This is a sentence-transformers model finetuned from nomic-ai/modernbert-embed-base on the stsb_multi_es_augmented (private) dataset. It maps sentences & paragraphs to a 768-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: nomic-ai/modernbert-embed-base
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- Private stsb dataset

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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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/modernbert-embed-base-ft-sts-spanish-matryoshka-768-64-5e")
# Run inference
sentences = [
    'El cordero está mirando hacia la cámara.',
    'Un gato está mirando hacia la cámara también.',
    '"Sí, no deseo estar presente durante este testimonio", declaró tranquilamente Peterson, de 31 años, al juez cuando fue devuelto a su celda.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Semantic Similarity

Datasets: sts-dev-768, sts-dev-512, sts-dev-256, sts-dev-128, sts-dev-64, sts-test-768, sts-test-512, sts-test-256, sts-test-128 and sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-dev-768	sts-dev-512	sts-dev-256	sts-dev-128	sts-dev-64	sts-test-768	sts-test-512	sts-test-256	sts-test-128	sts-test-64
pearson_cosine	0.7499	0.7468	0.7419	0.7263	0.6973	0.8673	0.8665	0.8568	0.8485	0.8194
spearman_cosine	0.7532	0.7482	0.7451	0.7304	0.707	0.8767	0.8752	0.8702	0.8617	0.842

Training Details

Training Dataset

stsb_multi_es_augmented (private)

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: 9 tokens
mean: 28.42 tokens
max: 96 tokens
min: 10 tokens
mean: 28.01 tokens
max: 92 tokens
min: 0.0
mean: 2.72
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_augmented (private)

Size: 697 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 697 samples:
sentence1 sentence2 score
type string string float
details
min: 9 tokens
mean: 29.35 tokens
max: 87 tokens
min: 9 tokens
mean: 28.52 tokens
max: 81 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
bf16: 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
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: 1.0
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: True
fp16: False
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	sts-dev-768_spearman_cosine	sts-dev-512_spearman_cosine	sts-dev-256_spearman_cosine	sts-dev-128_spearman_cosine	sts-dev-64_spearman_cosine	sts-test-768_spearman_cosine	sts-test-512_spearman_cosine	sts-test-256_spearman_cosine	sts-test-128_spearman_cosine	sts-test-64_spearman_cosine
0.5917	100	23.7709	22.5494	0.7185	0.7146	0.7055	0.6794	0.6570	-	-	-	-	-
1.1834	200	22.137	22.7634	0.7449	0.7412	0.7439	0.7287	0.7027	-	-	-	-	-
1.7751	300	21.5527	22.6985	0.7321	0.7281	0.7243	0.7063	0.6862	-	-	-	-	-
2.3669	400	20.5745	24.0021	0.7302	0.7264	0.7221	0.7097	0.6897	-	-	-	-	-
2.9586	500	20.0861	24.0091	0.7392	0.7361	0.7293	0.7124	0.6906	-	-	-	-	-
3.5503	600	18.8191	26.9012	0.7502	0.7462	0.7399	0.7207	0.6960	-	-	-	-	-
4.1420	700	18.3	29.0209	0.7496	0.7454	0.7432	0.7284	0.7065	-	-	-	-	-
4.7337	800	17.6496	28.9536	0.7532	0.7482	0.7451	0.7304	0.7070	-	-	-	-	-
5.0	845	-	-	-	-	-	-	-	0.8767	0.8752	0.8702	0.8617	0.8420

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.48.0
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

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: 9 tokens mean: 28.42 tokens max: 96 tokens	min: 10 tokens mean: 28.01 tokens max: 92 tokens	min: 0.0 mean: 2.72 max: 5.0