SentenceTransformer based on intfloat/multilingual-e5-large-instruct

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct. It maps sentences & paragraphs to a 1024-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-large-instruct
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 tokens
Similarity Function: Cosine Similarity

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': 1024, '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("Data-Lab/multilingual-e5-large-instruct-embedder_distill-tgd")
# Run inference
sentences = [
    'ленивые',
    'Instruct: Найти похожие продукты на основе деталей\nQuery: Большая порция ленивых голубцов в сметанном соусе None, голубцы, курица, рис, капуста, сметанный соус, овощи, домашняя кухня, готовое блюдо Голубцы из курицы с рисом, лучком, морковью и капустой в фарше, томленые в сметанном соусе.',
    'Instruct: Найти похожие продукты на основе деталей\nQuery: Жидкость для снятия лака "Кератиновая", 110 мл None, жидкость для снятия лака, укрепление ногтей, витамины, масло арганы, алоэ вера, уход за ногтями, None Бережно очищает и укрепляет ногти. Витамины, масло арганы и сок алоэ вера питают и восстанавливают.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Triplet

Dataset: dev
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.8994
dot_accuracy	0.1006
manhattan_accuracy	0.9029
euclidean_accuracy	0.8994
max_accuracy	0.9029

Training Details

Training Dataset

Unnamed Dataset

Size: 10,190 training samples
Columns: sentence_0, sentence_1, sentence_2, and label

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2	label
type	string	string	string	list
details	min: 3 tokens mean: 7.9 tokens max: 30 tokens	min: 39 tokens mean: 116.51 tokens max: 512 tokens	min: 41 tokens mean: 120.24 tokens max: 512 tokens	size: 3 elements

Samples:

sentence_0	sentence_1	sentence_2	label
`крем суп с лес`	`Instruct: Найти похожие продукты на основе деталей Query: Крем-суп с лесными грибами, 270 г None, крем-суп, лесные грибы, ароматный, шелковистый, насыщенный, 270 г Шелковистый крем-суп с лесными грибами, насыщенный и ароматный`	`Instruct: Найти похожие продукты на основе деталей Query: Коврик-подставка силиконовый None, силикон, эластичный, кухонные аксессуары, подставка, прихватка, открывание крышек, None Эластичный коврик-подставка, прихватку, для открытия крышек`	[[0.015802893787622452, 0.027458976954221725, 8.673759293742478e-05, -0.06202685087919235, 0.014784332364797592, -0.016287295147776604, -0.01697077788412571, 0.07662203162908554, 0.021663609892129898, -0.029335850849747658, 0.04525122046470642, 0.03856947273015976, -0.02774658054113388, -0.04689972475171089, -0.00804208591580391, -0.01562741957604885, -0.03728794306516647, 0.0346778929233551, -0.04056645557284355, -0.017431482672691345, 0.03701236844062805, -0.0040060486644506454, -0.03751157596707344, 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`сладкий банан для смузи`	Instruct: Найти похожие продукты на основе деталей Query: Смузи "Банан-клубника", 300 мл смузи, фруктовый напиток, клубника, банан, без добавленного сахара, здоровое питание, перекус, освежающий, натуральный, витаминизированный, легко переносимый Натуральный фруктово-ягодный смузи заботливо приготовлен из клубничного сока, пюре из сладкой садовой клубники и спелых бананов. Не содержит добавленного сахара, искусственных ароматизаторов и красителей. У этого сытного напитка яркий вкус с освежающей кислинкой. Отлично утоляет жажду, придаёт сил и бодрит. На завтрак или для здорового перекуса. Небольшую бутылочку удобно захватить с собой на прогулку, в поездку, в офис или в кино.	Instruct: Найти похожие продукты на основе деталей Query: Конфета протеиновая "Миндальный трюфель" протеиновая, низкокалорийная, без сахара, финиковая, шоколадная, ореховая, полезный перекус, низкий гликемический индекс, для спортсменов, сладость, веганская, без глютена, без аллергенов Конфета на финиковой основе с миндальной пастой, обогащённая белком. Обладает плотной, но тающей во рту текстурой и объёмным шоколадно-ореховым вкусом. Умеренно сладкая и питательная, она подойдёт как для чаепития, так и для полезного перекуса. Не содержит добавленного сахара.	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`мороженое веган`	`Instruct: Найти похожие продукты на основе деталей Query: Эскимо 'Пина Колада' None, веганский, десерт, кокос, ананас, замороженный Веганский десерт из кокоса и ананаса`	`Instruct: Найти похожие продукты на основе деталей Query: Сорбет из маракуйи сорбет, маракуйя, десерт, тропический вкус, низкая калорийность, сладость, закуска, козики сосуда, охлаждение, фруктовый десерт Тропический сорбет с кислым-сладким вкусом маракуйи`	[[0.026767171919345856, 0.013932106085121632, 0.015057631768286228, -0.04809686914086342, 0.005540795158594847, 0.00270196539349854, -0.018303072080016136, 0.08608134835958481, 0.04748193547129631, -0.021445805206894875, 0.031327031552791595, 0.02032851055264473, 0.0043844906613230705, -0.03350745886564255, -0.008186091668903828, -0.04003987833857536, -0.0576988160610199, 0.04856528341770172, -0.02198709361255169, -0.006314707454293966, 0.038219209760427475, 0.001512942835688591, -0.06373175978660583, -0.0514586977660656, -0.008774321526288986, 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Loss: main.DistillationTripletLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
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: adamw_torch
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: False
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
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	dev_max_accuracy
0.3928	500	0.1304	-
0.7855	1000	0.088	0.9135
1.0	1273	-	0.9109
1.1783	1500	0.07	-
1.5711	2000	0.0527	0.9056
1.9639	2500	0.0401	-
2.0	2546	-	0.9082
2.3566	3000	0.0324	0.9153
2.7494	3500	0.0247	-
3.0	3819	-	0.9029

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.2.0
Transformers: 4.44.0
PyTorch: 2.3.1+cu121
Accelerate: 0.31.0
Datasets: 2.20.0
Tokenizers: 0.19.1

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",
}

Data-Lab
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multilingual-e5-large-instruct-embedder_distill-tgd