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	0.7432	0.93
MaxSim_accuracy@10	1.0	0.9037	0.9878
MaxSim_precision@10	0.1	0.1547	0.0988
MaxSim_precision@100	0.01	0.0171	0.01
MaxSim_recall@10	1.0	0.8431	0.9878
MaxSim_recall@100	1.0	0.91	0.9978
MaxSim_ndcg@10	0.9423	0.7807	0.9599
MaxSim_mrr@10	0.9231	0.8018	0.9508
MaxSim_map@100	0.9231	0.7378	0.9512

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	46.2091	-	-	-
0.0021	20	44.8447	-	-	-
0.0031	30	44.3277	-	-	-
0.0042	40	42.8585	-	-	-
0.0052	50	40.2818	-	-	-
0.0063	60	37.4674	-	-	-
0.0073	70	34.2220	-	-	-
0.0084	80	30.7428	-	-	-
0.0094	90	27.1819	-	-	-
0.0105	100	23.4274	-	-	-
0.0115	110	20.8635	-	-	-
0.0125	120	19.2072	-	-	-
0.0136	130	16.5589	-	-	-
0.0146	140	16.7683	-	-	-
0.0157	150	15.4142	-	-	-
0.0167	160	15.4363	-	-	-
0.0178	170	14.3129	-	-	-
0.0188	180	13.4026	-	-	-
0.0199	190	13.4165	-	-	-
0.0209	200	12.6225	-	-	-
0.0220	210	11.9549	-	-	-
0.0230	220	11.3673	-	-	-
0.0240	230	11.3249	-	-	-
0.0251	240	11.2256	-	-	-
0.0261	250	11.5267	-	-	-
0.0272	260	10.5344	-	-	-
0.0282	270	10.4401	-	-	-
0.0293	280	9.7917	-	-	-
0.0303	290	9.7701	-	-	-
0.0314	300	9.6410	-	-	-
0.0324	310	10.3398	-	-	-
0.0334	320	9.4691	-	-	-
0.0345	330	9.3615	-	-	-
0.0355	340	9.5549	-	-	-
0.0366	350	8.6381	-	-	-
0.0376	360	8.7217	-	-	-
0.0387	370	8.7113	-	-	-
0.0397	380	8.4534	-	-	-
0.0408	390	8.8197	-	-	-
0.0418	400	8.1004	-	-	-
0.0429	410	8.5684	-	-	-
0.0439	420	8.4450	-	-	-
0.0449	430	7.9528	-	-	-
0.0460	440	8.9661	-	-	-
0.0470	450	7.9859	-	-	-
0.0481	460	8.2765	-	-	-
0.0491	470	8.4203	-	-	-
0.0501	479	-	0.8165	0.6319	0.8528
0.0502	480	8.0639	-	-	-
0.0512	490	7.8889	-	-	-
0.0523	500	8.5628	-	-	-
0.0533	510	7.4058	-	-	-
0.0544	520	7.2514	-	-	-
0.0554	530	7.2295	-	-	-
0.0564	540	7.4430	-	-	-
0.0575	550	7.9635	-	-	-
0.0585	560	7.6715	-	-	-
0.0596	570	7.1274	-	-	-
0.0606	580	7.2552	-	-	-
0.0617	590	6.9687	-	-	-
0.0627	600	6.7897	-	-	-
0.0638	610	6.6180	-	-	-
0.0648	620	6.4554	-	-	-
0.0659	630	7.2579	-	-	-
0.0669	640	6.4389	-	-	-
0.0679	650	6.5976	-	-	-
0.0690	660	7.0588	-	-	-
0.0700	670	7.0637	-	-	-
0.0711	680	6.1421	-	-	-
0.0721	690	5.8640	-	-	-
0.0732	700	6.2579	-	-	-
0.0742	710	7.0820	-	-	-
0.0753	720	6.5089	-	-	-
0.0763	730	6.3020	-	-	-
0.0773	740	5.6451	-	-	-
0.0784	750	6.3496	-	-	-
0.0794	760	6.0731	-	-	-
0.0805	770	6.4568	-	-	-
0.0815	780	6.2126	-	-	-
0.0826	790	6.6487	-	-	-
0.0836	800	5.4586	-	-	-
0.0847	810	5.3937	-	-	-
0.0857	820	5.9520	-	-	-
0.0868	830	5.9517	-	-	-
0.0878	840	6.1674	-	-	-
0.0888	850	5.9362	-	-	-
0.0899	860	6.4283	-	-	-
0.0909	870	6.0619	-	-	-
0.0920	880	5.7554	-	-	-
0.0930	890	5.5457	-	-	-
0.0941	900	5.5574	-	-	-
0.0951	910	5.8586	-	-	-
0.0962	920	5.0635	-	-	-
0.0972	930	5.6730	-	-	-
0.0983	940	5.7147	-	-	-
0.0993	950	5.2349	-	-	-
0.1001	958	-	0.9111	0.7274	0.9405
0.1003	960	5.7501	-	-	-
0.1014	970	6.1180	-	-	-
0.1024	980	5.2666	-	-	-
0.1035	990	5.6296	-	-	-
0.1045	1000	5.4542	-	-	-
0.1056	1010	5.5758	-	-	-
0.1066	1020	5.5970	-	-	-
0.1077	1030	5.5825	-	-	-
0.1087	1040	5.5949	-	-	-
0.1098	1050	6.0259	-	-	-
0.1108	1060	5.3556	-	-	-
0.1118	1070	5.0652	-	-	-
0.1129	1080	5.5211	-	-	-
0.1139	1090	5.8450	-	-	-
0.1150	1100	5.1471	-	-	-
0.1160	1110	5.4966	-	-	-
0.1171	1120	5.0071	-	-	-
0.1181	1130	5.3421	-	-	-
0.1192	1140	5.2694	-	-	-
0.1202	1150	4.7237	-	-	-
0.1213	1160	5.3324	-	-	-
0.1223	1170	5.1923	-	-	-
0.1233	1180	4.9403	-	-	-
0.1244	1190	4.8556	-	-	-
0.1254	1200	5.2487	-	-	-
0.1265	1210	5.1158	-	-	-
0.1275	1220	5.0475	-	-	-
0.1286	1230	4.9384	-	-	-
0.1296	1240	5.3601	-	-	-
0.1307	1250	4.8390	-	-	-
0.1317	1260	5.3765	-	-	-
0.1327	1270	4.7537	-	-	-
0.1338	1280	5.3173	-	-	-
0.1348	1290	5.3265	-	-	-
0.1359	1300	5.0792	-	-	-
0.1369	1310	4.8160	-	-	-
0.1380	1320	4.7652	-	-	-
0.1390	1330	5.0686	-	-	-
0.1401	1340	4.6717	-	-	-
0.1411	1350	5.2954	-	-	-
0.1422	1360	4.5524	-	-	-
0.1432	1370	5.3866	-	-	-
0.1442	1380	4.9627	-	-	-
0.1453	1390	4.4577	-	-	-
0.1463	1400	4.4149	-	-	-
0.1474	1410	4.5377	-	-	-
0.1484	1420	4.6458	-	-	-
0.1495	1430	5.0964	-	-	-
0.1502	1437	-	0.9294	0.7446	0.9425
0.1505	1440	4.6004	-	-	-
0.1516	1450	4.7434	-	-	-
0.1526	1460	4.5973	-	-	-
0.1537	1470	4.8758	-	-	-
0.1547	1480	5.1328	-	-	-
0.1557	1490	4.5946	-	-	-
0.1568	1500	4.5913	-	-	-
0.1578	1510	4.9194	-	-	-
0.1589	1520	4.5424	-	-	-
0.1599	1530	4.9346	-	-	-
0.1610	1540	4.5107	-	-	-
0.1620	1550	4.2763	-	-	-
0.1631	1560	4.1741	-	-	-
0.1641	1570	5.0282	-	-	-
0.1652	1580	4.9537	-	-	-
0.1662	1590	4.4177	-	-	-
0.1672	1600	4.1158	-	-	-
0.1683	1610	4.4747	-	-	-
0.1693	1620	4.3338	-	-	-
0.1704	1630	4.7027	-	-	-
0.1714	1640	4.5610	-	-	-
0.1725	1650	4.2773	-	-	-
0.1735	1660	4.6103	-	-	-
0.1746	1670	4.7944	-	-	-
0.1756	1680	4.4668	-	-	-
0.1766	1690	4.6387	-	-	-
0.1777	1700	4.3509	-	-	-
0.1787	1710	4.4754	-	-	-
0.1798	1720	4.0314	-	-	-
0.1808	1730	3.9788	-	-	-
0.1819	1740	4.7455	-	-	-
0.1829	1750	4.6095	-	-	-
0.1840	1760	4.7708	-	-	-
0.1850	1770	4.6487	-	-	-
0.1861	1780	4.6331	-	-	-
0.1871	1790	4.3817	-	-	-
0.1881	1800	4.3205	-	-	-
0.1892	1810	4.3616	-	-	-
0.1902	1820	5.0779	-	-	-
0.1913	1830	4.3325	-	-	-
0.1923	1840	4.6020	-	-	-
0.1934	1850	4.4358	-	-	-
0.1944	1860	4.2510	-	-	-
0.1955	1870	4.2217	-	-	-
0.1965	1880	4.3225	-	-	-
0.1976	1890	4.4409	-	-	-
0.1986	1900	3.9217	-	-	-
0.1996	1910	3.9213	-	-	-
0.2003	1916	-	0.9455	0.7476	0.9510
0.2007	1920	4.5634	-	-	-
0.2017	1930	4.6786	-	-	-
0.2028	1940	4.3416	-	-	-
0.2038	1950	3.8755	-	-	-
0.2049	1960	3.8782	-	-	-
0.2059	1970	3.8221	-	-	-
0.2070	1980	4.2674	-	-	-
0.2080	1990	4.1919	-	-	-
0.2091	2000	3.9949	-	-	-
0.2101	2010	3.9339	-	-	-
0.2111	2020	4.3739	-	-	-
0.2122	2030	4.1333	-	-	-
0.2132	2040	3.7659	-	-	-
0.2143	2050	4.3739	-	-	-
0.2153	2060	4.2548	-	-	-
0.2164	2070	3.7585	-	-	-
0.2174	2080	4.0651	-	-	-
0.2185	2090	3.9052	-	-	-
0.2195	2100	4.4214	-	-	-
0.2205	2110	4.1958	-	-	-
0.2216	2120	4.0892	-	-	-
0.2226	2130	4.3649	-	-	-
0.2237	2140	4.3085	-	-	-
0.2247	2150	4.1002	-	-	-
0.2258	2160	3.7709	-	-	-
0.2268	2170	4.0009	-	-	-
0.2279	2180	4.1302	-	-	-
0.2289	2190	4.5455	-	-	-
0.2300	2200	4.1756	-	-	-
0.2310	2210	3.7365	-	-	-
0.2320	2220	3.9900	-	-	-
0.2331	2230	4.3130	-	-	-
0.2341	2240	3.8795	-	-	-
0.2352	2250	4.3693	-	-	-
0.2362	2260	4.0762	-	-	-
0.2373	2270	3.5636	-	-	-
0.2383	2280	3.9004	-	-	-
0.2394	2290	3.9497	-	-	-
0.2404	2300	4.1567	-	-	-
0.2415	2310	3.6716	-	-	-
0.2425	2320	3.8828	-	-	-
0.2435	2330	4.3537	-	-	-
0.2446	2340	3.7551	-	-	-
0.2456	2350	3.8248	-	-	-
0.2467	2360	3.7938	-	-	-
0.2477	2370	4.2537	-	-	-
0.2488	2380	3.8145	-	-	-
0.2498	2390	3.6579	-	-	-
0.2503	2395	-	0.9380	0.7535	0.9482
0.2509	2400	4.1464	-	-	-
0.2519	2410	4.6173	-	-	-
0.2530	2420	3.6466	-	-	-
0.2540	2430	4.4148	-	-	-
0.2550	2440	4.2940	-	-	-
0.2561	2450	3.7966	-	-	-
0.2571	2460	3.9590	-	-	-
0.2582	2470	4.0761	-	-	-
0.2592	2480	3.9668	-	-	-
0.2603	2490	4.0561	-	-	-
0.2613	2500	4.1422	-	-	-
0.2624	2510	4.1923	-	-	-
0.2634	2520	3.8605	-	-	-
0.2645	2530	3.7975	-	-	-
0.2655	2540	4.1194	-	-	-
0.2665	2550	4.0243	-	-	-
0.2676	2560	4.3033	-	-	-
0.2686	2570	3.6871	-	-	-
0.2697	2580	3.9822	-	-	-
0.2707	2590	3.8237	-	-	-
0.2718	2600	4.0512	-	-	-
0.2728	2610	3.8920	-	-	-
0.2739	2620	4.0916	-	-	-
0.2749	2630	4.4035	-	-	-
0.2759	2640	3.6894	-	-	-
0.2770	2650	4.1621	-	-	-
0.2780	2660	3.5706	-	-	-
0.2791	2670	3.6533	-	-	-
0.2801	2680	4.1609	-	-	-
0.2812	2690	3.6793	-	-	-
0.2822	2700	3.9226	-	-	-
0.2833	2710	3.8775	-	-	-
0.2843	2720	3.9776	-	-	-
0.2854	2730	4.0196	-	-	-
0.2864	2740	3.8927	-	-	-
0.2874	2750	3.7200	-	-	-
0.2885	2760	3.6425	-	-	-
0.2895	2770	3.9153	-	-	-
0.2906	2780	3.6153	-	-	-
0.2916	2790	3.9675	-	-	-
0.2927	2800	4.2646	-	-	-
0.2937	2810	3.9695	-	-	-
0.2948	2820	3.7208	-	-	-
0.2958	2830	4.0357	-	-	-
0.2969	2840	3.8410	-	-	-
0.2979	2850	3.7762	-	-	-
0.2989	2860	3.9416	-	-	-
0.3000	2870	3.9695	-	-	-
0.3004	2874	-	0.9389	0.7703	0.9538
0.3010	2880	3.8019	-	-	-
0.3021	2890	4.3067	-	-	-
0.3031	2900	4.0153	-	-	-
0.3042	2910	3.5201	-	-	-
0.3052	2920	3.6554	-	-	-
0.3063	2930	3.4772	-	-	-
0.3073	2940	4.2149	-	-	-
0.3084	2950	3.8477	-	-	-
0.3094	2960	3.5512	-	-	-
0.3104	2970	3.1203	-	-	-
0.3115	2980	4.0222	-	-	-
0.3125	2990	4.1236	-	-	-
0.3136	3000	3.9607	-	-	-
0.3146	3010	3.9860	-	-	-
0.3157	3020	3.7009	-	-	-
0.3167	3030	3.3203	-	-	-
0.3178	3040	3.6223	-	-	-
0.3188	3050	3.4352	-	-	-
0.3198	3060	4.1516	-	-	-
0.3209	3070	3.7955	-	-	-
0.3219	3080	3.9558	-	-	-
0.3230	3090	3.8979	-	-	-
0.3240	3100	3.5395	-	-	-
0.3251	3110	3.6903	-	-	-
0.3261	3120	3.9248	-	-	-
0.3272	3130	3.5310	-	-	-
0.3282	3140	3.8741	-	-	-
0.3293	3150	3.6821	-	-	-
0.3303	3160	3.8081	-	-	-
0.3313	3170	3.4591	-	-	-
0.3324	3180	3.6389	-	-	-
0.3334	3190	3.0269	-	-	-
0.3345	3200	3.9073	-	-	-
0.3355	3210	3.4570	-	-	-
0.3366	3220	3.4819	-	-	-
0.3376	3230	3.2834	-	-	-
0.3387	3240	3.4783	-	-	-
0.3397	3250	3.4646	-	-	-
0.3408	3260	4.0734	-	-	-
0.3418	3270	3.8641	-	-	-
0.3428	3280	3.5274	-	-	-
0.3439	3290	3.5806	-	-	-
0.3449	3300	3.4979	-	-	-
0.3460	3310	3.5729	-	-	-
0.3470	3320	3.6917	-	-	-
0.3481	3330	3.9194	-	-	-
0.3491	3340	3.7660	-	-	-
0.3502	3350	3.7487	-	-	-
0.3505	3353	-	0.9367	0.7627	0.9557
0.3512	3360	3.6947	-	-	-
0.3523	3370	3.5374	-	-	-
0.3533	3380	3.6227	-	-	-
0.3543	3390	4.0427	-	-	-
0.3554	3400	3.2790	-	-	-
0.3564	3410	3.5423	-	-	-
0.3575	3420	3.6463	-	-	-
0.3585	3430	3.4895	-	-	-
0.3596	3440	3.4556	-	-	-
0.3606	3450	3.8894	-	-	-
0.3617	3460	3.8537	-	-	-
0.3627	3470	3.4285	-	-	-
0.3638	3480	3.5311	-	-	-
0.3648	3490	3.5475	-	-	-
0.3658	3500	3.6967	-	-	-
0.3669	3510	3.9842	-	-	-
0.3679	3520	3.7880	-	-	-
0.3690	3530	4.2557	-	-	-
0.3700	3540	3.5412	-	-	-
0.3711	3550	3.7756	-	-	-
0.3721	3560	3.7117	-	-	-
0.3732	3570	3.5600	-	-	-
0.3742	3580	3.6923	-	-	-
0.3752	3590	3.6120	-	-	-
0.3763	3600	3.7892	-	-	-
0.3773	3610	3.8289	-	-	-
0.3784	3620	3.6522	-	-	-
0.3794	3630	3.6082	-	-	-
0.3805	3640	4.1901	-	-	-
0.3815	3650	3.4512	-	-	-
0.3826	3660	3.6182	-	-	-
0.3836	3670	3.7004	-	-	-
0.3847	3680	3.9057	-	-	-
0.3857	3690	3.5888	-	-	-
0.3867	3700	4.0049	-	-	-
0.3878	3710	3.3720	-	-	-
0.3888	3720	3.6777	-	-	-
0.3899	3730	3.4481	-	-	-
0.3909	3740	3.4092	-	-	-
0.3920	3750	3.1657	-	-	-
0.3930	3760	3.5251	-	-	-
0.3941	3770	3.3502	-	-	-
0.3951	3780	3.6759	-	-	-
0.3962	3790	3.4920	-	-	-
0.3972	3800	3.4481	-	-	-
0.3982	3810	3.5784	-	-	-
0.3993	3820	3.5019	-	-	-
0.4003	3830	3.8835	-	-	-
0.4005	3832	-	0.9440	0.7793	0.9609
0.4014	3840	3.4840	-	-	-
0.4024	3850	3.3851	-	-	-
0.4035	3860	3.3280	-	-	-
0.4045	3870	3.3134	-	-	-
0.4056	3880	3.4897	-	-	-
0.4066	3890	3.7290	-	-	-
0.4077	3900	3.5043	-	-	-
0.4087	3910	3.3659	-	-	-
0.4097	3920	3.4737	-	-	-
0.4108	3930	3.6820	-	-	-
0.4118	3940	3.2581	-	-	-
0.4129	3950	3.5298	-	-	-
0.4139	3960	3.6048	-	-	-
0.4150	3970	3.3555	-	-	-
0.4160	3980	3.4481	-	-	-
0.4171	3990	3.6716	-	-	-
0.4181	4000	3.4296	-	-	-
0.4191	4010	3.4741	-	-	-
0.4202	4020	3.5437	-	-	-
0.4212	4030	3.2175	-	-	-
0.4223	4040	3.8077	-	-	-
0.4233	4050	3.7798	-	-	-
0.4244	4060	3.5918	-	-	-
0.4254	4070	3.6300	-	-	-
0.4265	4080	3.5510	-	-	-
0.4275	4090	3.3115	-	-	-
0.4286	4100	3.3353	-	-	-
0.4296	4110	3.1905	-	-	-
0.4306	4120	3.2659	-	-	-
0.4317	4130	3.5515	-	-	-
0.4327	4140	3.4594	-	-	-
0.4338	4150	3.7036	-	-	-
0.4348	4160	3.4691	-	-	-
0.4359	4170	3.7943	-	-	-
0.4369	4180	3.6109	-	-	-
0.4380	4190	3.7741	-	-	-
0.4390	4200	3.5559	-	-	-
0.4401	4210	3.3794	-	-	-
0.4411	4220	3.2639	-	-	-
0.4421	4230	3.4036	-	-	-
0.4432	4240	3.2700	-	-	-
0.4442	4250	3.4337	-	-	-
0.4453	4260	3.7614	-	-	-
0.4463	4270	3.4683	-	-	-
0.4474	4280	3.0771	-	-	-
0.4484	4290	3.7126	-	-	-
0.4495	4300	2.9577	-	-	-
0.4505	4310	3.6783	-	-	-
0.4506	4311	-	0.9417	0.7734	0.9562
0.4516	4320	3.7019	-	-	-
0.4526	4330	3.6088	-	-	-
0.4536	4340	3.7624	-	-	-
0.4547	4350	3.4703	-	-	-
0.4557	4360	3.0562	-	-	-
0.4568	4370	3.5901	-	-	-
0.4578	4380	3.2788	-	-	-
0.4589	4390	3.2149	-	-	-
0.4599	4400	3.7400	-	-	-
0.4610	4410	3.3619	-	-	-
0.4620	4420	3.3232	-	-	-
0.4631	4430	3.5791	-	-	-
0.4641	4440	3.4229	-	-	-
0.4651	4450	3.4527	-	-	-
0.4662	4460	3.2919	-	-	-
0.4672	4470	3.2088	-	-	-
0.4683	4480	2.9130	-	-	-
0.4693	4490	3.1803	-	-	-
0.4704	4500	3.2438	-	-	-
0.4714	4510	3.5128	-	-	-
0.4725	4520	3.1457	-	-	-
0.4735	4530	3.3433	-	-	-
0.4745	4540	3.0423	-	-	-
0.4756	4550	3.1949	-	-	-
0.4766	4560	3.0340	-	-	-
0.4777	4570	3.1985	-	-	-
0.4787	4580	3.3075	-	-	-
0.4798	4590	3.3502	-	-	-
0.4808	4600	3.2671	-	-	-
0.4819	4610	3.2807	-	-	-
0.4829	4620	3.2715	-	-	-
0.4840	4630	3.4984	-	-	-
0.4850	4640	3.4240	-	-	-
0.4860	4650	3.0397	-	-	-
0.4871	4660	3.1434	-	-	-
0.4881	4670	3.1731	-	-	-
0.4892	4680	3.7031	-	-	-
0.4902	4690	3.3188	-	-	-
0.4913	4700	3.3302	-	-	-
0.4923	4710	3.2008	-	-	-
0.4934	4720	2.9472	-	-	-
0.4944	4730	3.4778	-	-	-
0.4955	4740	3.1757	-	-	-
0.4965	4750	3.2938	-	-	-
0.4975	4760	3.5116	-	-	-
0.4986	4770	3.6895	-	-	-
0.4996	4780	3.3946	-	-	-
0.5007	4790	3.2443	0.9389	0.7727	0.9574
0.5017	4800	3.0681	-	-	-
0.5028	4810	3.0704	-	-	-
0.5038	4820	3.2642	-	-	-
0.5049	4830	3.3654	-	-	-
0.5059	4840	3.0907	-	-	-
0.5070	4850	3.2028	-	-	-
0.5080	4860	3.1806	-	-	-
0.5090	4870	2.9388	-	-	-
0.5101	4880	3.2691	-	-	-
0.5111	4890	3.4761	-	-	-
0.5122	4900	2.7103	-	-	-
0.5132	4910	2.9657	-	-	-
0.5143	4920	3.3092	-	-	-
0.5153	4930	3.0619	-	-	-
0.5164	4940	3.0552	-	-	-
0.5174	4950	3.4739	-	-	-
0.5184	4960	3.4565	-	-	-
0.5195	4970	3.1772	-	-	-
0.5205	4980	3.1930	-	-	-
0.5216	4990	3.7420	-	-	-
0.5226	5000	3.6115	-	-	-
0.5237	5010	2.9353	-	-	-
0.5247	5020	3.1397	-	-	-
0.5258	5030	3.4359	-	-	-
0.5268	5040	3.2201	-	-	-
0.5279	5050	3.6529	-	-	-
0.5289	5060	3.4384	-	-	-
0.5299	5070	3.5596	-	-	-
0.5310	5080	3.7967	-	-	-
0.5320	5090	3.1165	-	-	-
0.5331	5100	3.2096	-	-	-
0.5341	5110	3.1405	-	-	-
0.5352	5120	3.1374	-	-	-
0.5362	5130	3.4753	-	-	-
0.5373	5140	3.5819	-	-	-
0.5383	5150	2.7688	-	-	-
0.5394	5160	3.3101	-	-	-
0.5404	5170	3.2971	-	-	-
0.5414	5180	3.1514	-	-	-
0.5425	5190	3.0098	-	-	-
0.5435	5200	3.0270	-	-	-
0.5446	5210	3.8057	-	-	-
0.5456	5220	3.2875	-	-	-
0.5467	5230	3.2085	-	-	-
0.5477	5240	3.7086	-	-	-
0.5488	5250	3.5427	-	-	-
0.5498	5260	3.2585	-	-	-
0.5507	5269	-	0.9551	0.7739	0.9576
0.5509	5270	3.6734	-	-	-
0.5519	5280	3.0635	-	-	-
0.5529	5290	3.0950	-	-	-
0.5540	5300	2.9376	-	-	-
0.5550	5310	2.7345	-	-	-
0.5561	5320	3.2024	-	-	-
0.5571	5330	3.4806	-	-	-
0.5582	5340	3.1370	-	-	-
0.5592	5350	3.0835	-	-	-
0.5603	5360	3.0591	-	-	-
0.5613	5370	3.1138	-	-	-
0.5623	5380	3.0194	-	-	-
0.5634	5390	3.1118	-	-	-
0.5644	5400	3.2450	-	-	-
0.5655	5410	2.9750	-	-	-
0.5665	5420	3.0304	-	-	-
0.5676	5430	2.8808	-	-	-
0.5686	5440	3.3748	-	-	-
0.5697	5450	3.0163	-	-	-
0.5707	5460	3.0909	-	-	-
0.5718	5470	3.3591	-	-	-
0.5728	5480	3.4815	-	-	-
0.5738	5490	3.0499	-	-	-
0.5749	5500	3.1688	-	-	-
0.5759	5510	3.4054	-	-	-
0.5770	5520	3.3196	-	-	-
0.5780	5530	2.9470	-	-	-
0.5791	5540	3.2159	-	-	-
0.5801	5550	2.9220	-	-	-
0.5812	5560	2.6582	-	-	-
0.5822	5570	3.3886	-	-	-
0.5833	5580	3.4880	-	-	-
0.5843	5590	3.0641	-	-	-
0.5853	5600	3.3180	-	-	-
0.5864	5610	3.6548	-	-	-
0.5874	5620	3.2405	-	-	-
0.5885	5630	3.3945	-	-	-
0.5895	5640	3.0647	-	-	-
0.5906	5650	3.1413	-	-	-
0.5916	5660	3.0183	-	-	-
0.5927	5670	2.9131	-	-	-
0.5937	5680	3.2563	-	-	-
0.5948	5690	3.0822	-	-	-
0.5958	5700	3.2023	-	-	-
0.5968	5710	3.2782	-	-	-
0.5979	5720	3.2896	-	-	-
0.5989	5730	3.0422	-	-	-
0.6000	5740	2.9203	-	-	-
0.6008	5748	-	0.9439	0.7737	0.9556
0.6010	5750	3.3046	-	-	-
0.6021	5760	2.9995	-	-	-
0.6031	5770	2.8474	-	-	-
0.6042	5780	2.9989	-	-	-
0.6052	5790	3.3842	-	-	-
0.6063	5800	2.8906	-	-	-
0.6073	5810	2.9977	-	-	-
0.6083	5820	3.0662	-	-	-
0.6094	5830	3.2086	-	-	-
0.6104	5840	3.2307	-	-	-
0.6115	5850	3.2381	-	-	-
0.6125	5860	2.9657	-	-	-
0.6136	5870	3.2089	-	-	-
0.6146	5880	3.1070	-	-	-
0.6157	5890	3.2926	-	-	-
0.6167	5900	3.0993	-	-	-
0.6177	5910	3.2811	-	-	-
0.6188	5920	3.2418	-	-	-
0.6198	5930	3.0307	-	-	-
0.6209	5940	3.3551	-	-	-
0.6219	5950	3.1731	-	-	-
0.6230	5960	3.0853	-	-	-
0.6240	5970	3.3297	-	-	-
0.6251	5980	3.0738	-	-	-
0.6261	5990	3.1260	-	-	-
0.6272	6000	3.0320	-	-	-
0.6282	6010	3.0940	-	-	-
0.6292	6020	3.2201	-	-	-
0.6303	6030	3.1277	-	-	-
0.6313	6040	3.1603	-	-	-
0.6324	6050	2.6392	-	-	-
0.6334	6060	3.1787	-	-	-
0.6345	6070	3.1578	-	-	-
0.6355	6080	3.0864	-	-	-
0.6366	6090	3.1115	-	-	-
0.6376	6100	3.1791	-	-	-
0.6387	6110	3.1318	-	-	-
0.6397	6120	2.9826	-	-	-
0.6407	6130	3.1090	-	-	-
0.6418	6140	3.1322	-	-	-
0.6428	6150	3.1874	-	-	-
0.6439	6160	3.2759	-	-	-
0.6449	6170	3.0154	-	-	-
0.6460	6180	2.8917	-	-	-
0.6470	6190	3.3126	-	-	-
0.6481	6200	3.1859	-	-	-
0.6491	6210	2.7294	-	-	-
0.6502	6220	3.3816	-	-	-
0.6509	6227	-	0.9511	0.7842	0.9627
0.6512	6230	3.1273	-	-	-
0.6522	6240	3.3160	-	-	-
0.6533	6250	2.8875	-	-	-
0.6543	6260	3.1625	-	-	-
0.6554	6270	3.0617	-	-	-
0.6564	6280	3.1622	-	-	-
0.6575	6290	2.9219	-	-	-
0.6585	6300	3.0320	-	-	-
0.6596	6310	2.8679	-	-	-
0.6606	6320	2.9426	-	-	-
0.6616	6330	3.2016	-	-	-
0.6627	6340	3.0621	-	-	-
0.6637	6350	2.9824	-	-	-
0.6648	6360	3.0600	-	-	-
0.6658	6370	3.1925	-	-	-
0.6669	6380	2.8263	-	-	-
0.6679	6390	2.9975	-	-	-
0.6690	6400	3.0865	-	-	-
0.6700	6410	3.1987	-	-	-
0.6711	6420	2.8231	-	-	-
0.6721	6430	3.2760	-	-	-
0.6731	6440	3.1261	-	-	-
0.6742	6450	2.9704	-	-	-
0.6752	6460	2.8117	-	-	-
0.6763	6470	2.6916	-	-	-
0.6773	6480	2.9525	-	-	-
0.6784	6490	3.3812	-	-	-
0.6794	6500	2.9883	-	-	-
0.6805	6510	3.1305	-	-	-
0.6815	6520	2.8783	-	-	-
0.6826	6530	3.0539	-	-	-
0.6836	6540	3.1196	-	-	-
0.6846	6550	3.3115	-	-	-
0.6857	6560	3.1721	-	-	-
0.6867	6570	3.2426	-	-	-
0.6878	6580	2.7998	-	-	-
0.6888	6590	2.9493	-	-	-
0.6899	6600	3.2202	-	-	-
0.6909	6610	3.1696	-	-	-
0.6920	6620	2.9060	-	-	-
0.6930	6630	3.0832	-	-	-
0.6941	6640	3.1196	-	-	-
0.6951	6650	2.6949	-	-	-
0.6961	6660	3.1100	-	-	-
0.6972	6670	3.2124	-	-	-
0.6982	6680	2.9540	-	-	-
0.6993	6690	2.9221	-	-	-
0.7003	6700	2.6419	-	-	-
0.7010	6706	-	0.9399	0.7840	0.9597
0.7014	6710	3.0086	-	-	-
0.7024	6720	3.2360	-	-	-
0.7035	6730	2.9315	-	-	-
0.7045	6740	3.3634	-	-	-
0.7056	6750	3.0409	-	-	-
0.7066	6760	2.9353	-	-	-
0.7076	6770	2.9283	-	-	-
0.7087	6780	3.0781	-	-	-
0.7097	6790	3.0544	-	-	-
0.7108	6800	2.7957	-	-	-
0.7118	6810	3.2403	-	-	-
0.7129	6820	2.9436	-	-	-
0.7139	6830	2.9325	-	-	-
0.7150	6840	2.8483	-	-	-
0.7160	6850	3.0314	-	-	-
0.7170	6860	3.1667	-	-	-
0.7181	6870	3.2673	-	-	-
0.7191	6880	2.9235	-	-	-
0.7202	6890	2.7092	-	-	-
0.7212	6900	2.8491	-	-	-
0.7223	6910	3.2094	-	-	-
0.7233	6920	3.1572	-	-	-
0.7244	6930	3.1763	-	-	-
0.7254	6940	3.0369	-	-	-
0.7265	6950	3.0985	-	-	-
0.7275	6960	3.3817	-	-	-
0.7285	6970	3.4014	-	-	-
0.7296	6980	2.6468	-	-	-
0.7306	6990	2.9104	-	-	-
0.7317	7000	2.7150	-	-	-
0.7327	7010	2.6260	-	-	-
0.7338	7020	3.2209	-	-	-
0.7348	7030	2.9279	-	-	-
0.7359	7040	3.1306	-	-	-
0.7369	7050	3.0039	-	-	-
0.7380	7060	3.5099	-	-	-
0.7390	7070	2.9918	-	-	-
0.7400	7080	3.3389	-	-	-
0.7411	7090	3.1801	-	-	-
0.7421	7100	3.2960	-	-	-
0.7432	7110	3.0879	-	-	-
0.7442	7120	2.8828	-	-	-
0.7453	7130	3.1814	-	-	-
0.7463	7140	3.0058	-	-	-
0.7474	7150	2.8409	-	-	-
0.7484	7160	3.0292	-	-	-
0.7495	7170	3.2587	-	-	-
0.7505	7180	3.4095	-	-	-
0.7510	7185	-	0.9425	0.7825	0.9594
0.7515	7190	2.7569	-	-	-
0.7526	7200	3.1080	-	-	-
0.7536	7210	3.1538	-	-	-
0.7547	7220	2.9974	-	-	-
0.7557	7230	2.9961	-	-	-
0.7568	7240	2.8684	-	-	-
0.7578	7250	3.0390	-	-	-
0.7589	7260	3.0550	-	-	-
0.7599	7270	2.8912	-	-	-
0.7609	7280	3.1645	-	-	-
0.7620	7290	2.7030	-	-	-
0.7630	7300	2.9611	-	-	-
0.7641	7310	2.9236	-	-	-
0.7651	7320	3.0733	-	-	-
0.7662	7330	2.6256	-	-	-
0.7672	7340	2.9527	-	-	-
0.7683	7350	2.7406	-	-	-
0.7693	7360	2.9226	-	-	-
0.7704	7370	2.9953	-	-	-
0.7714	7380	2.8099	-	-	-
0.7724	7390	2.8975	-	-	-
0.7735	7400	3.3644	-	-	-
0.7745	7410	3.1927	-	-	-
0.7756	7420	2.7852	-	-	-
0.7766	7430	3.1090	-	-	-
0.7777	7440	2.8339	-	-	-
0.7787	7450	3.2135	-	-	-
0.7798	7460	2.7637	-	-	-
0.7808	7470	2.9763	-	-	-
0.7819	7480	3.1357	-	-	-
0.7829	7490	2.9451	-	-	-
0.7839	7500	2.9795	-	-	-
0.7850	7510	3.1045	-	-	-
0.7860	7520	2.6362	-	-	-
0.7871	7530	3.1910	-	-	-
0.7881	7540	3.0502	-	-	-
0.7892	7550	2.5919	-	-	-
0.7902	7560	2.9157	-	-	-
0.7913	7570	2.8876	-	-	-
0.7923	7580	3.1513	-	-	-
0.7934	7590	3.3226	-	-	-
0.7944	7600	3.0633	-	-	-
0.7954	7610	3.0588	-	-	-
0.7965	7620	2.6580	-	-	-
0.7975	7630	3.0114	-	-	-
0.7986	7640	3.2841	-	-	-
0.7996	7650	2.9898	-	-	-
0.8007	7660	3.1566	-	-	-
0.8011	7664	-	0.9446	0.7826	0.9586
0.8017	7670	3.1916	-	-	-
0.8028	7680	2.8639	-	-	-
0.8038	7690	2.8923	-	-	-
0.8049	7700	2.7390	-	-	-
0.8059	7710	2.8328	-	-	-
0.8069	7720	2.8658	-	-	-
0.8080	7730	3.0992	-	-	-
0.8090	7740	2.4796	-	-	-
0.8101	7750	2.9013	-	-	-
0.8111	7760	3.1138	-	-	-
0.8122	7770	3.1752	-	-	-
0.8132	7780	3.0014	-	-	-
0.8143	7790	2.9388	-	-	-
0.8153	7800	2.5322	-	-	-
0.8163	7810	2.5394	-	-	-
0.8174	7820	3.0805	-	-	-
0.8184	7830	2.7585	-	-	-
0.8195	7840	2.8214	-	-	-
0.8205	7850	2.8650	-	-	-
0.8216	7860	3.0985	-	-	-
0.8226	7870	3.1518	-	-	-
0.8237	7880	2.9308	-	-	-
0.8247	7890	3.3543	-	-	-
0.8258	7900	2.9313	-	-	-
0.8268	7910	2.9895	-	-	-
0.8278	7920	3.1913	-	-	-
0.8289	7930	2.9184	-	-	-
0.8299	7940	2.8370	-	-	-
0.8310	7950	3.1445	-	-	-
0.8320	7960	2.5969	-	-	-
0.8331	7970	3.4299	-	-	-
0.8341	7980	3.4735	-	-	-
0.8352	7990	3.3096	-	-	-
0.8362	8000	2.7927	-	-	-
0.8373	8010	3.0686	-	-	-
0.8383	8020	3.2347	-	-	-
0.8393	8030	3.0730	-	-	-
0.8404	8040	3.1444	-	-	-
0.8414	8050	2.7128	-	-	-
0.8425	8060	2.8853	-	-	-
0.8435	8070	3.0238	-	-	-
0.8446	8080	3.0451	-	-	-
0.8456	8090	2.5744	-	-	-
0.8467	8100	2.8215	-	-	-
0.8477	8110	3.1744	-	-	-
0.8488	8120	2.8678	-	-	-
0.8498	8130	3.0408	-	-	-
0.8508	8140	2.9163	-	-	-
0.8512	8143	-	0.9434	0.7815	0.9572
0.8519	8150	2.7434	-	-	-
0.8529	8160	2.7298	-	-	-
0.8540	8170	2.9721	-	-	-
0.8550	8180	2.6441	-	-	-
0.8561	8190	2.7726	-	-	-
0.8571	8200	2.6044	-	-	-
0.8582	8210	2.6415	-	-	-
0.8592	8220	3.2659	-	-	-
0.8602	8230	2.6741	-	-	-
0.8613	8240	2.5455	-	-	-
0.8623	8250	2.8049	-	-	-
0.8634	8260	3.0141	-	-	-
0.8644	8270	2.9130	-	-	-
0.8655	8280	2.8117	-	-	-
0.8665	8290	3.0745	-	-	-
0.8676	8300	3.2137	-	-	-
0.8686	8310	2.6111	-	-	-
0.8697	8320	2.8413	-	-	-
0.8707	8330	2.7960	-	-	-
0.8717	8340	2.9068	-	-	-
0.8728	8350	2.6328	-	-	-
0.8738	8360	2.8311	-	-	-
0.8749	8370	2.8926	-	-	-
0.8759	8380	2.8994	-	-	-
0.8770	8390	3.1248	-	-	-
0.8780	8400	2.5659	-	-	-
0.8791	8410	2.8494	-	-	-
0.8801	8420	2.8448	-	-	-
0.8812	8430	3.0652	-	-	-
0.8822	8440	2.8263	-	-	-
0.8832	8450	2.7359	-	-	-
0.8843	8460	2.9767	-	-	-
0.8853	8470	2.8697	-	-	-
0.8864	8480	2.9320	-	-	-
0.8874	8490	2.8290	-	-	-
0.8885	8500	2.6975	-	-	-
0.8895	8510	2.7510	-	-	-
0.8906	8520	2.7593	-	-	-
0.8916	8530	2.7789	-	-	-
0.8927	8540	3.2040	-	-	-
0.8937	8550	2.6625	-	-	-
0.8947	8560	2.9745	-	-	-
0.8958	8570	2.9550	-	-	-
0.8968	8580	2.6515	-	-	-
0.8979	8590	2.9206	-	-	-
0.8989	8600	2.7081	-	-	-
0.9000	8610	2.8019	-	-	-
0.9010	8620	2.8349	-	-	-
0.9012	8622	-	0.9423	0.7806	0.9589
0.9021	8630	2.2710	-	-	-
0.9031	8640	3.2877	-	-	-
0.9041	8650	2.9679	-	-	-
0.9052	8660	2.8738	-	-	-
0.9062	8670	3.2957	-	-	-
0.9073	8680	2.7289	-	-	-
0.9083	8690	2.8280	-	-	-
0.9094	8700	3.2182	-	-	-
0.9104	8710	3.0848	-	-	-
0.9115	8720	3.0359	-	-	-
0.9125	8730	2.7479	-	-	-
0.9136	8740	2.7358	-	-	-
0.9146	8750	3.1019	-	-	-
0.9156	8760	3.2598	-	-	-
0.9167	8770	3.2653	-	-	-
0.9177	8780	2.7774	-	-	-
0.9188	8790	3.2295	-	-	-
0.9198	8800	2.6491	-	-	-
0.9209	8810	2.8164	-	-	-
0.9219	8820	2.9490	-	-	-
0.9230	8830	3.1339	-	-	-
0.9240	8840	2.6716	-	-	-
0.9251	8850	2.7648	-	-	-
0.9261	8860	2.7514	-	-	-
0.9271	8870	2.7157	-	-	-
0.9282	8880	2.5352	-	-	-
0.9292	8890	2.8235	-	-	-
0.9303	8900	2.5484	-	-	-
0.9313	8910	3.1607	-	-	-
0.9324	8920	3.1466	-	-	-
0.9334	8930	3.0307	-	-	-
0.9345	8940	2.8943	-	-	-
0.9355	8950	2.9375	-	-	-
0.9366	8960	3.0969	-	-	-
0.9376	8970	2.6834	-	-	-
0.9386	8980	2.5673	-	-	-
0.9397	8990	2.6055	-	-	-
0.9407	9000	2.8498	-	-	-
0.9418	9010	2.6277	-	-	-
0.9428	9020	2.7596	-	-	-
0.9439	9030	3.0466	-	-	-
0.9449	9040	2.5339	-	-	-
0.9460	9050	2.6593	-	-	-
0.9470	9060	2.8744	-	-	-
0.9481	9070	2.9576	-	-	-
0.9491	9080	2.8455	-	-	-
0.9501	9090	2.7865	-	-	-
0.9512	9100	2.9752	-	-	-
0.9513	9101	-	0.9423	0.7807	0.9599
0.9522	9110	2.8567	-	-	-
0.9533	9120	2.9761	-	-	-
0.9543	9130	2.4698	-	-	-
0.9554	9140	2.7843	-	-	-
0.9564	9150	3.0695	-	-	-
0.9575	9160	2.9055	-	-	-
0.9585	9170	2.9593	-	-	-
0.9595	9180	2.9984	-	-	-
0.9606	9190	2.8931	-	-	-
0.9616	9200	2.9067	-	-	-
0.9627	9210	3.1085	-	-	-
0.9637	9220	2.9850	-	-	-
0.9648	9230	3.0427	-	-	-
0.9658	9240	3.1504	-	-	-
0.9669	9250	2.6642	-	-	-
0.9679	9260	2.7319	-	-	-
0.9690	9270	2.6815	-	-	-
0.9700	9280	3.2176	-	-	-
0.9710	9290	3.0496	-	-	-
0.9721	9300	2.6409	-	-	-
0.9731	9310	2.7164	-	-	-
0.9742	9320	3.2761	-	-	-
0.9752	9330	2.8299	-	-	-
0.9763	9340	2.6400	-	-	-
0.9773	9350	2.8062	-	-	-
0.9784	9360	2.7552	-	-	-
0.9794	9370	2.6870	-	-	-
0.9805	9380	2.8233	-	-	-
0.9815	9390	2.9251	-	-	-
0.9825	9400	3.0549	-	-	-
0.9836	9410	2.7645	-	-	-
0.9846	9420	2.5097	-	-	-
0.9857	9430	2.6357	-	-	-
0.9867	9440	2.9024	-	-	-
0.9878	9450	2.9469	-	-	-
0.9888	9460	2.9444	-	-	-
0.9899	9470	2.8488	-	-	-
0.9909	9480	2.8088	-	-	-
0.9920	9490	2.9287	-	-	-
0.9930	9500	3.0114	-	-	-
0.9940	9510	2.9660	-	-	-
0.9951	9520	2.6109	-	-	-
0.9961	9530	2.5789	-	-	-
0.9972	9540	2.8921	-	-	-
0.9982	9550	3.0957	-	-	-
0.9993	9560	3.0763	-	-	-

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

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