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SentenceTransformer based on intfloat/multilingual-e5-large

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large on an augmented version of stsb_multi_es dataset. 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- stsb_multi_es_aug

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("mrm8488/multilingual-e5-large-ft-sts-spanish-matryoshka-768-16-5e")
# Run inference
sentences = [
    'El avión está tocando tierra.',
    'El avión animado se encuentra en proceso de aterrizaje.',
    'Un pequeño niño montado en un columpio en el parque.',
]
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

Semantic Similarity

Dataset: sts-dev-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8382
spearman_cosine	0.843
pearson_manhattan	0.8337
spearman_manhattan	0.8449
pearson_euclidean	0.8329
spearman_euclidean	0.8442
pearson_dot	0.8287
spearman_dot	0.8323
pearson_max	0.8382
spearman_max	0.8449

Semantic Similarity

Dataset: sts-dev-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8335
spearman_cosine	0.8406
pearson_manhattan	0.8317
spearman_manhattan	0.8426
pearson_euclidean	0.8306
spearman_euclidean	0.8415
pearson_dot	0.8173
spearman_dot	0.823
pearson_max	0.8335
spearman_max	0.8426

Semantic Similarity

Dataset: sts-dev-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.824
spearman_cosine	0.8356
pearson_manhattan	0.8261
spearman_manhattan	0.8355
pearson_euclidean	0.8256
spearman_euclidean	0.8362
pearson_dot	0.7925
spearman_dot	0.7993
pearson_max	0.8261
spearman_max	0.8362

Semantic Similarity

Dataset: sts-dev-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8099
spearman_cosine	0.8305
pearson_manhattan	0.8209
spearman_manhattan	0.8308
pearson_euclidean	0.8195
spearman_euclidean	0.8302
pearson_dot	0.7413
spearman_dot	0.749
pearson_max	0.8209
spearman_max	0.8308

Semantic Similarity

Dataset: sts-dev-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7778
spearman_cosine	0.8152
pearson_manhattan	0.8007
spearman_manhattan	0.8116
pearson_euclidean	0.8001
spearman_euclidean	0.8111
pearson_dot	0.6541
spearman_dot	0.659
pearson_max	0.8007
spearman_max	0.8152

Semantic Similarity

Dataset: sts-dev-32
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7277
spearman_cosine	0.7806
pearson_manhattan	0.766
spearman_manhattan	0.7752
pearson_euclidean	0.7674
spearman_euclidean	0.7773
pearson_dot	0.5395
spearman_dot	0.5342
pearson_max	0.7674
spearman_max	0.7806

Semantic Similarity

Dataset: sts-dev-16
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6737
spearman_cosine	0.7425
pearson_manhattan	0.7187
spearman_manhattan	0.728
pearson_euclidean	0.7235
spearman_euclidean	0.7374
pearson_dot	0.447
spearman_dot	0.4424
pearson_max	0.7235
spearman_max	0.7425

Semantic Similarity

Dataset: sts-test-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8637
spearman_cosine	0.8775
pearson_manhattan	0.8739
spearman_manhattan	0.8771
pearson_euclidean	0.8743
spearman_euclidean	0.8774
pearson_dot	0.8587
spearman_dot	0.8693
pearson_max	0.8743
spearman_max	0.8775

Semantic Similarity

Dataset: sts-test-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8609
spearman_cosine	0.8761
pearson_manhattan	0.8723
spearman_manhattan	0.8755
pearson_euclidean	0.8727
spearman_euclidean	0.8759
pearson_dot	0.8498
spearman_dot	0.8568
pearson_max	0.8727
spearman_max	0.8761

Semantic Similarity

Dataset: sts-test-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8546
spearman_cosine	0.8715
pearson_manhattan	0.8698
spearman_manhattan	0.8737
pearson_euclidean	0.8699
spearman_euclidean	0.8737
pearson_dot	0.8131
spearman_dot	0.8076
pearson_max	0.8699
spearman_max	0.8737

Semantic Similarity

Dataset: sts-test-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8388
spearman_cosine	0.8645
pearson_manhattan	0.8611
spearman_manhattan	0.8667
pearson_euclidean	0.8622
spearman_euclidean	0.868
pearson_dot	0.7492
spearman_dot	0.7364
pearson_max	0.8622
spearman_max	0.868

Semantic Similarity

Dataset: sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8168
spearman_cosine	0.8585
pearson_manhattan	0.8518
spearman_manhattan	0.8607
pearson_euclidean	0.8534
spearman_euclidean	0.8624
pearson_dot	0.6646
spearman_dot	0.6473
pearson_max	0.8534
spearman_max	0.8624

Semantic Similarity

Dataset: sts-test-32
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7814
spearman_cosine	0.8425
pearson_manhattan	0.8315
spearman_manhattan	0.8432
pearson_euclidean	0.8345
spearman_euclidean	0.8466
pearson_dot	0.5521
spearman_dot	0.5319
pearson_max	0.8345
spearman_max	0.8466

Semantic Similarity

Dataset: sts-test-16
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7198
spearman_cosine	0.8072
pearson_manhattan	0.7806
spearman_manhattan	0.7998
pearson_euclidean	0.7879
spearman_euclidean	0.809
pearson_dot	0.4496
spearman_dot	0.4412
pearson_max	0.7879
spearman_max	0.809

Training Details

Training Dataset

stsb_multi_es_aug

Dataset: stsb_multi_es_aug
Size: 2,697 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 8 tokens
mean: 22.25 tokens
max: 68 tokens

min: 8 tokens
mean: 22.01 tokens
max: 79 tokens

min: 0.0
mean: 2.67
max: 5.0

	sentence1	sentence2	score
type	string	string	float
details	min: 8 tokens mean: 22.25 tokens max: 68 tokens	min: 8 tokens mean: 22.01 tokens max: 79 tokens	min: 0.0 mean: 2.67 max: 5.0

Samples:

sentence1	sentence2	score
`El pájaro de tamaño reducido se posó con delicadeza en una rama cubierta de escarcha.`	`Un ave de color amarillo descansaba tranquilamente en una rama.`	`3.200000047683716`
`Una chica está tocando la flauta en un parque.`	`Un grupo de músicos está tocando en un escenario al aire libre.`	`1.286`
`La aclamada escritora británica, Doris Lessing, galardonada con el premio Nobel, fallece`	`La destacada autora británica, Doris Lessing, reconocida con el prestigioso Premio Nobel, muere`	`4.199999809265137`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "CoSENTLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64,
        32,
        16
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Evaluation Dataset

stsb_multi_es_aug

Dataset: stsb_multi_es_aug
Size: 697 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 8 tokens
mean: 22.76 tokens
max: 67 tokens

min: 7 tokens
mean: 22.26 tokens
max: 63 tokens

min: 0.0
mean: 2.3
max: 5.0

	sentence1	sentence2	score
type	string	string	float
details	min: 8 tokens mean: 22.76 tokens max: 67 tokens	min: 7 tokens mean: 22.26 tokens max: 63 tokens	min: 0.0 mean: 2.3 max: 5.0

Samples:

sentence1	sentence2	score
`Un incendio ocurrido en un hospital psiquiátrico ruso resultó en la trágica muerte de 38 personas.`	`Se teme que el incendio en un hospital psiquiátrico ruso cause la pérdida de la vida de 38 individuos.`	`4.199999809265137`
`"Street dijo que el otro individuo a veces se siente avergonzado de su fiesta, lo cual provoca risas en la multitud"`	`"A veces, el otro tipo se encuentra avergonzado de su fiesta y no se le puede culpar."`	`3.5`
`El veterano diplomático de Malasia tuvo un encuentro con Suu Kyi el miércoles en la casa del lago en Yangon donde permanece bajo arresto domiciliario.`	`Razali Ismail tuvo una reunión de 90 minutos con Suu Kyi, quien ganó el Premio Nobel de la Paz en 1991, en su casa del lago donde está recluida.`	`3.691999912261963`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "CoSENTLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64,
        32,
        16
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 5
warmup_ratio: 0.1
fp16: 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: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 1.0
num_train_epochs: 5
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: 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: False
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	sts-dev-128_spearman_cosine	sts-dev-16_spearman_cosine	sts-dev-256_spearman_cosine	sts-dev-32_spearman_cosine	sts-dev-512_spearman_cosine	sts-dev-64_spearman_cosine	sts-dev-768_spearman_cosine	sts-test-128_spearman_cosine	sts-test-16_spearman_cosine	sts-test-256_spearman_cosine	sts-test-32_spearman_cosine	sts-test-512_spearman_cosine	sts-test-64_spearman_cosine	sts-test-768_spearman_cosine
0.5917	100	30.7503	30.6172	0.8117	0.7110	0.8179	0.7457	0.8244	0.7884	0.8252	-	-	-	-	-	-	-
1.1834	200	30.4696	32.6422	0.7952	0.7198	0.8076	0.7491	0.8125	0.7813	0.8142	-	-	-	-	-	-	-
1.7751	300	29.9233	31.5469	0.8152	0.7435	0.8250	0.7737	0.8302	0.8006	0.8305	-	-	-	-	-	-	-
2.3669	400	29.0716	31.8088	0.8183	0.7405	0.8248	0.7758	0.8299	0.8057	0.8324	-	-	-	-	-	-	-
2.9586	500	28.7971	32.6032	0.8176	0.7430	0.8241	0.7777	0.8289	0.8025	0.8316	-	-	-	-	-	-	-
3.5503	600	27.4766	34.7911	0.8241	0.7400	0.8314	0.7730	0.8369	0.8061	0.8394	-	-	-	-	-	-	-
4.1420	700	27.0639	35.7418	0.8294	0.7466	0.8354	0.7784	0.8389	0.8107	0.8409	-	-	-	-	-	-	-
4.7337	800	26.5119	36.2014	0.8305	0.7425	0.8356	0.7806	0.8406	0.8152	0.8430	-	-	-	-	-	-	-
5.0	845	-	-	-	-	-	-	-	-	-	0.8645	0.8072	0.8715	0.8425	0.8761	0.8585	0.8775

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.0
Transformers: 4.41.1
PyTorch: 2.3.0+cu121
Accelerate: 0.30.1
Datasets: 2.19.1
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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}

Downloads last month: 32

Safetensors

Model size

560M params

Tensor type

F32

Inference Examples

Sentence Similarity

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Model tree for mrm8488/multilingual-e5-large-ft-sts-spanish-matryoshka-768-16-5e

Base model

intfloat/multilingual-e5-large

Finetuned

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Collection including mrm8488/multilingual-e5-large-ft-sts-spanish-matryoshka-768-16-5e

embeddings-spanish-models 🎯

Collection

A collection with embeddings models I fine-tuned for better performance in Spanish texts. • 3 items • Updated Aug 30 • 2

Evaluation results

Pearson Cosine on sts dev 768
self-reported

0.838
Spearman Cosine on sts dev 768
self-reported

0.843
Pearson Manhattan on sts dev 768
self-reported

0.834
Spearman Manhattan on sts dev 768
self-reported

0.845
Pearson Euclidean on sts dev 768
self-reported

0.833
Spearman Euclidean on sts dev 768
self-reported

0.844
Pearson Dot on sts dev 768
self-reported

0.829
Spearman Dot on sts dev 768
self-reported

0.832
Pearson Max on sts dev 768
self-reported

0.838
Spearman Max on sts dev 768
self-reported

0.845

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