This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'donde:\n\\begin{itemize}\n    \\item $\\psi_i$ son espinores icosaédricos.\n    \\item $r_{ij}$ es la distancia entre nodos $i$ y $j$.\n    \\item $A,B,C$ son acoplamientos gauge discretos.\n\\end{itemize}',
    '\\begin{itemize}\n    \\item Cada nodo $\\psi_i$ como \\textbf{átomo de experiencia}.\n    \\item Patrones icosaédricos y $\\phi$ guían frecuencia de resonancia.\n    \\item Protocolos musicales y visuales para plasticidad neuronal.\n\\end{itemize}',
    '\\begin{abstract}\nEsta versión extendida del \\textbf{Resonance of Reality Framework (RRF)} presenta:\n\\begin{itemize}\n    \\item Hamiltoniano discreto icosaédrico con modos normales.\n    \\item Corrección logarítmica gravitatoria y acoplamientos gauge explícitos.\n    \\item Correspondencia con constantes físicas fundamentales.\n    \\item Ejemplo de simulación Python que visualiza la malla icosaédrica y autovalores.\n\\end{itemize}\n\\end{abstract}',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7950, 0.7297],
#         [0.7950, 1.0000, 0.7343],
#         [0.7297, 0.7343, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 363 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 363 samples:
sentence_0 sentence_1 label
type string string float
details
min: 16 tokens
mean: 65.29 tokens
max: 127 tokens
min: 3 tokens
mean: 38.02 tokens
max: 127 tokens
min: 0.0
mean: 0.5
max: 1.0

Samples:

sentence_0	sentence_1	label
`Sea $H$ la matriz discreta sobre la red icosaédrica. Los autovalores ${E_n}$ y vectores propios ${\Psi_n}$ cumplen:`	`# Crear grafo icosaédrico[object Object]G = nx.icosahedral_graph()[object Object]n = G.number_of_nodes()`	`0.4647801650468898`
`[[object Object]i \hbar \frac{\partial \Psi}{\partial t} = H \Psi[object Object]]`	`\begin{align}[object Object]\alpha_{\rm fine} &\approx f(E_n, \text{geometría icosaédrica}) \[object Object]m_\nu &\approx g(\text{acoplamientos SU(2)/SU(3) discretos}) \[object Object]\Lambda &\approx h(\text{energía de vacío logarítmica})[object Object]\end{align}`	`0.4930957329947213`
`\title{Resonance of Reality Framework (RRF) Extendido\[object Object]Hamiltoniano Icosaédrico, Gravedad Logarítmica y Simulación}[object Object]\author{Antony Padilla Morales}[object Object]\date{\today}`	`donde:[object Object]\begin{itemize}[object Object] \item $\psi_i$ son espinores icosaédricos.[object Object] \item $r_{ij}$ es la distancia entre nodos $i$ y $j$.[object Object] \item $A,B,C$ son acoplamientos gauge discretos.[object Object]\end{itemize}`	`0.5762137786115148`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 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
num_train_epochs: 3
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
bf16: False
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}
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
project: huggingface
trackio_space_id: trackio
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: no
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: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.1.1
Transformers: 4.57.0
PyTorch: 2.8.0+cu126
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.1

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",
}

@misc{antony_padilla_morales_2025,
	author       = { Antony Padilla Morales },
	title        = { RRFSAVANTMADE (Revision 13af35f) },
	year         = 2025,
	url          = { https://huggingface.co/antonypamo/RRFSAVANTMADE },
	doi          = { 10.57967/hf/7034 },
	publisher    = { Hugging Face }
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 16 tokens mean: 65.29 tokens max: 127 tokens	min: 3 tokens mean: 38.02 tokens max: 127 tokens	min: 0.0 mean: 0.5 max: 1.0