This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large on the clibrain/stsb_multi_es_aug_gpt3.5-turbo_2 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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("mrm8488/multilingual-e5-large-ft-sts-spanish-matryoshka-768-64-5e")
# Run inference
sentences = [
    'tres perros gruñendo entre sí',
    'Dos perros se aproximan uno al otro en el pasto.',
    'Una mujer sonriente brinda cariño a un pequeño bebé.',
]
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	0.828
spearman_cosine	0.8343
pearson_manhattan	0.8228
spearman_manhattan	0.8349
pearson_euclidean	0.8231
spearman_euclidean	0.8349
pearson_dot	0.8196
spearman_dot	0.8249
pearson_max	0.828
spearman_max	0.8349

Semantic Similarity

Dataset: sts-dev-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8236
spearman_cosine	0.8333
pearson_manhattan	0.8218
spearman_manhattan	0.8332
pearson_euclidean	0.8218
spearman_euclidean	0.8334
pearson_dot	0.8102
spearman_dot	0.8179
pearson_max	0.8236
spearman_max	0.8334

Semantic Similarity

Dataset: sts-dev-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8162
spearman_cosine	0.8304
pearson_manhattan	0.8179
spearman_manhattan	0.8301
pearson_euclidean	0.8184
spearman_euclidean	0.8302
pearson_dot	0.7879
spearman_dot	0.7905
pearson_max	0.8184
spearman_max	0.8304

Semantic Similarity

Dataset: sts-dev-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7942
spearman_cosine	0.8198
pearson_manhattan	0.8089
spearman_manhattan	0.8223
pearson_euclidean	0.8092
spearman_euclidean	0.822
pearson_dot	0.7342
spearman_dot	0.7352
pearson_max	0.8092
spearman_max	0.8223

Semantic Similarity

Dataset: sts-dev-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7727
spearman_cosine	0.8077
pearson_manhattan	0.7976
spearman_manhattan	0.8148
pearson_euclidean	0.7979
spearman_euclidean	0.8124
pearson_dot	0.6726
spearman_dot	0.6673
pearson_max	0.7979
spearman_max	0.8148

Semantic Similarity

Dataset: sts-test-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.863
spearman_cosine	0.8813
pearson_manhattan	0.8771
spearman_manhattan	0.8811
pearson_euclidean	0.877
spearman_euclidean	0.8812
pearson_dot	0.8582
spearman_dot	0.8707
pearson_max	0.8771
spearman_max	0.8813

Semantic Similarity

Dataset: sts-test-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.859
spearman_cosine	0.88
pearson_manhattan	0.8744
spearman_manhattan	0.8791
pearson_euclidean	0.8748
spearman_euclidean	0.8796
pearson_dot	0.8464
spearman_dot	0.855
pearson_max	0.8748
spearman_max	0.88

Semantic Similarity

Dataset: sts-test-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8528
spearman_cosine	0.8763
pearson_manhattan	0.8715
spearman_manhattan	0.8781
pearson_euclidean	0.8725
spearman_euclidean	0.8789
pearson_dot	0.802
spearman_dot	0.8007
pearson_max	0.8725
spearman_max	0.8789

Semantic Similarity

Dataset: sts-test-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8392
spearman_cosine	0.8692
pearson_manhattan	0.8632
spearman_manhattan	0.8716
pearson_euclidean	0.8644
spearman_euclidean	0.8724
pearson_dot	0.7462
spearman_dot	0.7403
pearson_max	0.8644
spearman_max	0.8724

Semantic Similarity

Dataset: sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8214
spearman_cosine	0.8621
pearson_manhattan	0.8531
spearman_manhattan	0.8632
pearson_euclidean	0.8541
spearman_euclidean	0.8633
pearson_dot	0.6854
spearman_dot	0.6726
pearson_max	0.8541
spearman_max	0.8633

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

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:
json
1{ 2 "loss": "CoSENTLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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

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:
json
1{ 2 "loss": "CoSENTLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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-256_spearman_cosine	sts-dev-512_spearman_cosine	sts-dev-64_spearman_cosine	sts-dev-768_spearman_cosine	sts-test-128_spearman_cosine	sts-test-256_spearman_cosine	sts-test-512_spearman_cosine	sts-test-64_spearman_cosine	sts-test-768_spearman_cosine
0.5917	100	21.7032	21.7030	0.8030	0.8124	0.8205	0.7839	0.8215	-	-	-	-	-
1.1834	200	21.4019	24.0898	0.7839	0.7972	0.8038	0.7680	0.8062	-	-	-	-	-
1.7751	300	21.2168	22.5421	0.7909	0.8027	0.8058	0.7786	0.8068	-	-	-	-	-
2.3669	400	20.7049	23.6522	0.7938	0.8049	0.8108	0.7873	0.8123	-	-	-	-	-
2.9586	500	20.5077	23.6100	0.8017	0.8116	0.8155	0.7893	0.8185	-	-	-	-	-
3.5503	600	19.2725	24.7539	0.8133	0.8254	0.8291	0.8032	0.8314	-	-	-	-	-
4.1420	700	19.0841	26.5286	0.8210	0.8298	0.8333	0.8102	0.8333	-	-	-	-	-
4.7337	800	18.6847	26.8158	0.8198	0.8304	0.8333	0.8077	0.8343	-	-	-	-	-
5.0	845	-	-	-	-	-	-	-	0.8692	0.8763	0.8800	0.8621	0.8813

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

bibtex

@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

bibtex

@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

bibtex

@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},
}

	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