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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-8")
# Run inference
sentences = [
    'query: ніж',
    'passage: Кухонный нож Tramontina Profissional Master для овощей 102 мм Белый (24625/184)',
    'passage: Ножи тактические BPS Knives Гарантия 14 дней Тип Нескладные Ножны/чехол Ножны Количество грузовых мест 1 Страна регистрации бренда Украина Страна-производитель товара Украина Доставка Готов к отправке Доставка Доставка в магазины ROZETKA Вид Охотничьи Вид Рыбацкие Вид Туристические Вид Карманные',
]
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	0.5072
dot_precision_10	0.1443
dot_recall_10	0.2278
dot_ndcg_10	0.2176
dot_mrr_10	0.2648
dot_map_60	0.185

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	0.4989	0.653	0.6458	0.5082	0.5339	0.7706	0.7773	0.6347	0.6303	0.6349	0.6508	0.5143	0.5079	0.7468	0.7278	0.5886	0.5791	0.5359	0.5585	0.4281	0.4199
dot_accuracy_3	0.667	0.8501	0.8634	0.7628	0.7844	0.9243	0.9265	0.8107	0.8419	0.8063	0.7873	0.6349	0.6508	0.8513	0.8386	0.7278	0.7278	0.7526	0.7659	0.6612	0.6591
dot_accuracy_5	0.747	0.923	0.924	0.8573	0.8624	0.9621	0.9666	0.8864	0.882	0.8476	0.8349	0.7111	0.7016	0.8987	0.8766	0.7816	0.7911	0.8378	0.8368	0.7495	0.7485
dot_accuracy_10	0.8432	0.9754	0.9774	0.9435	0.9292	0.9955	0.9978	0.9421	0.9465	0.8952	0.8889	0.7873	0.8	0.9304	0.9241	0.8544	0.8639	0.9076	0.8994	0.847	0.8552
dot_precision_1	0.4989	0.653	0.6458	0.5082	0.5339	0.7706	0.7773	0.6347	0.6303	0.6349	0.6508	0.5143	0.5079	0.7468	0.7278	0.5886	0.5791	0.5359	0.5585	0.4281	0.4199
dot_precision_3	0.5032	0.6304	0.6499	0.5209	0.5335	0.7335	0.7431	0.614	0.6206	0.6582	0.6476	0.5164	0.5206	0.7268	0.7215	0.5844	0.5802	0.5322	0.5469	0.4278	0.4319
dot_precision_5	0.5069	0.6224	0.6244	0.515	0.5236	0.6984	0.7002	0.5898	0.5893	0.6495	0.6419	0.5187	0.5137	0.712	0.7038	0.5867	0.5854	0.5271	0.5339	0.4261	0.4273
dot_precision_10	0.5027	0.5197	0.5285	0.4658	0.4664	0.5443	0.541	0.4873	0.4886	0.6219	0.6175	0.5092	0.5124	0.6718	0.6715	0.5728	0.5709	0.4425	0.4446	0.3835	0.3831
dot_recall_1	0.0121	0.0668	0.0658	0.051	0.0534	0.1172	0.1175	0.0929	0.0927	0.0461	0.0439	0.0409	0.0376	0.0541	0.0546	0.0422	0.0448	0.0549	0.0572	0.0428	0.0417
dot_recall_3	0.0359	0.1932	0.1988	0.1569	0.16	0.3355	0.3407	0.2672	0.2694	0.1203	0.1214	0.0913	0.0928	0.1355	0.1397	0.1121	0.1103	0.1639	0.1688	0.1286	0.1301
dot_recall_5	0.0601	0.3151	0.3169	0.2567	0.2612	0.5278	0.5303	0.4269	0.4224	0.1848	0.1811	0.1392	0.1389	0.2064	0.2057	0.1688	0.1648	0.2674	0.2716	0.2122	0.214
dot_recall_10	0.1176	0.5133	0.5225	0.4583	0.4586	0.7799	0.7774	0.6723	0.6773	0.3162	0.3097	0.2549	0.2484	0.3505	0.3494	0.2981	0.289	0.4386	0.441	0.3781	0.3783
dot_ndcg_10	0.5029	0.5841	0.5921	0.5041	0.5098	0.7549	0.755	0.6425	0.6444	0.6657	0.6596	0.5432	0.5416	0.7302	0.7271	0.6143	0.609	0.4958	0.5017	0.4156	0.4161
dot_mrr_10	0.5984	0.7655	0.7636	0.654	0.6712	0.8518	0.857	0.738	0.7419	0.7263	0.7312	0.5967	0.5957	0.8088	0.7934	0.6718	0.6658	0.6624	0.6764	0.561	0.5601
dot_map_100	0.3686	0.5692	0.5766	0.509	0.5139	0.7295	0.732	0.628	0.6256	0.5822	0.5843	0.5009	0.5023	0.6422	0.6462	0.5714	0.5707	0.4754	0.4823	0.415	0.4159

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	0.4989	0.5	0.4965	0.4873
dot_precision_1	0.4989	0.5	0.4965	0.4873
dot_recall_1	0.0121	0.012	0.0119	0.0115
dot_ndcg_1	0.4989	0.5	0.4965	0.4873
dot_mrr_1	0.4989	0.5	0.4965	0.4873
dot_map_100	0.3686	0.3655	0.3531	0.3308

Training Details

Training Dataset

rozetka_positive_pairs

Dataset: rozetka_positive_pairs
Size: 48,198,697 training samples
Columns: query and text
Approximate statistics based on the first 1000 samples:
query text
type string string
details
min: 6 tokens
mean: 10.75 tokens
max: 26 tokens

min: 8 tokens
mean: 61.91 tokens
max: 512 tokens

	query	text
type	string	string
details	min: 6 tokens mean: 10.75 tokens max: 26 tokens	min: 8 tokens mean: 61.91 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: 3,309,787 evaluation samples
Columns: query and text
Approximate statistics based on the first 1000 samples:
query text
type string string
details
min: 6 tokens
mean: 7.58 tokens
max: 12 tokens

min: 11 tokens
mean: 72.78 tokens
max: 264 tokens

	query	text
type	string	string
details	min: 6 tokens mean: 7.58 tokens max: 12 tokens	min: 11 tokens mean: 72.78 tokens max: 264 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-8
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-8
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	685	4.8405	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0100	1370	4.7324	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0150	2055	4.2883	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0200	2740	3.7416	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0250	3425	2.9713	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0300	4110	2.6176	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0350	4795	2.3556	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0400	5480	2.3165	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0450	6165	2.1682	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0500	6850	2.0736	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0550	7535	2.0138	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0600	8220	1.9518	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0650	8905	1.8403	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0700	9590	1.7593	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0750	10275	1.6818	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0800	10960	1.6754	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0850	11645	1.6262	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0900	12330	1.6023	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0950	13015	1.5401	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1000	13693	-	1.0477	0.1937	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1001	13700	1.4887	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1051	14385	1.4678	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.6653	91105	0.9078	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6704	91790	0.8888	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6754	92475	0.8544	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6804	93160	0.9402	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6854	93845	0.915	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6904	94530	0.8781	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6954	95215	0.8752	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7000	95851	-	0.5202	0.2182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7004	95900	0.8934	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7054	96585	0.8828	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7104	97270	0.895	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7154	97955	0.9171	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7204	98640	0.8893	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7254	99325	0.8539	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7304	100010	0.8699	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7354	100695	0.901	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7404	101380	0.8686	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7454	102065	0.9085	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7504	102750	0.8985	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7554	103435	0.8823	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7604	104120	0.9014	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7654	104805	0.8996	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7704	105490	0.9107	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7754	106175	0.875	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7804	106860	0.8662	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7854	107545	0.8906	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7904	108230	0.8993	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7954	108915	0.8606	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8	109544	-	0.5142	0.2174	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8004	109600	0.8822	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8054	110285	0.8696	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8104	110970	0.8954	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8154	111655	0.8714	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8204	112340	0.9049	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8254	113025	0.8926	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8304	113710	0.8731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8354	114395	0.8805	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8404	115080	0.8649	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8454	115765	0.8999	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8504	116450	0.8942	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8554	117135	0.8887	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8605	117820	0.8693	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8655	118505	0.8588	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8705	119190	0.8685	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8755	119875	0.9273	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8805	120560	0.9164	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8855	121245	0.9086	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8905	121930	0.8691	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8955	122615	0.8755	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9000	123237	-	0.5203	0.2176	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9005	123300	0.9047	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9055	123985	0.9107	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9105	124670	0.9226	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9155	125355	0.8988	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9205	126040	0.8795	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9255	126725	0.8973	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9305	127410	0.8622	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9355	128095	0.9181	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9405	128780	0.8847	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9455	129465	0.911	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9505	130150	0.8987	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9555	130835	0.8915	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9605	131520	0.9008	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9655	132205	0.8999	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9705	132890	0.8777	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9755	133575	0.8833	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9805	134260	0.8979	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9855	134945	0.8824	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9905	135630	0.8758	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9955	136315	0.8929	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0000	136927	-	-	-	0.5029	0.5841	0.5921	0.5041	0.5098	0.7549	0.7550	0.6425	0.6444	0.6657	0.6596	0.5432	0.5416	0.7302	0.7271	0.6143	0.6090	0.4958	0.5017	0.4156	0.4161	0.4989	0.5	0.4965	0.4873

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-8

Base model

intfloat/multilingual-e5-base

Quantized

(13)

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Evaluation results

Dot Accuracy 10 on validation matryoshka dim 768
self-reported

0.507
Dot Precision 10 on validation matryoshka dim 768
self-reported

0.144
Dot Recall 10 on validation matryoshka dim 768
self-reported

0.228
Dot Ndcg 10 on validation matryoshka dim 768
self-reported

0.218
Dot Mrr 10 on validation matryoshka dim 768
self-reported

0.265
Dot Map 60 on validation matryoshka dim 768
self-reported

0.185
Dot Accuracy 1 on bm full
self-reported

0.499
Dot Accuracy 3 on bm full
self-reported

0.667
Dot Accuracy 5 on bm full
self-reported

0.747
Dot Accuracy 10 on bm full
self-reported

0.843

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