This is a sentence-transformers model trained. 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

Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

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, 'architecture': '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})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'cta test̾i sur: u̾bi qd̾ madauit in mil egones. Q uod disposunt ad abrahã. mũti sui ad p̃saaci Et statuit il acob ĩ p̾ceptũ: ⁊ isrł mn testiñ etꝰ Dices tibi dabo t̾ram chanaan: fu ncdũ heditatis ur̃e. Dũ e̾e̾nt nũo ocui. paucissimi ⁊ ĩcole ouis. Et ꝑtͣni eẽt de gnͣte ĩ gentẽ: ⁊ de regno ad ulũ alterũ. Non reliquit hoĩem',
    'cta test̾i sur: u̾bi qd̾ madauit in mil egones. Q uod disposunt ad abrahã. mũti sui ad p̃saaci Et statuit il acob ĩ p̾ceptũ: ⁊ isrł mn testiñ etꝰ Dices tibi dabo t̾ram chanaan: fu ncdũ heditatis ur̃e. Dũ e̾e̾nt nũo ocui. paucissimi ⁊ ĩcole ouis. Et ꝑtͣni eẽt de gnͣte ĩ gentẽ: ⁊ de regno ad ulũ alterũ. Non reliquit hoĩem',
    'p̾mioꝵ. p̃s. b̾ildixit finis tuus inte. Et ĩminitas apee cato. Qua xp̃c donatus e̾ ps. p̾ucinsti eũ i bñ. dicidis ¶Infernans quo\uf1ac dupiex .s. adinacio. ps. laudat᷑ͣ ptc̃ce indesidus aĩe sue ⁊ ñquis bñdi. et cũ quis sibi tribuit bona que ht̃ atco. Iob. timebat enĩ ne forte peccau̾int fuii eius. ⁊ bñdix̾int deo incordib\uf1ac suis. Corꝑans ẽ ad carnis delecta tr̃em us. or̃s caro feñ. ⁊ oĩs gła euis qiͣ d̾r ꝑ ysaiam. ue qͥ niungitis domũ addom̃. ⁊ agr̃ ago copłatis us\uf1ac ad t̾minũ ioci. Nñquid ħ̾itabitis uos so',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 1.0000, 0.2812],
#         [1.0000, 1.0000, 0.2812],
#         [0.2812, 0.2812, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 99,840 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 6 tokens
mean: 85.65 tokens
max: 473 tokens
min: 6 tokens
mean: 85.65 tokens
max: 473 tokens

Samples:

sentence_0	sentence_1
`Per totum namque mundum est mundus; et mundum persequitur mundus, coinquinatus mundum, perditus redemptum, damnatus salvatum.`	`Per totum namque mundum est mundus; et mundum persequitur mundus, coinquinatus mundum, perditus redemptum, damnatus salvatum.`
`motꝰ siait supͣ sepe dixmꝰ gꝰ anteon nem generanonem est motus ge eti am aute generaitionem primi mobilis est mo tus go etiam motus est. inte p̾mum mo tum᷑ ꝙ est impossibile go fint hec caisa ꝙ motus non eet̾ sꝑ momĩ p̾tito iprẽ ꝙ primum mobile oportet᷑ prius generari mẽe et postea moneri qr absq dubio se queret᷑ ꝙ quedam mutatio eet̃ anteil`	`motꝰ siait supͣ sepe dixmꝰ gꝰ anteon nem generanonem est motus ge eti am aute generaitionem primi mobilis est mo tus go etiam motus est. inte p̾mum mo tum᷑ ꝙ est impossibile go fint hec caisa ꝙ motus non eet̾ sꝑ momĩ p̾tito iprẽ ꝙ primum mobile oportet᷑ prius generari mẽe et postea moneri qr absq dubio se queret᷑ ꝙ quedam mutatio eet̃ anteil`
Dictum est, id quod in nomine confuse significaretur, in definitione quae fit enumeratione partium, aperiri atque explicari. Quod fieri non potest, nisi per quarumdam partium nuncupationem; nihil enim dum explicatur oratione, totum simul dici potest. Quae cum ita sint, cumque omnis hujusmodi definitio quaedam sit partium distributio, quatuor his modis fieri potest. Aut enim substantiales partes explicantur, aut proprietatis partes dicuntur, aut quasi totius membra enumerantur, aut tanquam species dividuntur. Substantiales partes explicantur, cum ex genere ac differentiis definitio constituitur. Genus enim quod singulariter praedicatur, speciei totum est. Id genus sumptum in definitione, pars quaedam fit. Non enim solum speciem complet, nisi adjiciantur etiam differentiae, in quibus eadem ratio quae in genere est. Nam cum ipsae singulariter dictae totam speciem claudant, in definitione sumptae, partes speciei fiunt, quia non solum speciem quidem esse designant, sed etiam genus.	Dictum est, id quod in nomine confuse significaretur, in definitione quae fit enumeratione partium, aperiri atque explicari. Quod fieri non potest, nisi per quarumdam partium nuncupationem; nihil enim dum explicatur oratione, totum simul dici potest. Quae cum ita sint, cumque omnis hujusmodi definitio quaedam sit partium distributio, quatuor his modis fieri potest. Aut enim substantiales partes explicantur, aut proprietatis partes dicuntur, aut quasi totius membra enumerantur, aut tanquam species dividuntur. Substantiales partes explicantur, cum ex genere ac differentiis definitio constituitur. Genus enim quod singulariter praedicatur, speciei totum est. Id genus sumptum in definitione, pars quaedam fit. Non enim solum speciem complet, nisi adjiciantur etiam differentiae, in quibus eadem ratio quae in genere est. Nam cum ipsae singulariter dictae totam speciem claudant, in definitione sumptae, partes speciei fiunt, quia non solum speciem quidem esse designant, sed etiam genus.

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 128
per_device_eval_batch_size: 128
num_train_epochs: 1
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
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
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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
hub_revision: None
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
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.6410	500	0.1311

Framework Versions

Python: 3.12.11
Sentence Transformers: 5.1.0
Transformers: 4.56.0
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.0

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",
}

MultipleNegativesRankingLoss