SentenceTransformer based on intfloat/multilingual-e5-base

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-base on the rztk/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 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- rztk/rozetka_positive_pairs

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'поилка для детей',
    "<category>Поїльники та непроливайки</category><brand>Nuk</brand><options><option_title>Стать дитини</option_title><option_value>Хлопчик</option_value><option_title>Стать дитини</option_title><option_value>Дівчинка</option_value><option_title>Кількість вантажних місць</option_title><option_value>1</option_value><option_title>Країна реєстрації бренда</option_title><option_value>Німеччина</option_value><option_title>Країна-виробник товару</option_title><option_value>Німеччина</option_value><option_title>Об'єм, мл</option_title><option_value>300</option_value><option_title>Матеріал</option_title><option_value>Пластик</option_value><option_title>Колір</option_title><option_value>Блакитний</option_value><option_title>Тип</option_title><option_value>Поїльник</option_value><option_title>Тип гарантійного талона</option_title><option_value>Гарантія по чеку</option_value><option_title>Доставка Premium</option_title></options>",
    'Шафа розпашній Fenster Оксфорд Лагуна',
]
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

Information Retrieval

Dataset: rusisms-uk-title
Evaluated with InformationRetrievalEvaluator

Metric	Value
dot_accuracy@1	0.5429
dot_accuracy@3	0.6889
dot_accuracy@5	0.7492
dot_accuracy@10	0.8
dot_precision@1	0.5429
dot_precision@3	0.5217
dot_precision@5	0.5035
dot_precision@10	0.4768
dot_recall@1	0.0092
dot_recall@3	0.0238
dot_recall@5	0.0351
dot_recall@10	0.0599
dot_ndcg@10	0.4937
dot_mrr@10	0.6287
dot_map@100	0.1404

Information Retrieval

Dataset: rusisms-uk-title--matryoshka_dim-768--
Evaluated with InformationRetrievalEvaluator

Metric	Value
dot_accuracy@1	0.1619
dot_precision@1	0.1619
dot_recall@1	0.002
dot_ndcg@1	0.1619
dot_mrr@1	0.1619
dot_map@100	0.0213

Information Retrieval

Dataset: rusisms-uk-title--matryoshka_dim-512--
Evaluated with InformationRetrievalEvaluator

Metric	Value
dot_accuracy@1	0.146
dot_precision@1	0.146
dot_recall@1	0.0017
dot_ndcg@1	0.146
dot_mrr@1	0.146
dot_map@100	0.0152

Information Retrieval

Dataset: rusisms-uk-title--matryoshka_dim-256--
Evaluated with InformationRetrievalEvaluator

Metric	Value
dot_accuracy@1	0.1016
dot_precision@1	0.1016
dot_recall@1	0.0013
dot_ndcg@1	0.1016
dot_mrr@1	0.1016
dot_map@100	0.012

Information Retrieval

Dataset: rusisms-uk-title--matryoshka_dim-128--
Evaluated with InformationRetrievalEvaluator

Metric	Value
dot_accuracy@1	0.054
dot_precision@1	0.054
dot_recall@1	0.0007
dot_ndcg@1	0.054
dot_mrr@1	0.054
dot_map@100	0.0054

Training Details

Training Dataset

rztk/rozetka_positive_pairs

Dataset: rztk/rozetka_positive_pairs
Size: 44,800 training samples
Columns: query and text
Approximate statistics based on the first 1000 samples:
query text
type string string
details
min: 3 tokens
mean: 7.18 tokens
max: 16 tokens

min: 9 tokens
mean: 158.88 tokens
max: 512 tokens

	query	text
type	string	string
details	min: 3 tokens mean: 7.18 tokens max: 16 tokens	min: 9 tokens mean: 158.88 tokens max: 512 tokens

Samples:

query	text
`p smart z`	`TPU чехол Ultrathin Series 0,33 mm для Huawei P Smart Z Безбарвний (прозорий)`
`p smart z`	`Чохли для мобільних телефонівМатеріалСиліконКолірTransparentСумісна модельP Smart Z`
`p smart z`	`TPU чехол Ultrathin Series 0,33mm для Huawei P Smart Z Бесцветный (прозрачный)`

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

rztk/rozetka_positive_pairs

Dataset: rztk/rozetka_positive_pairs
Size: 4,480 evaluation samples
Columns: query and text
Approximate statistics based on the first 1000 samples:
query text
type string string
details
min: 3 tokens
mean: 6.29 tokens
max: 11 tokens

min: 12 tokens
mean: 161.36 tokens
max: 512 tokens

	query	text
type	string	string
details	min: 3 tokens mean: 6.29 tokens max: 11 tokens	min: 12 tokens mean: 161.36 tokens max: 512 tokens

Samples:

query	text
`кошелек женский`	`Портмоне BAELLERRY Forever N2345 Черный (020354)`
`кошелек женский`	`ГаманціBaellerryДля когоДля жінокВидПортмонеМатеріалШтучна шкіраКраїна-виробник товаруКитай`
`кошелек женский`	`Портмоне BAELLERRY Forever N2345 Черный (020354)`

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: 112
per_device_eval_batch_size: 112
torch_empty_cache_steps: 30
learning_rate: 2e-05
num_train_epochs: 1.0
warmup_ratio: 0.1
bf16: True
bf16_full_eval: True
tf32: True
dataloader_num_workers: 2
load_best_model_at_end: True
optim: adafactor
push_to_hub: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 112
per_device_eval_batch_size: 112
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: 30
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: 2
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: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
ddp_static_graph: False
ddp_comm_hook: bf16
gradient_as_bucket_view: False

Training Logs

Epoch	Step	Training Loss	loss	rusisms-uk-title--matryoshka_dim-128--_dot_map@100	rusisms-uk-title--matryoshka_dim-256--_dot_map@100	rusisms-uk-title--matryoshka_dim-512--_dot_map@100	rusisms-uk-title--matryoshka_dim-768--_dot_map@100	rusisms-uk-title_dot_map@100
0.1	10	6.6103	-	-	-	-	-	-
0.2	20	5.524	-	-	-	-	-	-
0.3	30	4.759	3.6444	-	-	-	-	-
0.4	40	4.5195	-	-	-	-	-	-
0.5	50	3.6598	-	-	-	-	-	-
0.6	60	3.7912	2.8962	-	-	-	-	-
0.7	70	3.9935	-	-	-	-	-	-
0.8	80	3.3929	-	-	-	-	-	-
0.9	90	3.6101	2.6889	-	-	-	-	-
1.0	100	3.8753	-	0.0054	0.0120	0.0152	0.0213	0.1404

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.6
Sentence Transformers: 3.0.1
Transformers: 4.45.1
PyTorch: 2.4.1
Accelerate: 0.34.2
Datasets: 3.0.0
Tokenizers: 0.20.0

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",
}

rztk-bohdanbilonoh
/

multilingual-e5-base-test

SentenceTransformer based on intfloat/multilingual-e5-base

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Training Details

Training Dataset

rztk/rozetka_positive_pairs

Evaluation Dataset

rztk/rozetka_positive_pairs

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

Model tree for rztk-bohdanbilonoh/multilingual-e5-base-test

Evaluation results