In the case of snowflake-arctic-embed-l-v2.0-ko, if the document length exceeds 1300 tokens or approximately 2500 characters, it is recommended to consider the following models instead.

Model	MultiLongDocRetrieval
Alibaba-NLP/gte-multilingual-base/Alibaba-NLP/gte-multilingual-base	0.48402
nlpai-lab/KURE-v1/nlpai-lab_KURE-v1	0.47528
dragonkue/snowflake-arctic-embed-l-v2.0-ko	0.4459
BAAI/bge-m3/BAAI_bge-m3	0.43011
Snowflake/snowflake-arctic-embed-l-v2.0	0.40401
dragonkue/BGE-m3-ko/dragonkue_BGE-m3-ko	0.40135
openai/text-embedding-3-large	0.31108
BAAI/bge-multilingual-gemma2	0.31021
nlpai-lab/KoE5	0.30869
jinaai/jina-embeddings-v3/jinaai__jina-embeddings-v3	0.30512
Alibaba-NLP/gte-Qwen2-7B-instruct/Alibaba-NLP__gte-Qwen2-7B-instruct	0.30313
intfloat/multilingual-e5-large-instruct/intfloat__multilingual-e5-large-instruct	0.27973
nomic-ai/nomic-embed-text-v2-moe	0.27135
intfloat/e5-mistral-7b-instruct/intfloat__e5-mistral-7b-instruct	0.2583
intfloat/multilingual-e5-large/intfloat__multilingual-e5-large	0.24596
Salesforce/SFR-Embedding-2_R/Salesforce__SFR-Embedding-2_R	0.24346
intfloat/multilingual-e5-base/intfloat__multilingual-e5-base	0.23766
upskyy/bge-m3-korean/upskyy__bge-m3-korean	0.21968
ibm-granite/granite-embedding-278m-multilingual/ibm-granite__granite-embedding-278m-multilingual	0.20781
jhgan/ko-sroberta-multitask/jhgan__ko-sroberta-multitask	0.20416

Training Details

Loss: CachedGISTEmbedLoss with these parameters:

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 20000
per_device_eval_batch_size: 4096
learning_rate: 2e-05
num_train_epochs: 2
lr_scheduler_type: warmup_stable_decay
lr_scheduler_kwargs: {'num_decay_steps': 160}
warmup_ratio: 0.05
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 10000
per_device_eval_batch_size: 4096
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: warmup_stable_decay
lr_scheduler_kwargs: {'num_decay_steps': 160}
warmup_ratio: 0.05
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.4.1
Transformers: 4.49.0
PyTorch: 2.6.0+cu124
Accelerate: 1.4.0
Datasets: 3.3.2
Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084"
}

KURE

bibtex

@misc{KURE,
  publisher = {Youngjoon Jang, Junyoung Son, Taemin Lee},
  year = {2024},
  url = {https://github.com/nlpai-lab/KURE}
}

Arctic-Embed 2.0

bibtex

@article{yu2024arcticembed,
  title = "Arctic-Embed 2.0: Multilingual Retrieval Without Compromise",
  author = "Puxuan Yu, Luke Merrick, Gaurav Nuti, Daniel Campos",
  journal = "arXiv preprint arXiv:2412.04506",
  year = "2024",
  url = "https://arxiv.org/abs/2412.04506"
}

Embedding And Clustering Your Data Can Improve Contrastive Pretraining

bibtex

@article{merrick2024embedding,
  title = "Embedding And Clustering Your Data Can Improve Contrastive Pretraining",
  author = "Luke Merrick",
  journal = "arXiv preprint arXiv:2407.18887",
  year = "2024",
  url = "https://arxiv.org/abs/2407.18887"
}

Contextual Document Embeddings

bibtex

@article{morris2024contextual,
  title = "Contextual Document Embeddings",
  author = "John X. Morris, Alexander M. Rush",
  journal = "arXiv preprint arXiv:2410.02525",
  year = "2024",
  url = "https://arxiv.org/abs/2410.02525"
}

License

Arctic is licensed under the Apache-2. The released models can be used for commercial purposes free of charge.