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-tg")
# Run inference
sentences = [
    'малин',
    'Instruct: Найти похожие продукты на основе деталей\nQuery: Малина фермерская, 125 г малина, фермерская, сладкая, ароматная, ягоды, джем, варенье, морс, мусс, желе, десерт, полезное питание, натуральный продукт, выпечка, добавка к блюдам',
    'Instruct: Найти похожие продукты на основе деталей\nQuery: Малина и облепиха протертые с сахаром, 200 г None, мед, десерт, ягоды, натуральные ингредиенты, здоровое питание, сладость, без консервантов, None',
]
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	0.9223
dot_accuracy	0.0777
manhattan_accuracy	0.9206
euclidean_accuracy	0.9223
max_accuracy	0.9223

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: 8.27 tokens max: 29 tokens	min: 34 tokens mean: 68.4 tokens max: 130 tokens	min: 37 tokens mean: 70.44 tokens max: 137 tokens	size: 3 elements

Samples:

sentence_0	sentence_1	sentence_2	label
`пиво безалкогольное`	Instruct: Найти похожие продукты на основе деталей Query: Пиво "Бакалар" безалкогольное светлое холодного охмеления, 330 мл безалкогольное, светлое пиво, холодное охмеление, лагер, Чехия, фильтрованное, пастеризованное, напиток с низким содержанием алкоголя, плотный вкус, горчинка, сочетание с едой, 330 мл	`Instruct: Найти похожие продукты на основе деталей Query: Пиво "Рижское" светлое фильтрованное пиво, светлое, фильтрованное, ячменный солод, карамельный солод, алкоголь 4.9%, освежающий вкус, горечь, закуски, диоксид серы, Россия`	[[0.028471343219280243, 0.013647344894707203, -0.01368124969303608, -0.045746996998786926, 0.022533774375915527, 0.0005974382511340082, 0.017170460894703865, 0.07986210286617279, 0.047736626118421555, -0.010252098552882671, 0.022612091153860092, -0.0017559807747602463, -0.028282305225729942, -0.025950592011213303, -0.011386270634829998, -0.04217831790447235, -0.06597516685724258, 0.0143783213570714, -0.006398841273039579, 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`трюфель`	`Instruct: Найти похожие продукты на основе деталей Query: Конфеты веган "Трюфель с фундуком" 2шт None, веганские конфеты, шоколад, трюфель, фундук, сладости, None`	`Instruct: Найти похожие продукты на основе деталей Query: Бургер Трюфель бургер, мраморная говядина, чеддер, шпинат, лук, яйцо, голландский соус, трюфельное масло, коул слоу, картофель фри`	[[0.003756050020456314, 0.019587218761444092, 0.010780439712107182, -0.056993454694747925, 0.0184906255453825, 0.004006661009043455, 0.00945166777819395, 0.09036280959844589, 0.028595196083188057, -0.008867761120200157, 0.03172329440712929, 0.025312403216958046, -0.03228877857327461, -0.035275671631097794, -0.008146975189447403, -0.02784574031829834, -0.03905689716339111, 0.02442908100783825, -0.018150798976421356, 0.00173333496786654, 0.025070739910006523, 0.012937502935528755, -0.05534491688013077, -0.052861426025629044, -0.03236917033791542, 0.011576652526855469, -0.019496120512485504, -0.02246737852692604, 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`масло какао`	`Instruct: Найти похожие продукты на основе деталей Query: Какао масло в дисках, 100 гр натуральный продукт, нерафинированное масло, кулинария, десерты, выпечка, глазурь, аромат, ритуалы красоты, увлажение, питание, смягчение, уход за волосами, уход за кожей, Колумбия`	`Instruct: Найти похожие продукты на основе деталей Query: Крем-масло для тела "Кокос" крем, масло для тела, увлажнение, питание, смягчение, эластичность, регенерация, коллаген, уход за кожей, кокос, масла, сухая кожа, воспаление, успокоение, индивидуальная непереносимость`	[[0.028976688161492348, 0.026656223461031914, 0.009426922537386417, -0.060251384973526, 0.01592271775007248, 0.005438604392111301, 0.0017150921048596501, 0.1088123545050621, 0.03497568890452385, -0.030431341379880905, 0.03993084654211998, 0.010625554248690605, -0.03960930556058884, -0.0320606604218483, -0.017990315333008766, -0.022068828344345093, -0.03879866376519203, 0.015070708468556404, -0.03331803157925606, 0.001215354073792696, 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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	0.13	-
0.7855	1000	0.0845	0.9056
1.0	1273	-	0.9162
1.1783	1500	0.0697	-
1.5711	2000	0.0513	0.9214
1.9639	2500	0.0405	-
2.0	2546	-	0.9214
2.3566	3000	0.0323	0.9073
2.7494	3500	0.0263	-
3.0	3819	-	0.9223

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-tg