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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-9")
# Run inference
sentences = [
    'query: ручки для мебели',
    'passage: Мебельные ручки ДС',
    'passage: Ручка для мебели фигурная стекло 26х35 мм Прозрачный (S-009)',
]
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	0.5139
dot_precision_10	0.1059
dot_recall_10	0.3502
dot_ndcg_10	0.2648
dot_mrr_10	0.2743
dot_map_60	0.2281

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	0.6593	0.8018	0.8005	0.7152	0.7231	0.8301	0.8398	0.6917	0.699	0.9176	0.9118	0.8	0.8	0.888	0.896	0.792	0.788	0.7336	0.7283	0.6207	0.6234
dot_accuracy_3	0.7828	0.937	0.9318	0.8753	0.878	0.9515	0.949	0.8568	0.8447	0.9529	0.9412	0.8882	0.8824	0.956	0.944	0.872	0.868	0.8727	0.8609	0.7874	0.794
dot_accuracy_5	0.8321	0.9738	0.9711	0.9278	0.9304	0.9709	0.9757	0.9199	0.9029	0.9706	0.9647	0.9235	0.9	0.972	0.968	0.9	0.892	0.9239	0.9173	0.8412	0.8583
dot_accuracy_10	0.8924	0.9921	0.9921	0.9738	0.9764	0.9951	0.9976	0.9636	0.9587	0.9882	0.9824	0.9471	0.9353	0.984	0.988	0.952	0.948	0.9685	0.9659	0.9134	0.916
dot_precision_1	0.6593	0.8018	0.8005	0.7152	0.7231	0.8301	0.8398	0.6917	0.699	0.9176	0.9118	0.8	0.8	0.888	0.896	0.792	0.788	0.7336	0.7283	0.6207	0.6234
dot_precision_3	0.6607	0.7406	0.7458	0.6636	0.6597	0.7451	0.7532	0.6367	0.6375	0.8039	0.8	0.7275	0.7235	0.8173	0.8133	0.7187	0.728	0.6719	0.6728	0.5722	0.5682
dot_precision_5	0.6427	0.6478	0.6507	0.5887	0.5877	0.5893	0.5874	0.5063	0.5049	0.7247	0.7235	0.6612	0.6565	0.7672	0.7648	0.6912	0.6912	0.5916	0.5898	0.5097	0.5113
dot_precision_10	0.6031	0.3948	0.3951	0.3798	0.378	0.3352	0.3369	0.3102	0.308	0.5988	0.6024	0.5535	0.5541	0.6712	0.6712	0.628	0.6252	0.3693	0.3699	0.3407	0.3392
dot_recall_1	0.0442	0.2539	0.2531	0.2153	0.2189	0.2594	0.2619	0.2104	0.2153	0.2578	0.2586	0.198	0.2009	0.186	0.1873	0.1541	0.1563	0.2205	0.2154	0.1813	0.1841
dot_recall_3	0.1304	0.592	0.5948	0.5178	0.5146	0.666	0.6738	0.5627	0.5664	0.4822	0.4849	0.4289	0.4345	0.3899	0.3879	0.3291	0.3377	0.5296	0.5316	0.4397	0.4413
dot_recall_5	0.2046	0.8034	0.8081	0.72	0.7222	0.8504	0.8477	0.7282	0.7259	0.6112	0.6106	0.5554	0.5504	0.5203	0.5223	0.4531	0.4571	0.7311	0.7273	0.6172	0.6244
dot_recall_10	0.3602	0.9439	0.9444	0.9022	0.8975	0.96	0.9646	0.8819	0.8766	0.8003	0.8029	0.7397	0.7429	0.7523	0.7587	0.6954	0.6922	0.8793	0.8823	0.8044	0.8012
dot_ndcg_10	0.6414	0.8761	0.8772	0.8078	0.8074	0.8822	0.8884	0.7797	0.7788	0.926	0.9287	0.8329	0.8343	0.9004	0.9016	0.8166	0.819	0.8028	0.8027	0.7084	0.7091
dot_mrr_10	0.729	0.8723	0.8719	0.8035	0.8107	0.8903	0.8978	0.7853	0.7862	0.9372	0.9313	0.8497	0.8447	0.9241	0.925	0.8369	0.8381	0.8127	0.8083	0.7175	0.7216
dot_map_100	0.6102	0.8376	0.8393	0.7575	0.7574	0.829	0.8347	0.7186	0.7205	0.9226	0.9258	0.8207	0.8231	0.8903	0.8898	0.8061	0.8119	0.7588	0.7586	0.6585	0.6599

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	0.6593	0.6627	0.6551	0.6426
dot_precision_1	0.6593	0.6627	0.6551	0.6426
dot_recall_1	0.0442	0.044	0.0433	0.042
dot_ndcg_1	0.6593	0.6627	0.6551	0.6426
dot_mrr_1	0.6593	0.6627	0.6551	0.6426
dot_map_100	0.6102	0.6056	0.5964	0.5723

Training Details

Training Dataset

rozetka_positive_pairs

Dataset: rozetka_positive_pairs
Size: 85,255,772 training samples
Columns: query and text
Approximate statistics based on the first 1000 samples:
query text
type string string
details
min: 7 tokens
mean: 12.49 tokens
max: 40 tokens

min: 7 tokens
mean: 54.6 tokens
max: 512 tokens

	query	text
type	string	string
details	min: 7 tokens mean: 12.49 tokens max: 40 tokens	min: 7 tokens mean: 54.6 tokens max: 512 tokens

Samples:

query	text
`query: campingaz fold n cool classic 10l dark blue`	`passage: Термосумка Campingaz Fold'n Cool Classic 10L Dark Blue (4823082704729)`
`query: campingaz fold n cool classic 10l dark blue`	`passage: Термопродукція Campingaz Гарантія 14 днів Вид Термосумки Колір Синій з білим Режим роботи Охолодження Країна реєстрації бренда Франція Країна-виробник товару Китай Тип гарантійного талона Гарантія по чеку Можливість доставки Почтомати Доставка Premium Немає`
`query: campingaz fold n cool classic 10l dark blue`	`passage: Термосумка Campingaz Fold'n Cool Classic 10L Dark Blue (4823082704729)`

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: 1,132,902 evaluation samples
Columns: query and text
Approximate statistics based on the first 1000 samples:
query text
type string string
details
min: 7 tokens
mean: 9.93 tokens
max: 14 tokens

min: 11 tokens
mean: 43.34 tokens
max: 258 tokens

	query	text
type	string	string
details	min: 7 tokens mean: 9.93 tokens max: 14 tokens	min: 11 tokens mean: 43.34 tokens max: 258 tokens

Samples:

query	text
`query: батончики без сахара`	`passage: Батончик журавлиновий кокос Gavra натуральний без цукру 40 г`
`query: батончики без сахара`	`passage: Батончики ГАВРА Країна-виробник товару Україна Тип Фруктові Вага 40 г Домішки Горіхи Домішки Мед`
`query: батончики без сахара`	`passage: Батончик клюквенный кокос Gavra натуральный без сахара 40 г`

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-9
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-9
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	1212	4.5978	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0100	2424	4.3615	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0150	3636	3.8175	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0200	4848	2.936	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0250	6060	2.2596	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0300	7272	1.9819	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0350	8484	1.8095	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0400	9696	1.6372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0450	10908	1.525	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0500	12120	1.4855	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0550	13332	1.3615	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0600	14544	1.3257	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0651	15756	1.199	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0701	16968	1.1829	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0751	18180	1.1388	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0801	19392	1.0946	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0851	20604	1.0621	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0901	21816	1.0827	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0951	23028	0.971	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1000	24221	-	0.7535	0.2438	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1001	24240	0.9553	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1051	25452	0.9061	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1101	26664	0.8798	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1151	27876	0.8616	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1201	29088	0.8223	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1251	30300	0.8249	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1301	31512	0.7795	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.2252	54540	0.6189	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.2352	56964	0.6146	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2402	58176	0.5869	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2452	59388	0.585	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2502	60600	0.5868	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2552	61812	0.5698	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2602	63024	0.5904	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2652	64236	0.5835	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2702	65448	0.5723	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2752	66660	0.5832	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2802	67872	0.5802	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2852	69084	0.5475	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2902	70296	0.5649	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2952	71508	0.593	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.3052	73932	0.5364	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.3353	81204	0.5187	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3403	82416	0.518	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3453	83628	0.5396	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3503	84840	0.5237	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3553	86052	0.5209	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3603	87264	0.5247	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3653	88476	0.5348	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.3753	90900	0.5307	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3803	92112	0.5196	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3853	93324	0.5193	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3903	94536	0.5137	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3953	95748	0.5194	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4000	96884	-	0.3739	0.2626	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.4954	119988	0.5229	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5000	121105	-	0.3700	0.2651	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5004	121200	0.5001	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.6355	153924	0.492	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.6856	166044	0.4858	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6906	167256	0.4938	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6956	168468	0.5003	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7000	169547	-	0.3588	0.2653	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7006	169680	0.4865	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7056	170892	0.4756	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7106	172104	0.5094	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7156	173316	0.5073	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7206	174528	0.4851	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7256	175740	0.4968	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7306	176952	0.4894	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7356	178164	0.493	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7406	179376	0.4897	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7456	180588	0.4836	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7506	181800	0.4889	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7556	183012	0.5145	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7606	184224	0.5032	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7656	185436	0.5048	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7706	186648	0.5168	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7756	187860	0.5001	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7806	189072	0.4904	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7856	190284	0.5048	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7906	191496	0.4982	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7956	192708	0.4987	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8000	193768	-	0.3554	0.2649	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8007	193920	0.5009	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8057	195132	0.4931	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8107	196344	0.4752	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8157	197556	0.4841	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8207	198768	0.4929	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8257	199980	0.486	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8307	201192	0.5016	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8357	202404	0.5026	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8407	203616	0.4957	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8457	204828	0.5047	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8507	206040	0.4822	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8557	207252	0.49	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8607	208464	0.4729	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8657	209676	0.4879	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8707	210888	0.4935	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8757	212100	0.4998	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8807	213312	0.4992	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8857	214524	0.4952	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8907	215736	0.516	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8957	216948	0.4965	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9	217989	-	0.355	0.2648	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9007	218160	0.517	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9057	219372	0.492	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9107	220584	0.4924	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9157	221796	0.4826	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9207	223008	0.4943	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9258	224220	0.502	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9308	225432	0.5054	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9358	226644	0.5064	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9408	227856	0.5062	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9458	229068	0.4315	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9508	230280	0.3721	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9558	231492	0.4901	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9608	232704	0.491	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9658	233916	0.5232	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9708	235128	0.4325	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9758	236340	0.3745	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9808	237552	0.3681	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9858	238764	0.3627	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9908	239976	0.4791	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9958	241188	0.5111	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0	242203	-	-	-	0.6414	0.8761	0.8772	0.8078	0.8074	0.8822	0.8884	0.7797	0.7788	0.9260	0.9287	0.8329	0.8343	0.9004	0.9016	0.8166	0.8190	0.8028	0.8027	0.7084	0.7091	0.6593	0.6627	0.6551	0.6426

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

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Sentence Similarity

This model is not currently available via any of the supported third-party Inference Providers, and the model is not deployed on the HF Inference API.

Model tree for yklymchuk-rztk/multilingual-e5-base-matryoshka2d-mnr-9

Base model

intfloat/multilingual-e5-base

Quantized

(19)

this model

Evaluation results

Dot Accuracy 10 on validation matryoshka dim 768
self-reported

0.514
Dot Precision 10 on validation matryoshka dim 768
self-reported

0.106
Dot Recall 10 on validation matryoshka dim 768
self-reported

0.350
Dot Ndcg 10 on validation matryoshka dim 768
self-reported

0.265
Dot Mrr 10 on validation matryoshka dim 768
self-reported

0.274
Dot Map 60 on validation matryoshka dim 768
self-reported

0.228
Dot Accuracy 1 on bm full
self-reported

0.659
Dot Accuracy 3 on bm full
self-reported

0.783
Dot Accuracy 5 on bm full
self-reported

0.832
Dot Accuracy 10 on bm full
self-reported

0.892

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