Modernbert Embed Base Ft Sts Spanish Matryoshka 768 64
This is a sentence transformer fine-tuned from the modernbert-embed-base model for generating sentence embeddings and calculating semantic similarity.
Downloads 443
Release Time : 1/10/2025
Model Overview
The model can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks like semantic text similarity, semantic search, paraphrase mining, text classification, and clustering.
Model Features
High-dimensional semantic representation
Can map text to a 768-dimensional vector space to capture deep semantic features
Multidimensional similarity calculation
Supports semantic similarity calculation at different dimensions (768/512/256/128/64)
Long text processing
Maximum sequence length of 8192 tokens, suitable for processing long texts
Efficient fine-tuning
Fine-tuned on private STS datasets to improve performance on semantic similarity tasks
Model Capabilities
Semantic text similarity calculation
Semantic search
Paraphrase mining
Text classification
Text clustering
Use Cases
Information retrieval
Similar document retrieval
Retrieve relevant documents by calculating document vector similarity
Content recommendation
Related content recommendation
Recommend related content to users based on semantic similarity
Q&A systems
Similar question matching
Match semantically similar questions in Q&A systems
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2697
- loss:MatryoshkaLoss
- loss:CoSENTLoss base_model: nomic-ai/modernbert-embed-base widget:
- source_sentence: En un mercado de granjeros, se encuentra un hombre.
sentences:
- Un abogado de la CPI detenido en Libia está ahora mismo encarando un período de detención de 45 días
- Un hombre está presente en un mercado donde se venden productos agrícolas directamente de los agricultores.
- ¿Existe la posibilidad de que cambie de opinión si no se expresa de manera enérgica o muestra un comportamiento inapropiado?
- source_sentence: Una mujer está posada en una postura con los brazos abiertos mientras
otra persona le toma una fotografía.
sentences:
- Un hombre se encuentra parado en medio de una multitud sujetando un objeto de color blanco.
- Las personas están cerca del agua.
- Frente a una estatua de una vaca, hay una mujer, un niño pequeño y un bebé diminuto.
- source_sentence: Un grupo de cuatro niños está observando los diferentes animales
que están en el establo.
sentences:
- Evita apoyar todo tu peso en los brazos, ya que tus manos no están diseñadas para soportar esa presión constante.
- Los niños están mirando atentamente a una oveja.
- Un puma persigue a un oso grande en el bosque.
- source_sentence: La gente se balancea saltando al agua mientras otros pescan en
el fondo del mar.
sentences:
- Dos individuos observan el agua con atención.
- Siempre golpeamos suavemente a nuestros hijos en la boca para mostrarles que su boca es lo que les causa dolor.
- Aunque el sistema de prioridad al primero en llegar beneficia a dos participantes, no asegura definitivamente la exclusión de terceros.
- source_sentence: El cordero está mirando hacia la cámara.
sentences:
- Manmohan en Teherán insta a NAM a tomar una posición clara sobre el conflicto en Siria
- 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.' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics:
- pearson_cosine
- spearman_cosine model-index:
- name: SentenceTransformer based on nomic-ai/modernbert-embed-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 768
type: sts-dev-768
metrics:
- type: pearson_cosine value: 0.7498914121357008 name: Pearson Cosine
- type: spearman_cosine value: 0.7531670275662775 name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine value: 0.7468285624371191 name: Pearson Cosine
- type: spearman_cosine value: 0.7482342767593612 name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine value: 0.7419098803201045 name: Pearson Cosine
- type: spearman_cosine value: 0.7450577925521013 name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine value: 0.7262860099881795 name: Pearson Cosine
- type: spearman_cosine value: 0.7304432975238186 name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine value: 0.6973267849431932 name: Pearson Cosine
- type: spearman_cosine value: 0.7069603266334332 name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine value: 0.8673484326459211 name: Pearson Cosine
- type: spearman_cosine value: 0.8767387684433159 name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine value: 0.8665336885415594 name: Pearson Cosine
- type: spearman_cosine value: 0.8751868367625472 name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine value: 0.8568125590206718 name: Pearson Cosine
- type: spearman_cosine value: 0.8702353416571491 name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine value: 0.8485344363338887 name: Pearson Cosine
- type: spearman_cosine value: 0.8617402150766132 name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine value: 0.8193790032247387 name: Pearson Cosine
- type: spearman_cosine value: 0.8419631939550043 name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 768
type: sts-dev-768
metrics:
SentenceTransformer based on nomic-ai/modernbert-embed-base
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.
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
andsts-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
, andscore
- 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:{ "loss": "CoSENTLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Evaluation Dataset
stsb_multi_es_augmented (private)
- Size: 697 evaluation samples
- Columns:
sentence1
,sentence2
, andscore
- 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:{ "loss": "CoSENTLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16num_train_epochs
: 5warmup_ratio
: 0.1bf16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_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
@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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