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SentenceTransformer based on intfloat/multilingual-e5-base

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-base on the rozetka_positive_pairs dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: intfloat/multilingual-e5-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Dot Product
Training Dataset:
- rozetka_positive_pairs

Model Sources

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

Full Model Architecture

RZTKSentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yklymchuk-rztk/multilingual-e5-base-matryoshka2d-mnr-7")
# Run inference
sentences = [
    'query: bl-5c',
    'passage: Аккумуляторы для мобильных телефонов Nokia Гарантия 12 месяцев Технология Li-ion Страна-производитель товара Китай Совместимость Nokia Класс качества Original (PRC) Емкость, мА*ч 1020 Доставка Доставка в магазины ROZETKA',
    'passage: Нож складной Firebird F7211-BK by Ganzo G7211-BK',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

RZTKInformation Retrieval

Dataset: validation--matryoshka_dim-768--
Evaluated with sentence_transformers_training.evaluation.information_retrieval_evaluator.RZTKInformationRetrievalEvaluator

Metric	Value
dot_accuracy_10	0.5134
dot_precision_10	0.1242
dot_recall_10	0.3025
dot_ndcg_10	0.2415
dot_mrr_10	0.2662
dot_map_60	0.2087

RZTKInformation Retrieval

Datasets: bm-full, core-uk-title, core-ru-title, core-uk-options, core-ru-options, options-uk-title, options-ru-title, options-uk-options, options-ru-options, rusisms-uk-title, rusisms-ru-title, rusisms-uk-options, rusisms-ru-options, rusisms_corrected-uk-title, rusisms_corrected-ru-title, rusisms_corrected-uk-options, rusisms_corrected-ru-options, core_typos-uk-title, core_typos-ru-title, core_typos-uk-options and core_typos-ru-options
Evaluated with sentence_transformers_training.evaluation.information_retrieval_evaluator.RZTKInformationRetrievalEvaluator

Metric	bm-full	core-uk-title	core-ru-title	core-uk-options	core-ru-options	options-uk-title	options-ru-title	options-uk-options	options-ru-options	rusisms-uk-title	rusisms-ru-title	rusisms-uk-options	rusisms-ru-options	rusisms_corrected-uk-title	rusisms_corrected-ru-title	rusisms_corrected-uk-options	rusisms_corrected-ru-options	core_typos-uk-title	core_typos-ru-title	core_typos-uk-options	core_typos-ru-options
dot_accuracy_1	0.4915	0.6324	0.6478	0.5123	0.5041	0.7439	0.7684	0.6236	0.6214	0.6635	0.654	0.527	0.5175	0.7373	0.7373	0.5949	0.5759	0.5462	0.5616	0.4302	0.4158
dot_accuracy_3	0.6663	0.8522	0.8604	0.7454	0.7567	0.9243	0.9154	0.8107	0.8263	0.7937	0.7841	0.6571	0.6444	0.8513	0.8291	0.6962	0.712	0.7772	0.768	0.6509	0.6591
dot_accuracy_5	0.7481	0.9179	0.924	0.8347	0.8409	0.9577	0.9599	0.8797	0.8797	0.8381	0.8381	0.7175	0.7079	0.8924	0.8734	0.7816	0.7848	0.848	0.846	0.73	0.7413
dot_accuracy_10	0.8298	0.9641	0.9713	0.9292	0.9261	0.9911	0.9889	0.9488	0.9443	0.8984	0.8952	0.7746	0.7905	0.9272	0.9177	0.8639	0.8513	0.9066	0.9045	0.848	0.848
dot_precision_1	0.4915	0.6324	0.6478	0.5123	0.5041	0.7439	0.7684	0.6236	0.6214	0.6635	0.654	0.527	0.5175	0.7373	0.7373	0.5949	0.5759	0.5462	0.5616	0.4302	0.4158
dot_precision_3	0.4973	0.6407	0.6492	0.5075	0.5151	0.7275	0.7387	0.6177	0.6147	0.6593	0.6466	0.5228	0.5196	0.7268	0.7173	0.5802	0.577	0.5441	0.5551	0.4203	0.4244
dot_precision_5	0.5042	0.6154	0.6312	0.4975	0.5074	0.6935	0.6962	0.5853	0.584	0.6508	0.6457	0.5117	0.513	0.7127	0.7006	0.5848	0.5816	0.5279	0.5372	0.4088	0.4179
dot_precision_10	0.4996	0.5158	0.5254	0.4538	0.4555	0.5385	0.5388	0.4784	0.4786	0.6206	0.6175	0.5073	0.5013	0.6693	0.6639	0.5728	0.5623	0.4395	0.4428	0.3746	0.3752
dot_recall_1	0.0116	0.0647	0.0662	0.0514	0.0504	0.1134	0.1175	0.0903	0.0906	0.0446	0.0437	0.0341	0.0324	0.0535	0.0542	0.0444	0.0397	0.0559	0.0577	0.0428	0.0412
dot_recall_3	0.0353	0.1958	0.1983	0.1522	0.1547	0.333	0.3376	0.2676	0.2689	0.1193	0.1212	0.0923	0.091	0.1349	0.1335	0.1047	0.1074	0.1667	0.17	0.1266	0.128
dot_recall_5	0.0592	0.3109	0.3192	0.2482	0.2533	0.5235	0.5256	0.4241	0.4223	0.1867	0.1822	0.1371	0.1382	0.2032	0.1999	0.1612	0.1639	0.2679	0.2729	0.2046	0.2094
dot_recall_10	0.1162	0.5097	0.5193	0.4469	0.4481	0.7725	0.7713	0.6623	0.6652	0.3138	0.3082	0.2401	0.2405	0.3452	0.3444	0.2868	0.2824	0.4358	0.44	0.3697	0.3713
dot_ndcg_10	0.4991	0.5795	0.5904	0.4924	0.4948	0.7456	0.7508	0.6337	0.635	0.6664	0.6602	0.5383	0.532	0.7266	0.7207	0.6097	0.5997	0.4957	0.5022	0.4079	0.4084
dot_mrr_10	0.5924	0.7523	0.763	0.6472	0.6476	0.8395	0.8507	0.7297	0.7332	0.7412	0.7342	0.6065	0.598	0.8016	0.7943	0.6689	0.6602	0.6733	0.6795	0.5606	0.556
dot_map_100	0.3582	0.5586	0.5684	0.4979	0.4997	0.7187	0.7273	0.6183	0.618	0.575	0.5785	0.4881	0.4862	0.6393	0.6404	0.5611	0.5584	0.47	0.4773	0.4058	0.4061

RZTKInformation Retrieval

Datasets: bm-full--matryoshka_dim-768--, bm-full--matryoshka_dim-512--, bm-full--matryoshka_dim-256-- and bm-full--matryoshka_dim-128--
Evaluated with sentence_transformers_training.evaluation.information_retrieval_evaluator.RZTKInformationRetrievalEvaluator

Metric	bm-full--matryoshka_dim-768--	bm-full--matryoshka_dim-512--	bm-full--matryoshka_dim-256--	bm-full--matryoshka_dim-128--
dot_accuracy_1	0.4915	0.4894	0.4763	0.4565
dot_precision_1	0.4915	0.4894	0.4763	0.4565
dot_recall_1	0.0116	0.0116	0.0113	0.0107
dot_ndcg_1	0.4915	0.4894	0.4763	0.4565
dot_mrr_1	0.4915	0.4894	0.4763	0.4565
dot_map_100	0.3582	0.3535	0.3406	0.3153

Training Details

Training Dataset

rozetka_positive_pairs

Dataset: rozetka_positive_pairs
Size: 31,662,142 training samples
Columns: query and text
Approximate statistics based on the first 1000 samples:
query text
type string string
details
min: 6 tokens
mean: 11.06 tokens
max: 26 tokens

min: 10 tokens
mean: 59.69 tokens
max: 512 tokens

	query	text
type	string	string
details	min: 6 tokens mean: 11.06 tokens max: 26 tokens	min: 10 tokens mean: 59.69 tokens max: 512 tokens

Samples:

query	text
`query: клей огнеупорн`	`passage: Герметик вогнетривкий Penosil Premium +1500 °C Sealant Black 310 мл (H2130)`
`query: клей огнеупорн`	passage: Герметики Penosil Гарантія 14 днів Габарити С Стандарт (до 300x200x250 мм) Кількість вантажних місць 1 Країна реєстрації бренда Естонія Кількість предметів, шт 1 Країна-виробник товару Естонія Вид_old Герметик Колір Чорний Об'єм 310 мл Тип гарантійного талона Без гарантійного талона Можливість доставки Почтомати Доставка Premium Немає Наявність товара по містах Київ і область Доставка Доставка в магазини ROZETKA
`query: клей огнеупорн`	`passage: Герметик огнеупорный Penosil Premium +1500°C Sealant Black 310 мл (H2130)`

Loss: sentence_transformers_training.model.matryoshka2d_loss.RZTKMatryoshka2dLoss with these parameters:

{
    "loss": "RZTKMultipleNegativesRankingLoss",
    "n_layers_per_step": 1,
    "last_layer_weight": 1.0,
    "prior_layers_weight": 1.0,
    "kl_div_weight": 1.0,
    "kl_temperature": 0.3,
    "matryoshka_dims": [
        768,
        512,
        256,
        128
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": 1
}

Evaluation Dataset

rozetka_positive_pairs

Dataset: rozetka_positive_pairs
Size: 1,651,951 evaluation samples
Columns: query and text
Approximate statistics based on the first 1000 samples:
query text
type string string
details
min: 6 tokens
mean: 10.01 tokens
max: 15 tokens

min: 9 tokens
mean: 65.99 tokens
max: 234 tokens

	query	text
type	string	string
details	min: 6 tokens mean: 10.01 tokens max: 15 tokens	min: 9 tokens mean: 65.99 tokens max: 234 tokens

Samples:

query	text
`query: ніж`	`passage: Мисливський Ніж з Нержавіючої Сталі HK6 SSH BPS Knives - Ніж для риболовлі, мисливства, походів`
`query: ніж`	`passage: Ножі тактичні BPS Knives Гарантія 14 днів Тип Нескладані Кількість вантажних місць 1 Країна реєстрації бренда Україна Країна-виробник товару Україна Вид Туристичні Вид Авторські вироби Вид Сувенірні Вид Мисливські Вид Рибальські Вид Клинки`
`query: ніж`	`passage: Охотничий Нож из Нержавеющей Стали HK6 SSH BPS Knives - Нож для рыбалки, охоты, походов`

Loss: sentence_transformers_training.model.matryoshka2d_loss.RZTKMatryoshka2dLoss with these parameters:

{
    "loss": "RZTKMultipleNegativesRankingLoss",
    "n_layers_per_step": 1,
    "last_layer_weight": 1.0,
    "prior_layers_weight": 1.0,
    "kl_div_weight": 1.0,
    "kl_temperature": 0.3,
    "matryoshka_dims": [
        768,
        512,
        256,
        128
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": 1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 88
per_device_eval_batch_size: 88
learning_rate: 2e-05
num_train_epochs: 1.0
warmup_ratio: 0.1
bf16: True
bf16_full_eval: True
tf32: True
dataloader_num_workers: 4
load_best_model_at_end: True
optim: adafactor
push_to_hub: True
hub_model_id: yklymchuk-rztk/multilingual-e5-base-matryoshka2d-mnr-7
hub_private_repo: True
prompts: {'query': 'query: ', 'text': 'passage: '}
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: 88
per_device_eval_batch_size: 88
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: 1.0
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: True
fp16_full_eval: False
tf32: True
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
dataloader_num_workers: 4
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
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: adafactor
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: True
resume_from_checkpoint: None
hub_model_id: yklymchuk-rztk/multilingual-e5-base-matryoshka2d-mnr-7
hub_strategy: every_save
hub_private_repo: True
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: {'query': 'query: ', 'text': 'passage: '}
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
ddp_static_graph: False
ddp_comm_hook: bf16
gradient_as_bucket_view: False
num_proc: 30

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	validation--matryoshka_dim-768--_dot_ndcg_10	bm-full_dot_ndcg_10	core-uk-title_dot_ndcg_10	core-ru-title_dot_ndcg_10	core-uk-options_dot_ndcg_10	core-ru-options_dot_ndcg_10	options-uk-title_dot_ndcg_10	options-ru-title_dot_ndcg_10	options-uk-options_dot_ndcg_10	options-ru-options_dot_ndcg_10	rusisms-uk-title_dot_ndcg_10	rusisms-ru-title_dot_ndcg_10	rusisms-uk-options_dot_ndcg_10	rusisms-ru-options_dot_ndcg_10	rusisms_corrected-uk-title_dot_ndcg_10	rusisms_corrected-ru-title_dot_ndcg_10	rusisms_corrected-uk-options_dot_ndcg_10	rusisms_corrected-ru-options_dot_ndcg_10	core_typos-uk-title_dot_ndcg_10	core_typos-ru-title_dot_ndcg_10	core_typos-uk-options_dot_ndcg_10	core_typos-ru-options_dot_ndcg_10	bm-full--matryoshka_dim-768--_dot_ndcg_1	bm-full--matryoshka_dim-512--_dot_ndcg_1	bm-full--matryoshka_dim-256--_dot_ndcg_1	bm-full--matryoshka_dim-128--_dot_ndcg_1
0.0050	450	4.8006	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0100	900	4.6705	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0150	1350	4.3682	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0200	1800	3.8002	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0250	2250	3.058	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0300	2700	2.6656	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0350	3150	2.3286	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0400	3600	2.196	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0450	4050	2.0868	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0500	4500	1.9213	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0550	4950	1.9351	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0600	5400	1.821	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0650	5850	1.7502	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0700	6300	1.8085	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0750	6750	1.6075	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0800	7200	1.608	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0850	7650	1.6332	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0901	8100	1.5358	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0951	8550	1.4157	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1000	8995	-	1.0920	0.2172	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1001	9000	1.4988	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1051	9450	1.4312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1101	9900	1.3679	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1151	10350	1.3273	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1201	10800	1.2433	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1251	11250	1.2409	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1301	11700	1.2429	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1351	12150	1.1993	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1401	12600	1.2249	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1451	13050	1.2095	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1501	13500	1.1526	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1551	13950	1.2118	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1601	14400	1.1092	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1651	14850	1.1545	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1701	15300	1.1194	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1751	15750	1.15	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1801	16200	1.1483	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1851	16650	1.1453	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1901	17100	1.1538	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1951	17550	1.0421	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2000	17990	-	0.7770	0.2284	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2001	18000	1.0905	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2051	18450	1.0441	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2101	18900	1.0386	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2151	19350	1.0384	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2201	19800	1.02	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2251	20250	1.0408	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2301	20700	1.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2351	21150	1.0453	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2401	21600	1.0161	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2451	22050	1.0078	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2501	22500	1.0208	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2551	22950	0.9729	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2601	23400	0.9849	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2652	23850	1.0323	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2702	24300	1.012	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2752	24750	0.9303	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2802	25200	0.9616	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2852	25650	1.0045	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2902	26100	0.9498	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2952	26550	0.8919	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3000	26985	-	0.6559	0.2360	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3002	27000	0.9267	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3052	27450	0.9188	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3102	27900	0.9495	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3152	28350	0.9408	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3202	28800	0.9272	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3252	29250	0.9323	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3302	29700	0.9404	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3352	30150	0.9452	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3402	30600	0.9793	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3452	31050	0.9342	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3502	31500	0.9014	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3552	31950	0.9229	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3602	32400	0.8533	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3652	32850	0.9242	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3702	33300	0.9093	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3752	33750	0.8891	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3802	34200	0.9323	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3852	34650	0.8977	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3902	35100	0.8863	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3952	35550	0.9077	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4000	35980	-	0.6095	0.2375	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4002	36000	0.8989	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4052	36450	0.8946	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4102	36900	0.8759	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4152	37350	0.8605	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4202	37800	0.8901	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4252	38250	0.9056	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4302	38700	0.8982	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4352	39150	0.9252	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4402	39600	0.9088	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4453	40050	0.9082	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4503	40500	0.8494	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4553	40950	0.8819	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4603	41400	0.9072	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4653	41850	0.8827	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4703	42300	0.8342	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4753	42750	0.8326	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4803	43200	0.8525	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4853	43650	0.8744	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4903	44100	0.8883	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4953	44550	0.8841	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5000	44975	-	0.5651	0.2390	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5003	45000	0.8662	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5053	45450	0.8572	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5103	45900	0.9092	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5153	46350	0.8311	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5203	46800	0.8333	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5253	47250	0.9085	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5303	47700	0.8324	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5353	48150	0.8198	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5403	48600	0.8813	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5453	49050	0.863	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5503	49500	0.8788	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5553	49950	0.8545	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5603	50400	0.9037	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5653	50850	0.8463	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5703	51300	0.8097	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5753	51750	0.8235	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5803	52200	0.8149	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5853	52650	0.7932	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5903	53100	0.8743	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5953	53550	0.8574	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6000	53970	-	0.5688	0.2402	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6003	54000	0.88	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6053	54450	0.8542	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6103	54900	0.8382	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6153	55350	0.7793	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6204	55800	0.8274	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6254	56250	0.8732	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6304	56700	0.8207	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6354	57150	0.8281	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6404	57600	0.8196	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6454	58050	0.8554	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6504	58500	0.8358	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6554	58950	0.8567	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6604	59400	0.8253	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6654	59850	0.8183	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6704	60300	0.8667	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6754	60750	0.8863	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6804	61200	0.8161	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6854	61650	0.818	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6904	62100	0.8116	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6954	62550	0.7865	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7	62965	-	0.5341	0.242	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7004	63000	0.7946	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7054	63450	0.8029	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7104	63900	0.8011	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7154	64350	0.8202	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7204	64800	0.8239	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7254	65250	0.834	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7304	65700	0.8555	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7354	66150	0.8649	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7404	66600	0.8148	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7454	67050	0.8438	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7504	67500	0.8645	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7554	67950	0.8182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7604	68400	0.8419	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7654	68850	0.787	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7704	69300	0.8074	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7754	69750	0.8268	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7804	70200	0.8349	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7854	70650	0.8092	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7904	71100	0.8169	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7955	71550	0.818	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8000	71960	-	0.5391	0.2414	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8005	72000	0.8132	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8055	72450	0.8772	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8105	72900	0.8364	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8155	73350	0.7612	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8205	73800	0.829	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8255	74250	0.815	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8305	74700	0.8183	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8355	75150	0.7887	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8405	75600	0.8352	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8455	76050	0.8083	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8505	76500	0.8182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8555	76950	0.8444	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8605	77400	0.8747	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8655	77850	0.8485	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8705	78300	0.8371	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8755	78750	0.8185	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8805	79200	0.82	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8855	79650	0.8591	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8905	80100	0.8225	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8955	80550	0.8014	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9000	80955	-	0.5387	0.2415	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9005	81000	0.8326	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9055	81450	0.833	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9105	81900	0.8127	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9155	82350	0.8372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9205	82800	0.8627	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9255	83250	0.8111	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9305	83700	0.7934	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9355	84150	0.8642	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9405	84600	0.8164	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9455	85050	0.8703	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9505	85500	0.8213	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9555	85950	0.8414	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9605	86400	0.8257	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9655	86850	0.8623	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9705	87300	0.8221	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9756	87750	0.7964	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9806	88200	0.8227	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9856	88650	0.8306	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9906	89100	0.8565	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9956	89550	0.8286	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0000	89948	-	-	-	0.4991	0.5795	0.5904	0.4924	0.4948	0.7456	0.7508	0.6337	0.6350	0.6664	0.6602	0.5383	0.5320	0.7266	0.7207	0.6097	0.5997	0.4957	0.5022	0.4079	0.4084	0.4915	0.4894	0.4763	0.4565

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.10
Sentence Transformers: 3.3.0
Transformers: 4.46.3
PyTorch: 2.5.1+cu124
Accelerate: 1.1.1
Datasets: 3.1.0
Tokenizers: 0.20.3

Citation

BibTeX

Sentence Transformers

@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",
}

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Safetensors

Model size

278M params

Tensor type

BF16

Inference Examples

Sentence Similarity

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Model tree for yklymchuk-rztk/multilingual-e5-base-matryoshka2d-mnr-7

Base model

intfloat/multilingual-e5-base

Quantized

(13)

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Evaluation results

Dot Accuracy 10 on validation matryoshka dim 768
self-reported

0.513
Dot Precision 10 on validation matryoshka dim 768
self-reported

0.124
Dot Recall 10 on validation matryoshka dim 768
self-reported

0.302
Dot Ndcg 10 on validation matryoshka dim 768
self-reported

0.241
Dot Mrr 10 on validation matryoshka dim 768
self-reported

0.266
Dot Map 60 on validation matryoshka dim 768
self-reported

0.209
Dot Accuracy 1 on bm full
self-reported

0.492
Dot Accuracy 3 on bm full
self-reported

0.666
Dot Accuracy 5 on bm full
self-reported

0.748
Dot Accuracy 10 on bm full
self-reported

0.830

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