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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-10")
# Run inference
sentences = [
    'query: koton женская одежда',
    'passage: Жіночі штани Koton Габарити С Стандарт (до 300x200x250 мм) Кількість вантажних місць 1 Країна реєстрації бренда Туреччина Країна-виробник товару Туреччина Розмір M Стиль Повсякденний (casual) Колір Зелений Моделі Кюлоти Доставка Доставка в магазини ROZETKA',
    'passage: Женские блузы Koton Габариты_old C Стандарт (до 300x200x250 мм) Количество грузовых мест 1 Страна регистрации бренда Турция Страна-производитель товара Турция Размер M Стиль Повседневный (casual) Цвет Бежевый Материал Полиэстер Материал Эластан Доставка Доставка в магазины ROZETKA',
]
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	0.4715
dot_precision_10	0.0816
dot_recall_10	0.3422
dot_ndcg_10	0.2382
dot_mrr_10	0.2343
dot_map_60	0.2037

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	0.6862	0.7887	0.8005	0.6667	0.6759	0.8228	0.8204	0.6893	0.6917	0.8308	0.8538	0.6846	0.7308	0.9231	0.9154	0.8	0.8154	0.7021	0.7178	0.563	0.5656
dot_accuracy_3	0.7965	0.9265	0.9226	0.8451	0.8504	0.9442	0.9369	0.8641	0.8714	0.9077	0.8923	0.7846	0.8231	0.9769	0.9615	0.8846	0.9385	0.853	0.8543	0.7507	0.7507
dot_accuracy_5	0.8526	0.9672	0.9646	0.9029	0.9094	0.9733	0.9757	0.915	0.9175	0.9231	0.9154	0.8308	0.8846	0.9846	0.9615	0.9231	0.9692	0.9094	0.9094	0.811	0.8136
dot_accuracy_10	0.9063	0.9908	0.9908	0.9606	0.9593	0.9927	0.9927	0.9587	0.9587	0.9385	0.9385	0.9077	0.9	0.9923	0.9923	0.9923	0.9923	0.9567	0.9593	0.8832	0.8911
dot_precision_1	0.6862	0.7887	0.8005	0.6667	0.6759	0.8228	0.8204	0.6893	0.6917	0.8308	0.8538	0.6846	0.7308	0.9231	0.9154	0.8	0.8154	0.7021	0.7178	0.563	0.5656
dot_precision_3	0.6771	0.7196	0.724	0.6251	0.6251	0.7484	0.75	0.6319	0.6343	0.7872	0.7872	0.6744	0.6769	0.8359	0.8282	0.7462	0.7769	0.6374	0.643	0.5324	0.5284
dot_precision_5	0.6606	0.6291	0.6339	0.5585	0.5593	0.5893	0.5898	0.5131	0.5121	0.7354	0.7415	0.6415	0.6631	0.7969	0.7815	0.7108	0.7431	0.5667	0.5685	0.4745	0.4732
dot_precision_10	0.6134	0.3899	0.3904	0.3681	0.3656	0.3379	0.3367	0.3097	0.309	0.64	0.6362	0.5746	0.5754	0.6754	0.6738	0.6469	0.6469	0.3597	0.3604	0.3202	0.3209
dot_recall_1	0.0467	0.2405	0.2447	0.1954	0.1986	0.2577	0.2554	0.2108	0.2121	0.1537	0.1673	0.1288	0.1384	0.1929	0.1927	0.1614	0.1617	0.2042	0.2075	0.1624	0.1628
dot_recall_3	0.1351	0.5724	0.5729	0.4826	0.4859	0.6689	0.6682	0.5591	0.5635	0.3589	0.3508	0.3125	0.318	0.3944	0.3826	0.3388	0.3648	0.5013	0.5054	0.4062	0.404
dot_recall_5	0.2131	0.7821	0.7877	0.6773	0.6818	0.8499	0.8496	0.7383	0.7374	0.4859	0.487	0.4256	0.4479	0.5414	0.5193	0.4734	0.5108	0.6996	0.7007	0.5715	0.5723
dot_recall_10	0.366	0.934	0.9335	0.8668	0.8626	0.9645	0.9613	0.879	0.8778	0.7002	0.6997	0.6454	0.6398	0.7549	0.7513	0.7306	0.7309	0.8574	0.8614	0.7553	0.7583
dot_ndcg_10	0.656	0.8576	0.861	0.7688	0.7676	0.8845	0.8832	0.7782	0.7788	0.843	0.8448	0.7481	0.7591	0.9158	0.9094	0.8466	0.8601	0.7737	0.778	0.6584	0.6597
dot_mrr_10	0.7509	0.8627	0.868	0.7697	0.7727	0.8859	0.8836	0.7828	0.7869	0.8716	0.8784	0.7509	0.7872	0.9503	0.9432	0.858	0.8773	0.7895	0.7988	0.6702	0.6718
dot_map_100	0.6072	0.8123	0.8174	0.7161	0.7162	0.8301	0.8311	0.7188	0.7188	0.8333	0.8405	0.7426	0.7545	0.9056	0.8989	0.8358	0.8504	0.7246	0.7278	0.6089	0.6086

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	0.6862	0.6786	0.6705	0.6562
dot_precision_1	0.6862	0.6786	0.6705	0.6562
dot_recall_1	0.0467	0.0462	0.0454	0.0434
dot_ndcg_1	0.6862	0.6786	0.6705	0.6562
dot_mrr_1	0.6862	0.6786	0.6705	0.6562
dot_map_100	0.6072	0.6023	0.5909	0.5607

Training Details

Training Dataset

rozetka_positive_pairs

Dataset: rozetka_positive_pairs
Size: 53,499,287 training samples
Columns: query and text
Approximate statistics based on the first 1000 samples:
query text
type string string
details
min: 7 tokens
mean: 13.2 tokens
max: 40 tokens

min: 6 tokens
mean: 56.49 tokens
max: 512 tokens

	query	text
type	string	string
details	min: 7 tokens mean: 13.2 tokens max: 40 tokens	min: 6 tokens mean: 56.49 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: 1,369,397 evaluation samples
Columns: query and text
Approximate statistics based on the first 1000 samples:
query text
type string string
details
min: 8 tokens
mean: 10.41 tokens
max: 14 tokens

min: 11 tokens
mean: 50.29 tokens
max: 512 tokens

	query	text
type	string	string
details	min: 8 tokens mean: 10.41 tokens max: 14 tokens	min: 11 tokens mean: 50.29 tokens max: 512 tokens

Samples:

query	text
`query: ab553446bu`	`passage: Акумулятори для мобільних телефонів`
`query: ab553446bu`	`passage: Аккумулятор AB553446BU для Samsung i320 1000 mAh (03649-25)`
`query: ab553446bu`	`passage: Аккумуляторы для мобильных телефонов`

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-10
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-10
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	760	4.4782	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0100	1520	4.298	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0150	2280	3.7517	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0200	3040	2.9677	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0250	3800	2.1971	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0300	4560	1.8109	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0350	5320	1.6811	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0400	6080	1.5155	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0450	6840	1.4494	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0500	7600	1.3583	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0550	8360	1.2634	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0600	9120	1.1704	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0650	9880	1.1106	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0700	10640	1.0827	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0750	11400	1.0318	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0800	12160	1.0159	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0850	12920	0.9551	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0900	13680	0.908	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0950	14440	0.9252	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1000	15199	-	0.7099	0.2217	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1000	15200	0.8214	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1050	15960	0.8172	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1100	16720	0.7878	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1150	17480	0.8079	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1200	18240	0.7556	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1250	19000	0.7015	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1300	19760	0.6926	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1350	20520	0.6752	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1400	21280	0.6514	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1450	22040	0.6533	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1500	22800	0.6643	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1550	23560	0.6372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1600	24320	0.602	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1650	25080	0.5874	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1700	25840	0.5992	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1750	26600	0.5684	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1800	27360	0.5775	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1850	28120	0.5668	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1900	28880	0.539	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1950	29640	0.5759	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2000	30398	-	0.4521	0.2296	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2000	30400	0.5295	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2050	31160	0.5536	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2100	31920	0.5089	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2150	32680	0.4998	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2200	33440	0.5035	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2250	34200	0.5086	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2300	34960	0.5093	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2350	35720	0.5082	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2400	36480	0.5111	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2450	37240	0.5204	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2500	38000	0.4984	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2550	38760	0.4695	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2600	39520	0.492	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2650	40280	0.4831	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2700	41040	0.4885	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2750	41800	0.4742	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2800	42560	0.4814	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2850	43320	0.4895	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2900	44080	0.4735	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2950	44840	0.479	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3000	45597	-	0.3801	0.2321	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3000	45600	0.4739	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3050	46360	0.4787	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3100	47120	0.4854	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3150	47880	0.4499	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3200	48640	0.4825	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3250	49400	0.4401	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3300	50160	0.4441	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3350	50920	0.4512	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3400	51680	0.459	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3450	52440	0.4381	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3500	53200	0.4219	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3550	53960	0.4417	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3600	54720	0.4416	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3650	55480	0.4143	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3700	56240	0.4345	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3750	57000	0.4351	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3800	57760	0.445	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3850	58520	0.4296	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3900	59280	0.4487	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3950	60040	0.4218	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4000	60796	-	0.3541	0.2339	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4000	60800	0.4427	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4050	61560	0.4535	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4100	62320	0.4506	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4150	63080	0.4213	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4200	63840	0.4293	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4250	64600	0.4112	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4300	65360	0.4261	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4350	66120	0.4232	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4400	66880	0.4322	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4450	67640	0.4169	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4500	68400	0.398	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4550	69160	0.426	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4600	69920	0.4083	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4650	70680	0.4139	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4700	71440	0.4305	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4750	72200	0.4146	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4800	72960	0.4228	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4850	73720	0.4149	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4900	74480	0.411	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4950	75240	0.3896	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5000	75995	-	0.3302	0.2366	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5000	76000	0.3936	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5050	76760	0.3955	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5100	77520	0.3984	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5150	78280	0.4076	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5200	79040	0.4109	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5250	79800	0.428	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5300	80560	0.4064	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5350	81320	0.4113	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5400	82080	0.409	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5451	82840	0.3872	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5501	83600	0.403	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5551	84360	0.3903	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5601	85120	0.4044	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5651	85880	0.401	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5701	86640	0.4059	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5751	87400	0.3946	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5801	88160	0.39	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5851	88920	0.3826	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5901	89680	0.4143	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5951	90440	0.3974	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6	91194	-	0.329	0.2374	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6001	91200	0.4139	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6051	91960	0.4232	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6101	92720	0.4011	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6151	93480	0.3973	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6201	94240	0.4059	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6251	95000	0.397	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6301	95760	0.4073	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6351	96520	0.365	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6401	97280	0.3963	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6451	98040	0.3938	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6501	98800	0.3894	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6551	99560	0.3977	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6601	100320	0.4	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6651	101080	0.3977	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6701	101840	0.4152	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6751	102600	0.3812	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6801	103360	0.4086	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6851	104120	0.4051	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6901	104880	0.4072	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6951	105640	0.4022	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7000	106393	-	0.3372	0.2381	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7001	106400	0.3968	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7051	107160	0.3706	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7101	107920	0.4186	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7151	108680	0.4076	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7201	109440	0.3908	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7251	110200	0.4042	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7301	110960	0.3835	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7351	111720	0.3891	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7401	112480	0.4026	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7451	113240	0.4007	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7501	114000	0.3967	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7551	114760	0.3847	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7601	115520	0.3817	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7651	116280	0.3981	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7701	117040	0.3889	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7751	117800	0.4015	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7801	118560	0.391	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7851	119320	0.3887	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7901	120080	0.4005	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7951	120840	0.3823	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8000	121592	-	0.3333	0.2376	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8001	121600	0.383	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8051	122360	0.4252	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8101	123120	0.396	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8151	123880	0.3882	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8201	124640	0.4026	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8251	125400	0.4042	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8301	126160	0.4047	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8351	126920	0.3832	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8401	127680	0.3977	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8451	128440	0.3842	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8501	129200	0.3679	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8551	129960	0.3889	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8601	130720	0.3985	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8651	131480	0.3843	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8701	132240	0.4125	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8751	133000	0.3934	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8801	133760	0.3835	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8851	134520	0.3852	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8901	135280	0.4017	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8951	136040	0.4022	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9000	136791	-	0.3308	0.2382	0.6560	0.8576	0.8610	0.7688	0.7676	0.8845	0.8832	0.7782	0.7788	0.8430	0.8448	0.7481	0.7591	0.9158	0.9094	0.8466	0.8601	0.7737	0.7780	0.6584	0.6597	0.6862	0.6786	0.6705	0.6562

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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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-10

Base model

intfloat/multilingual-e5-base

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Evaluation results

Dot Accuracy 10 on validation matryoshka dim 768
self-reported

0.472
Dot Precision 10 on validation matryoshka dim 768
self-reported

0.082
Dot Recall 10 on validation matryoshka dim 768
self-reported

0.342
Dot Ndcg 10 on validation matryoshka dim 768
self-reported

0.238
Dot Mrr 10 on validation matryoshka dim 768
self-reported

0.234
Dot Map 60 on validation matryoshka dim 768
self-reported

0.204
Dot Accuracy 1 on bm full
self-reported

0.686
Dot Accuracy 3 on bm full
self-reported

0.796
Dot Accuracy 5 on bm full
self-reported

0.853
Dot Accuracy 10 on bm full
self-reported

0.906

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