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multilingual-e5-large-ft-sts-spanish-matryoshka-768-16-5e

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dataset_size:1K<n<10K
loss:MatryoshkaLoss
loss:CoSENTLoss
Eval Results
text-embeddings-inference
Model card Files
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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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
  • 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
  • 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},
}
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