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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:363
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: \section{5. Correspondencia con constantes físicas}
sentences:
- \section{7. Simulación Python – Malla Icosaédrica y Autovalores}
- \textbf{Interpretación:} Cada modo $n$ representa un posible estado de partícula,
resonancia o nodo cognitivo.
- rroc_V
- source_sentence: '\[
S_{\rm eff} = \int d^4x \left[ \frac{R}{16 \pi G} + \lambda \log(r) + \mathcal{L}_{\rm
matter} \right]
\]'
sentences:
- 'Sea $H$ la matriz discreta sobre la red icosaédrica. Los autovalores $\{E_n\}$
y vectores propios $\{\Psi_n\}$ cumplen:'
- '\begin{lstlisting}[language=Python]
import numpy as np
import networkx as nx
from scipy.linalg import eigh
import matplotlib.pyplot as plt'
- \section{6. Aplicaciones en resonancia y cognición}
- source_sentence: \section{6. Aplicaciones en resonancia y cognición}
sentences:
- '# Visualizar malla
pos = nx.spring_layout(G, seed=42)
nx.draw(G, pos, with_labels=True, node_color=''cyan'', edge_color=''gray'')
plt.show()
\end{lstlisting}'
- '\begin{lstlisting}[language=Python]
import numpy as np
import networkx as nx
from scipy.linalg import eigh
import matplotlib.pyplot as plt'
- \section{5. Correspondencia con constantes físicas}
- source_sentence: \textbf{Interpretación:} Cada modo $n$ representa un posible estado
de partícula, resonancia o nodo cognitivo.
sentences:
- '\begin{lstlisting}[language=Python]
import numpy as np
import networkx as nx
from scipy.linalg import eigh
import matplotlib.pyplot as plt'
- ln(phi)
- '# Visualizar malla
pos = nx.spring_layout(G, seed=42)
nx.draw(G, pos, with_labels=True, node_color=''cyan'', edge_color=''gray'')
plt.show()
\end{lstlisting}'
- source_sentence: "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}"
sentences:
- "\\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}"
- "\\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{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}"
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/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](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
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]])
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 363 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 363 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 16 tokens</li><li>mean: 65.29 tokens</li><li>max: 127 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 38.02 tokens</li><li>max: 127 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
| <code>Sea $H$ la matriz discreta sobre la red icosaédrica. Los autovalores $\{E_n\}$ y vectores propios $\{\Psi_n\}$ cumplen:</code> | <code># Crear grafo icosaédrico<br>G = nx.icosahedral_graph()<br>n = G.number_of_nodes()</code> | <code>0.4647801650468898</code> |
| <code>\[<br>i \hbar \frac{\partial \Psi}{\partial t} = H \Psi<br>\]</code> | <code>\begin{align*}<br>\alpha_{\rm fine} &\approx f(E_n, \text{geometría icosaédrica}) \\<br>m_\nu &\approx g(\text{acoplamientos SU(2)/SU(3) discretos}) \\<br>\Lambda &\approx h(\text{energía de vacío logarítmica})<br>\end{align*}</code> | <code>0.4930957329947213</code> |
| <code>\title{Resonance of Reality Framework (RRF) Extendido\\<br>Hamiltoniano Icosaédrico, Gravedad Logarítmica y Simulación}<br>\author{Antony Padilla Morales}<br>\date{\today}</code> | <code>donde:<br>\begin{itemize}<br> \item $\psi_i$ son espinores icosaédricos.<br> \item $r_{ij}$ es la distancia entre nodos $i$ y $j$.<br> \item $A,B,C$ son acoplamientos gauge discretos.<br>\end{itemize}</code> | <code>0.5762137786115148</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"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
<details><summary>Click to expand</summary>
- `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`: {}
</details>
### 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 }
}
```
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