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antonypamo
/
RRFSAVANTMADE

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:363
loss:CosineSimilarityLoss
text-embeddings-inference
Model card Files
xet
Community

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

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

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.

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
    G = nx.icosahedral_graph()
    n = G.number_of_nodes()    		 0.4647801650468898
    [
    i \hbar \frac{\partial \Psi}{\partial t} = H \Psi
    ]    		 \begin{align*}
    \alpha_{\rm fine} &\approx f(E_n, \text{geometría icosaédrica}) \
    m_\nu &\approx g(\text{acoplamientos SU(2)/SU(3) discretos}) \
    \Lambda &\approx h(\text{energía de vacío logarítmica})
    \end{align*}    		 0.4930957329947213
    \title{Resonance of Reality Framework (RRF) Extendido\
    Hamiltoniano Icosaédrico, Gravedad Logarítmica y Simulación}
    \author{Antony Padilla Morales}
    \date{\today}    		 donde:
    \begin{itemize}
    \item $\psi_i$ son espinores icosaédricos.
    \item $r_{ij}$ es la distancia entre nodos $i$ y $j$.
    \item $A,B,C$ son acoplamientos gauge discretos.
    \end{itemize}    		 0.5762137786115148
  • Loss: CosineSimilarityLoss with these parameters:
    {
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

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 

@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 }
}
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