Subida inicial del modelo RRF-SAVANT

model/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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
+}

model/README.md

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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]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## 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",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

model/config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model/config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.57.0",
+    "pytorch": "2.8.0+cu126"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model/model.safetensors

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+oid sha256:25cd3046cdf2dbbf3abb8fc8189b5a327fc504a8df99b1f9e4eca148b2b84473
+size 90864192

model/modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

model/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "do_lower_case": false
+}

model/special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

model/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}