This is a PyLate model finetuned from jhu-clsp/mmBERT-base on the en dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Performance on MTEB(kor, v2)

nDCG@10 on six small-corpus Korean retrieval tasks. This model is trained on English data only yet transfers to Korean; see the language-transfer analysis. Late-interaction rows are our two models; all others are dense embedding models (numbers from the colbert-ko-en-v2 card).

Model	Params	Avg	AutoRAG	PubHealth	StrategyQA	LawIR	SQuAD	Belebele
mmBERT-base-en-cl-kd	307M	0.8156	0.9517	0.7478	0.7636	0.5033	0.9788	0.9484
mmBERT-base-en-cl	307M	0.8190	0.9506	0.7489	0.7724	0.5113	0.9758	0.9551
sionic-ai/comsat-embed-ko-8b-preview	7.6B	0.8828	0.8518	0.8871	0.8394	0.8164	0.9168	0.9853
microsoft/harrier-oss-v1-27b	27.0B	0.8832	0.8176	0.8971	0.8361	0.8737	0.9204	0.9546
Qwen/Qwen3-Embedding-8B	7.6B	0.8736	0.8276	0.8721	0.8363	0.8171	0.9063	0.9824
codefuse-ai/F2LLM-v2-8B	7.6B	0.8703	0.7678	0.9380	0.8371	0.8405	0.8874	0.9513
telepix/PIXIE-Rune-v1.5	568M	0.8699	0.8927	0.8426	0.8064	0.7705	0.9457	0.9617
dragonkue/snowflake-arctic-embed-l-v2.0-ko	568M	0.8697	0.9093	0.8337	0.8050	0.7735	0.9447	0.9518
Qwen/Qwen3-Embedding-4B	4.0B	0.8622	0.8431	0.8693	0.8270	0.7769	0.9044	0.9522
nlpai-lab/KURE-v1	568M	0.8531	0.8708	0.8193	0.7999	0.7426	0.9357	0.9502
dragonkue/BGE-m3-ko	568M	0.8515	0.8738	0.8155	0.7959	0.7322	0.9414	0.9503
nlpai-lab/KoE5	560M	0.8491	0.8434	0.8351	0.8001	0.7756	0.8980	0.9425

Model Details

Model Description

Model Type: PyLate model
Base model: jhu-clsp/mmBERT-base
Document Length: 1024 tokens
Query Length: 64 tokens
Output Dimensionality: 128 tokens
Similarity Function: MaxSim
Training Dataset:
- en

Model Sources

Documentation: PyLate Documentation
Repository: PyLate on GitHub
Hugging Face: PyLate models on Hugging Face

Full Model Architecture

ColBERT(
  (0): Transformer({'max_seq_length': 1023, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)

Usage

First install the PyLate library:

pip install -U pylate

Retrieval

Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search.

Indexing documents

Load the ColBERT model and initialize the PLAID index, then encode and index your documents:

python

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model
model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

# Step 2: Initialize the PLAID index
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

python

# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
)

Retrieving top-k documents for queries

Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:

python

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
    show_progress_bar=True,
)

# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

Reranking

If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:

python

from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

Evaluation

Metrics

Py Late Information Retrieval

Dataset: ['dev-AutoRAGRetrieval', 'dev-Ko-StrategyQA', 'dev-BelebeleRetrieval']
Evaluated with pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator

Metric	dev-AutoRAGRetrieval	dev-Ko-StrategyQA	dev-BelebeleRetrieval
MaxSim_accuracy@1	0.8772	0.7517	0.9167
MaxSim_accuracy@10	1.0	0.9003	0.9789
MaxSim_precision@10	0.1	0.1542	0.0979
MaxSim_precision@100	0.01	0.0172	0.01
MaxSim_recall@10	1.0	0.8428	0.9789
MaxSim_recall@100	1.0	0.9177	0.9978
MaxSim_ndcg@10	0.9432	0.7799	0.9495
MaxSim_mrr@10	0.9243	0.8052	0.9399
MaxSim_map@100	0.9243	0.7365	0.9409

Training Details

Training Dataset

en

Dataset: en
Size: 1,224,652 training samples
Columns: query, positive, negative_0, negative_1, negative_2, negative_3, negative_4, negative_5, negative_6, negative_7, negative_8, negative_9, and label
Loss: mdenseon_finetune.CachedContrastiveKLDiv

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 128
num_train_epochs: 1.0
learning_rate: 2e-05
warmup_steps: 0.1
bf16: True
eval_strategy: steps
per_device_eval_batch_size: 16
eval_on_start: True
accelerator_config: {'split_batches': True, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}

All Hyperparameters

Click to expand

per_device_train_batch_size: 128
num_train_epochs: 1.0
max_steps: -1
learning_rate: 2e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: trackio
eval_strategy: steps
per_device_eval_batch_size: 16
prediction_loss_only: True
eval_on_start: True
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': True, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: True
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	dev-AutoRAGRetrieval_MaxSim_ndcg@10	dev-Ko-StrategyQA_MaxSim_ndcg@10	dev-BelebeleRetrieval_MaxSim_ndcg@10
0	0	-	0.4605	0.0779	0.3604
0.0010	10	57.2983	-	-	-
0.0021	20	55.9773	-	-	-
0.0031	30	55.1089	-	-	-
0.0042	40	53.4385	-	-	-
0.0052	50	51.2050	-	-	-
0.0063	60	49.9745	-	-	-
0.0073	70	47.5977	-	-	-
0.0084	80	45.3172	-	-	-
0.0094	90	43.3336	-	-	-
0.0105	100	39.7050	-	-	-
0.0115	110	36.5734	-	-	-
0.0125	120	35.1804	-	-	-
0.0136	130	31.7750	-	-	-
0.0146	140	30.3082	-	-	-
0.0157	150	28.5158	-	-	-
0.0167	160	27.4602	-	-	-
0.0178	170	25.5721	-	-	-
0.0188	180	24.7087	-	-	-
0.0199	190	24.1524	-	-	-
0.0209	200	24.1581	-	-	-
0.0220	210	23.3294	-	-	-
0.0230	220	22.4116	-	-	-
0.0240	230	21.5358	-	-	-
0.0251	240	21.3590	-	-	-
0.0261	250	22.4298	-	-	-
0.0272	260	20.1569	-	-	-
0.0282	270	21.0478	-	-	-
0.0293	280	19.3497	-	-	-
0.0303	290	18.8081	-	-	-
0.0314	300	17.9920	-	-	-
0.0324	310	18.8005	-	-	-
0.0334	320	17.7992	-	-	-
0.0345	330	17.4975	-	-	-
0.0355	340	18.0194	-	-	-
0.0366	350	16.3271	-	-	-
0.0376	360	15.8461	-	-	-
0.0387	370	16.4777	-	-	-
0.0397	380	15.7189	-	-	-
0.0408	390	16.2248	-	-	-
0.0418	400	14.8902	-	-	-
0.0429	410	15.8047	-	-	-
0.0439	420	15.4271	-	-	-
0.0449	430	14.0370	-	-	-
0.0460	440	16.6539	-	-	-
0.0470	450	14.7556	-	-	-
0.0481	460	14.7637	-	-	-
0.0491	470	15.2811	-	-	-
0.0501	479	-	0.8219	0.6120	0.8306
0.0502	480	14.8768	-	-	-
0.0512	490	14.2301	-	-	-
0.0523	500	14.9667	-	-	-
0.0533	510	12.9956	-	-	-
0.0544	520	12.9479	-	-	-
0.0554	530	13.0168	-	-	-
0.0564	540	13.6081	-	-	-
0.0575	550	14.4543	-	-	-
0.0585	560	14.5453	-	-	-
0.0596	570	12.8088	-	-	-
0.0606	580	12.9431	-	-	-
0.0617	590	13.2950	-	-	-
0.0627	600	12.2339	-	-	-
0.0638	610	12.2585	-	-	-
0.0648	620	12.1489	-	-	-
0.0659	630	12.9164	-	-	-
0.0669	640	11.8287	-	-	-
0.0679	650	12.1291	-	-	-
0.0690	660	12.2635	-	-	-
0.0700	670	13.0570	-	-	-
0.0711	680	11.9665	-	-	-
0.0721	690	11.0534	-	-	-
0.0732	700	11.5367	-	-	-
0.0742	710	12.3828	-	-	-
0.0753	720	11.6566	-	-	-
0.0763	730	11.2309	-	-	-
0.0773	740	10.2374	-	-	-
0.0784	750	11.8376	-	-	-
0.0794	760	10.7845	-	-	-
0.0805	770	11.5817	-	-	-
0.0815	780	11.3723	-	-	-
0.0826	790	11.4466	-	-	-
0.0836	800	9.9044	-	-	-
0.0847	810	10.3317	-	-	-
0.0857	820	10.6773	-	-	-
0.0868	830	11.2671	-	-	-
0.0878	840	11.1765	-	-	-
0.0888	850	11.3965	-	-	-
0.0899	860	11.1819	-	-	-
0.0909	870	10.7729	-	-	-
0.0920	880	10.8639	-	-	-
0.0930	890	10.2239	-	-	-
0.0941	900	10.0214	-	-	-
0.0951	910	11.0513	-	-	-
0.0962	920	9.8060	-	-	-
0.0972	930	10.6656	-	-	-
0.0983	940	10.2207	-	-	-
0.0993	950	9.7490	-	-	-
0.1001	958	-	0.8950	0.6807	0.9018
0.1003	960	10.4866	-	-	-
0.1014	970	11.3278	-	-	-
0.1024	980	10.1953	-	-	-
0.1035	990	10.7676	-	-	-
0.1045	1000	10.4331	-	-	-
0.1056	1010	10.1877	-	-	-
0.1066	1020	10.0052	-	-	-
0.1077	1030	10.6269	-	-	-
0.1087	1040	10.2089	-	-	-
0.1098	1050	10.8243	-	-	-
0.1108	1060	10.1026	-	-	-
0.1118	1070	9.2267	-	-	-
0.1129	1080	9.8565	-	-	-
0.1139	1090	10.9286	-	-	-
0.1150	1100	9.6140	-	-	-
0.1160	1110	10.1860	-	-	-
0.1171	1120	10.0805	-	-	-
0.1181	1130	10.0446	-	-	-
0.1192	1140	9.7126	-	-	-
0.1202	1150	9.1908	-	-	-
0.1213	1160	9.6352	-	-	-
0.1223	1170	9.5098	-	-	-
0.1233	1180	9.4626	-	-	-
0.1244	1190	9.5946	-	-	-
0.1254	1200	9.3738	-	-	-
0.1265	1210	9.2083	-	-	-
0.1275	1220	9.1199	-	-	-
0.1286	1230	9.7524	-	-	-
0.1296	1240	10.1292	-	-	-
0.1307	1250	9.2885	-	-	-
0.1317	1260	10.1084	-	-	-
0.1327	1270	9.4031	-	-	-
0.1338	1280	10.1662	-	-	-
0.1348	1290	9.6788	-	-	-
0.1359	1300	9.5017	-	-	-
0.1369	1310	8.8307	-	-	-
0.1380	1320	8.8476	-	-	-
0.1390	1330	9.7446	-	-	-
0.1401	1340	8.4118	-	-	-
0.1411	1350	9.1709	-	-	-
0.1422	1360	8.3114	-	-	-
0.1432	1370	10.6151	-	-	-
0.1442	1380	9.4526	-	-	-
0.1453	1390	8.7293	-	-	-
0.1463	1400	8.8319	-	-	-
0.1474	1410	8.9524	-	-	-
0.1484	1420	9.5885	-	-	-
0.1495	1430	9.4021	-	-	-
0.1502	1437	-	0.9129	0.7169	0.9090
0.1505	1440	8.6521	-	-	-
0.1516	1450	8.6227	-	-	-
0.1526	1460	8.3772	-	-	-
0.1537	1470	8.5195	-	-	-
0.1547	1480	9.3735	-	-	-
0.1557	1490	8.6999	-	-	-
0.1568	1500	9.2154	-	-	-
0.1578	1510	9.0089	-	-	-
0.1589	1520	8.6055	-	-	-
0.1599	1530	10.0069	-	-	-
0.1610	1540	8.9018	-	-	-
0.1620	1550	8.2297	-	-	-
0.1631	1560	7.8741	-	-	-
0.1641	1570	9.4306	-	-	-
0.1652	1580	9.2803	-	-	-
0.1662	1590	7.7809	-	-	-
0.1672	1600	7.9764	-	-	-
0.1683	1610	8.4013	-	-	-
0.1693	1620	8.3370	-	-	-
0.1704	1630	8.2461	-	-	-
0.1714	1640	8.3157	-	-	-
0.1725	1650	7.7299	-	-	-
0.1735	1660	8.2407	-	-	-
0.1746	1670	9.1713	-	-	-
0.1756	1680	8.2463	-	-	-
0.1766	1690	8.9470	-	-	-
0.1777	1700	8.4160	-	-	-
0.1787	1710	7.7903	-	-	-
0.1798	1720	8.0626	-	-	-
0.1808	1730	8.1269	-	-	-
0.1819	1740	8.4284	-	-	-
0.1829	1750	8.6172	-	-	-
0.1840	1760	8.7689	-	-	-
0.1850	1770	8.5001	-	-	-
0.1861	1780	8.7231	-	-	-
0.1871	1790	8.0056	-	-	-
0.1881	1800	8.2325	-	-	-
0.1892	1810	8.5952	-	-	-
0.1902	1820	9.1430	-	-	-
0.1913	1830	8.2261	-	-	-
0.1923	1840	8.6731	-	-	-
0.1934	1850	8.5120	-	-	-
0.1944	1860	7.9608	-	-	-
0.1955	1870	8.1451	-	-	-
0.1965	1880	8.2817	-	-	-
0.1976	1890	7.9721	-	-	-
0.1986	1900	7.4712	-	-	-
0.1996	1910	7.5160	-	-	-
0.2003	1916	-	0.9303	0.7349	0.9288
0.2007	1920	8.5678	-	-	-
0.2017	1930	8.9222	-	-	-
0.2028	1940	8.3231	-	-	-
0.2038	1950	7.4491	-	-	-
0.2049	1960	7.8048	-	-	-
0.2059	1970	7.8064	-	-	-
0.2070	1980	8.6599	-	-	-
0.2080	1990	7.9160	-	-	-
0.2091	2000	7.5319	-	-	-
0.2101	2010	7.5030	-	-	-
0.2111	2020	8.1132	-	-	-
0.2122	2030	8.4232	-	-	-
0.2132	2040	6.9407	-	-	-
0.2143	2050	8.4254	-	-	-
0.2153	2060	8.1845	-	-	-
0.2164	2070	7.2004	-	-	-
0.2174	2080	7.8885	-	-	-
0.2185	2090	7.8051	-	-	-
0.2195	2100	8.1562	-	-	-
0.2205	2110	8.1120	-	-	-
0.2216	2120	7.9766	-	-	-
0.2226	2130	7.9334	-	-	-
0.2237	2140	7.7178	-	-	-
0.2247	2150	8.4197	-	-	-
0.2258	2160	7.2016	-	-	-
0.2268	2170	7.3539	-	-	-
0.2279	2180	7.8712	-	-	-
0.2289	2190	8.2998	-	-	-
0.2300	2200	8.2781	-	-	-
0.2310	2210	6.9097	-	-	-
0.2320	2220	7.5848	-	-	-
0.2331	2230	7.8651	-	-	-
0.2341	2240	7.0148	-	-	-
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0.2989	2860	7.0419	-	-	-
0.3000	2870	7.5420	-	-	-
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0.6491	6210	5.2935	-	-	-
0.6502	6220	5.9549	-	-	-
0.6509	6227	-	0.9378	0.7669	0.9443
0.6512	6230	5.8086	-	-	-
0.6522	6240	6.1464	-	-	-
0.6533	6250	5.1077	-	-	-
0.6543	6260	5.5684	-	-	-
0.6554	6270	6.1883	-	-	-
0.6564	6280	5.9687	-	-	-
0.6575	6290	5.9311	-	-	-
0.6585	6300	5.4328	-	-	-
0.6596	6310	5.1875	-	-	-
0.6606	6320	5.3904	-	-	-
0.6616	6330	6.0831	-	-	-
0.6627	6340	5.4682	-	-	-
0.6637	6350	5.3335	-	-	-
0.6648	6360	5.6569	-	-	-
0.6658	6370	5.9439	-	-	-
0.6669	6380	5.1361	-	-	-
0.6679	6390	5.1323	-	-	-
0.6690	6400	6.0127	-	-	-
0.6700	6410	5.8333	-	-	-
0.6711	6420	5.6919	-	-	-
0.6721	6430	6.1825	-	-	-
0.6731	6440	5.5990	-	-	-
0.6742	6450	5.5014	-	-	-
0.6752	6460	5.1662	-	-	-
0.6763	6470	5.2456	-	-	-
0.6773	6480	5.6394	-	-	-
0.6784	6490	6.1019	-	-	-
0.6794	6500	5.7043	-	-	-
0.6805	6510	5.8644	-	-	-
0.6815	6520	5.8368	-	-	-
0.6826	6530	5.8332	-	-	-
0.6836	6540	5.9013	-	-	-
0.6846	6550	5.9287	-	-	-
0.6857	6560	5.9017	-	-	-
0.6867	6570	5.8468	-	-	-
0.6878	6580	5.5384	-	-	-
0.6888	6590	5.7853	-	-	-
0.6899	6600	5.3098	-	-	-
0.6909	6610	5.9731	-	-	-
0.6920	6620	5.5039	-	-	-
0.6930	6630	5.4807	-	-	-
0.6941	6640	5.9441	-	-	-
0.6951	6650	5.5573	-	-	-
0.6961	6660	5.6211	-	-	-
0.6972	6670	5.7566	-	-	-
0.6982	6680	5.6000	-	-	-
0.6993	6690	5.5085	-	-	-
0.7003	6700	5.2398	-	-	-
0.7010	6706	-	0.9316	0.7713	0.9458
0.7014	6710	5.6979	-	-	-
0.7024	6720	6.3184	-	-	-
0.7035	6730	5.6301	-	-	-
0.7045	6740	6.0894	-	-	-
0.7056	6750	5.5629	-	-	-
0.7066	6760	5.6842	-	-	-
0.7076	6770	5.6657	-	-	-
0.7087	6780	5.2353	-	-	-
0.7097	6790	5.9955	-	-	-
0.7108	6800	5.3159	-	-	-
0.7118	6810	5.7163	-	-	-
0.7129	6820	5.6412	-	-	-
0.7139	6830	5.8951	-	-	-
0.7150	6840	5.1451	-	-	-
0.7160	6850	5.6594	-	-	-
0.7170	6860	6.1816	-	-	-
0.7181	6870	5.8236	-	-	-
0.7191	6880	5.7522	-	-	-
0.7202	6890	5.2983	-	-	-
0.7212	6900	5.4474	-	-	-
0.7223	6910	6.1625	-	-	-
0.7233	6920	5.9625	-	-	-
0.7244	6930	5.7456	-	-	-
0.7254	6940	5.4710	-	-	-
0.7265	6950	5.8996	-	-	-
0.7275	6960	6.3989	-	-	-
0.7285	6970	6.2378	-	-	-
0.7296	6980	4.9934	-	-	-
0.7306	6990	5.1639	-	-	-
0.7317	7000	5.0227	-	-	-
0.7327	7010	5.1877	-	-	-
0.7338	7020	6.3049	-	-	-
0.7348	7030	5.6892	-	-	-
0.7359	7040	5.7532	-	-	-
0.7369	7050	5.5178	-	-	-
0.7380	7060	6.3196	-	-	-
0.7390	7070	5.3712	-	-	-
0.7400	7080	5.9655	-	-	-
0.7411	7090	6.1041	-	-	-
0.7421	7100	6.0734	-	-	-
0.7432	7110	5.7659	-	-	-
0.7442	7120	5.3487	-	-	-
0.7453	7130	5.7572	-	-	-
0.7463	7140	5.9154	-	-	-
0.7474	7150	5.3702	-	-	-
0.7484	7160	5.4124	-	-	-
0.7495	7170	5.7485	-	-	-
0.7505	7180	6.4862	-	-	-
0.7510	7185	-	0.9508	0.7623	0.9397
0.7515	7190	5.3642	-	-	-
0.7526	7200	5.4654	-	-	-
0.7536	7210	5.6791	-	-	-
0.7547	7220	5.5377	-	-	-
0.7557	7230	5.2542	-	-	-
0.7568	7240	5.7225	-	-	-
0.7578	7250	5.5659	-	-	-
0.7589	7260	5.9535	-	-	-
0.7599	7270	5.3275	-	-	-
0.7609	7280	5.5350	-	-	-
0.7620	7290	5.2377	-	-	-
0.7630	7300	5.8614	-	-	-
0.7641	7310	5.4114	-	-	-
0.7651	7320	5.8856	-	-	-
0.7662	7330	5.0510	-	-	-
0.7672	7340	5.3818	-	-	-
0.7683	7350	5.0915	-	-	-
0.7693	7360	5.6641	-	-	-
0.7704	7370	5.6571	-	-	-
0.7714	7380	5.2004	-	-	-
0.7724	7390	5.3837	-	-	-
0.7735	7400	6.2034	-	-	-
0.7745	7410	6.1608	-	-	-
0.7756	7420	5.2252	-	-	-
0.7766	7430	5.5199	-	-	-
0.7777	7440	5.2356	-	-	-
0.7787	7450	5.9572	-	-	-
0.7798	7460	5.4038	-	-	-
0.7808	7470	5.4828	-	-	-
0.7819	7480	5.9554	-	-	-
0.7829	7490	5.3782	-	-	-
0.7839	7500	5.3745	-	-	-
0.7850	7510	5.8603	-	-	-
0.7860	7520	4.6921	-	-	-
0.7871	7530	5.9175	-	-	-
0.7881	7540	5.8279	-	-	-
0.7892	7550	5.0900	-	-	-
0.7902	7560	5.3628	-	-	-
0.7913	7570	5.6027	-	-	-
0.7923	7580	5.5124	-	-	-
0.7934	7590	5.5174	-	-	-
0.7944	7600	5.4320	-	-	-
0.7954	7610	5.7467	-	-	-
0.7965	7620	5.4978	-	-	-
0.7975	7630	5.8794	-	-	-
0.7986	7640	5.9752	-	-	-
0.7996	7650	5.8575	-	-	-
0.8007	7660	5.6722	-	-	-
0.8011	7664	-	0.9464	0.7757	0.9475
0.8017	7670	5.5799	-	-	-
0.8028	7680	5.7843	-	-	-
0.8038	7690	5.1803	-	-	-
0.8049	7700	5.0621	-	-	-
0.8059	7710	5.4194	-	-	-
0.8069	7720	5.4373	-	-	-
0.8080	7730	5.7806	-	-	-
0.8090	7740	4.6113	-	-	-
0.8101	7750	5.4602	-	-	-
0.8111	7760	5.4044	-	-	-
0.8122	7770	5.7035	-	-	-
0.8132	7780	5.7093	-	-	-
0.8143	7790	5.5565	-	-	-
0.8153	7800	5.1946	-	-	-
0.8163	7810	4.6171	-	-	-
0.8174	7820	6.0233	-	-	-
0.8184	7830	5.0765	-	-	-
0.8195	7840	5.2966	-	-	-
0.8205	7850	5.1385	-	-	-
0.8216	7860	5.6781	-	-	-
0.8226	7870	5.9824	-	-	-
0.8237	7880	5.2003	-	-	-
0.8247	7890	6.0339	-	-	-
0.8258	7900	5.1430	-	-	-
0.8268	7910	5.4955	-	-	-
0.8278	7920	5.9574	-	-	-
0.8289	7930	5.4407	-	-	-
0.8299	7940	5.4271	-	-	-
0.8310	7950	5.6288	-	-	-
0.8320	7960	4.6513	-	-	-
0.8331	7970	6.0172	-	-	-
0.8341	7980	5.9517	-	-	-
0.8352	7990	5.9860	-	-	-
0.8362	8000	5.0427	-	-	-
0.8373	8010	5.4410	-	-	-
0.8383	8020	5.8218	-	-	-
0.8393	8030	6.0393	-	-	-
0.8404	8040	5.5947	-	-	-
0.8414	8050	4.9512	-	-	-
0.8425	8060	5.3139	-	-	-
0.8435	8070	5.6376	-	-	-
0.8446	8080	5.6569	-	-	-
0.8456	8090	5.1113	-	-	-
0.8467	8100	4.8262	-	-	-
0.8477	8110	5.7394	-	-	-
0.8488	8120	5.6082	-	-	-
0.8498	8130	5.8332	-	-	-
0.8508	8140	5.4075	-	-	-
0.8512	8143	-	0.9367	0.7737	0.9459
0.8519	8150	5.0807	-	-	-
0.8529	8160	5.1852	-	-	-
0.8540	8170	5.5108	-	-	-
0.8550	8180	5.0774	-	-	-
0.8561	8190	5.3312	-	-	-
0.8571	8200	5.2492	-	-	-
0.8582	8210	4.7941	-	-	-
0.8592	8220	5.9581	-	-	-
0.8602	8230	5.1578	-	-	-
0.8613	8240	4.7837	-	-	-
0.8623	8250	5.3941	-	-	-
0.8634	8260	5.3513	-	-	-
0.8644	8270	5.3195	-	-	-
0.8655	8280	5.1222	-	-	-
0.8665	8290	5.4265	-	-	-
0.8676	8300	5.4166	-	-	-
0.8686	8310	5.0012	-	-	-
0.8697	8320	5.4816	-	-	-
0.8707	8330	4.9878	-	-	-
0.8717	8340	5.7982	-	-	-
0.8728	8350	5.1691	-	-	-
0.8738	8360	5.2478	-	-	-
0.8749	8370	5.6111	-	-	-
0.8759	8380	5.3835	-	-	-
0.8770	8390	5.6739	-	-	-
0.8780	8400	4.8203	-	-	-
0.8791	8410	5.3288	-	-	-
0.8801	8420	5.7013	-	-	-
0.8812	8430	5.6653	-	-	-
0.8822	8440	5.3670	-	-	-
0.8832	8450	5.1137	-	-	-
0.8843	8460	5.3491	-	-	-
0.8853	8470	5.5426	-	-	-
0.8864	8480	5.1735	-	-	-
0.8874	8490	5.1831	-	-	-
0.8885	8500	5.6186	-	-	-
0.8895	8510	5.4487	-	-	-
0.8906	8520	5.2472	-	-	-
0.8916	8530	4.9657	-	-	-
0.8927	8540	5.7762	-	-	-
0.8937	8550	5.2084	-	-	-
0.8947	8560	5.3704	-	-	-
0.8958	8570	5.4438	-	-	-
0.8968	8580	4.9669	-	-	-
0.8979	8590	5.3325	-	-	-
0.8989	8600	5.0400	-	-	-
0.9000	8610	5.6515	-	-	-
0.9010	8620	5.3018	-	-	-
0.9012	8622	-	0.9411	0.7812	0.9496
0.9021	8630	4.8083	-	-	-
0.9031	8640	5.4896	-	-	-
0.9041	8650	5.4350	-	-	-
0.9052	8660	5.2057	-	-	-
0.9062	8670	6.2496	-	-	-
0.9073	8680	5.0700	-	-	-
0.9083	8690	5.4620	-	-	-
0.9094	8700	5.6485	-	-	-
0.9104	8710	5.8693	-	-	-
0.9115	8720	5.9363	-	-	-
0.9125	8730	4.9062	-	-	-
0.9136	8740	5.0350	-	-	-
0.9146	8750	5.5748	-	-	-
0.9156	8760	6.0747	-	-	-
0.9167	8770	5.8477	-	-	-
0.9177	8780	5.2310	-	-	-
0.9188	8790	5.3695	-	-	-
0.9198	8800	5.0600	-	-	-
0.9209	8810	5.3772	-	-	-
0.9219	8820	5.4177	-	-	-
0.9230	8830	5.7534	-	-	-
0.9240	8840	5.2277	-	-	-
0.9251	8850	5.2897	-	-	-
0.9261	8860	5.2851	-	-	-
0.9271	8870	5.0995	-	-	-
0.9282	8880	4.8781	-	-	-
0.9292	8890	5.2694	-	-	-
0.9303	8900	4.6971	-	-	-
0.9313	8910	6.0011	-	-	-
0.9324	8920	5.3150	-	-	-
0.9334	8930	5.9239	-	-	-
0.9345	8940	6.0521	-	-	-
0.9355	8950	5.3220	-	-	-
0.9366	8960	5.5840	-	-	-
0.9376	8970	5.4564	-	-	-
0.9386	8980	5.0136	-	-	-
0.9397	8990	5.0305	-	-	-
0.9407	9000	5.3980	-	-	-
0.9418	9010	4.7400	-	-	-
0.9428	9020	5.5491	-	-	-
0.9439	9030	5.4815	-	-	-
0.9449	9040	4.5017	-	-	-
0.9460	9050	5.0556	-	-	-
0.9470	9060	5.5491	-	-	-
0.9481	9070	5.2417	-	-	-
0.9491	9080	5.1847	-	-	-
0.9501	9090	4.9169	-	-	-
0.9512	9100	5.2210	-	-	-
0.9513	9101	-	0.9432	0.7799	0.9495
0.9522	9110	5.0194	-	-	-
0.9533	9120	5.6970	-	-	-
0.9543	9130	4.7173	-	-	-
0.9554	9140	5.1799	-	-	-
0.9564	9150	5.6866	-	-	-
0.9575	9160	5.4585	-	-	-
0.9585	9170	5.6573	-	-	-
0.9595	9180	5.5710	-	-	-
0.9606	9190	5.6039	-	-	-
0.9616	9200	5.6509	-	-	-
0.9627	9210	5.3539	-	-	-
0.9637	9220	5.7121	-	-	-
0.9648	9230	5.7096	-	-	-
0.9658	9240	5.8567	-	-	-
0.9669	9250	5.3887	-	-	-
0.9679	9260	5.3783	-	-	-
0.9690	9270	5.1182	-	-	-
0.9700	9280	5.5447	-	-	-
0.9710	9290	5.4235	-	-	-
0.9721	9300	5.2533	-	-	-
0.9731	9310	5.4380	-	-	-
0.9742	9320	6.1285	-	-	-
0.9752	9330	5.1483	-	-	-
0.9763	9340	5.1581	-	-	-
0.9773	9350	5.4102	-	-	-
0.9784	9360	5.1247	-	-	-
0.9794	9370	5.0264	-	-	-
0.9805	9380	5.4215	-	-	-
0.9815	9390	5.1006	-	-	-
0.9825	9400	5.4054	-	-	-
0.9836	9410	5.4740	-	-	-
0.9846	9420	4.9311	-	-	-
0.9857	9430	5.2327	-	-	-
0.9867	9440	5.1672	-	-	-
0.9878	9450	5.3044	-	-	-
0.9888	9460	5.6912	-	-	-
0.9899	9470	5.0527	-	-	-
0.9909	9480	5.4718	-	-	-
0.9920	9490	5.2244	-	-	-
0.9930	9500	5.5866	-	-	-
0.9940	9510	5.7772	-	-	-
0.9951	9520	5.0796	-	-	-
0.9961	9530	4.8196	-	-	-
0.9972	9540	5.5365	-	-	-
0.9982	9550	5.4738	-	-	-
0.9993	9560	5.6570	-	-	-

Framework Versions

Python: 3.10.12
Sentence Transformers: 5.3.0
PyLate: 1.6.0
Transformers: 5.3.0
PyTorch: 2.8.0+cu128
Accelerate: 1.14.0
Datasets: 5.0.1
Tokenizers: 0.22.2

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"
}

PyLate

bibtex

@inproceedings{DBLP:conf/cikm/ChaffinS25,
  author       = {Antoine Chaffin and
                  Rapha{"{e}}l Sourty},
  editor       = {Meeyoung Cha and
                  Chanyoung Park and
                  Noseong Park and
                  Carl Yang and
                  Senjuti Basu Roy and
                  Jessie Li and
                  Jaap Kamps and
                  Kijung Shin and
                  Bryan Hooi and
                  Lifang He},
  title        = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
  booktitle    = {Proceedings of the 34th {ACM} International Conference on Information
                  and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
                  10-14, 2025},
  pages        = {6334--6339},
  publisher    = {{ACM}},
  year         = {2025},
  url          = {https://github.com/lightonai/pylate},
  doi          = {10.1145/3746252.3761608},
}

yjoonjang/mmBERT-en-CL-KD

PyLate model based on jhu-clsp/mmBERT-base

Performance on MTEB(kor, v2)

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Retrieval

Indexing documents

Retrieving top-k documents for queries

Reranking

Evaluation

Metrics

Py Late Information Retrieval

Training Details

Training Dataset

en

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

PyLate

yjoonjang/
mmBERT-en-CL-KD