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checkpoints/checkpoint-856000/1_Pooling/config.json

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+{
+    "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
+}

checkpoints/checkpoint-856000/README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:1175405
+- loss:CosineSimilarityLoss
+base_model: BSC-LT/MrBERT-es
+widget:
+- source_sentence: El camino de Santiago articula la península ibérica con Europa.
+  sentences:
+  - Y un millon de euros y de pesetas tampoco son lo mismo.
+  - Asimismo, en los montes puede haber matorral de coscoja y, también, lentisco,
+    romero, enebro o brezo.
+  - El país fue el noveno mayor importador de petróleo del mundo en 2013 .
+- source_sentence: Será la oportunidad de fabulosos negocios, que enriquecieron a
+    José de Salamanca y Mayol, marqués de Salamanca, quien dio nombre al nuevo barrio
+    creado al este de lo que pasará a ser el eje central de la ciudad .
+  sentences:
+  - Para terminar, como suelen hacer, el 'Free from desire', de Gala.
+  - Que JAMT sus deseos y buenos pensamientos FIELES sean sólo para mi AMPS, que sus
+    pensamientos, ATENCION,gentilezas, HALAGOS,REGALOS,TIEMPO LIBRE,amor, cariño,
+    ternura, dinero, bondades,DEDICACION y detalles sean sólo para mi AMPS Solamente
+    Y UNICAMENTE yo AMPS le daré Y DOY AMOR Y placer varias veces en el mismo día,
+    solo yo AMPS tendré Y TENGO ese poder dado por ti mi reina.
+  - Esperamos con anhelo poder saludarte personalmente en breve. 50 años invirtiendo
+    en personas Comunicación SSRR Comunicación SSRR2020-05-05 17:59:082020-07-30 16:55:37Regresamos
+    con más energía, si cabe.
+- source_sentence: Fin del sitio En una sección titulada "Un lentísimo adiós", Xataka
+    en 2017 decía que la portada de Barrapunto mostraba contenidos de hacía 42 y más
+    días.
+  sentences:
+  - Taxonomía Castanea henryi fue descrita primero por Sidney Alfred Skan como Castanopsis
+    henryi y luego trasladado al género Castanea por Alfred Rehder & Ernest Henry
+    Wilson y publicado en Plantae Wilsonianae, an enumeration of the woody plants
+    collected in Western China for the Arnold Arboretum of Harvard University during
+    the years 1907, 1908 and 1910 by E.H.
+  - Para este 2019 se trabaja con 6 empresas, que representarían a la segunda generación
+    de dicho programa.
+  - Ya no está uno para estos trotes.
+- source_sentence: Teatro Poético repartido en veintiún entremeses nuevos, Zaragoza,
+    1651.
+  sentences:
+  - Finalmente el territorio caribeño logró la independencia entre finales del y el
+    .
+  - No es considerada fiable.
+  - La página se generó a las 19:58:53.
+- source_sentence: Historia La botánica moderna Significado de la botánica como ciencia
+    Los distintos grupos de vegetales participan de manera fundamental en los ciclos
+    de la biosfera.
+  sentences:
+  - Durante la transpiración, el sudor elimina el calor del cuerpo humano por evaporación.
+  - El COPINH exige a las autoridades judiciales y fiscales proceder judicialmente
+    contra los alcaldes municipales, altos funcionarios de SERNA, y contra las empresas
+    y demás sectores involucrados en esta agresión contra el pueblo lenca.
+  - A nivel global, el artículo13 del Pacto Internacional de Derechos Económicos,
+    Sociales y Culturales de 1966 de las Naciones Unidas reconoce el derecho de toda
+    persona a la educación.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on BSC-LT/MrBERT-es
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts eval
+      type: sts_eval
+    metrics:
+    - type: pearson_cosine
+      value: 0.43442931591911665
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.2596907649612308
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on BSC-LT/MrBERT-es
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BSC-LT/MrBERT-es](https://huggingface.co/BSC-LT/MrBERT-es). 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
+- **Base model:** [BSC-LT/MrBERT-es](https://huggingface.co/BSC-LT/MrBERT-es) <!-- at revision cfc9d049c3dee345ec55fa69e689c75e8af3c094 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 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/huggingface/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': 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})
+  (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 = [
+    'Historia La botánica moderna Significado de la botánica como ciencia Los distintos grupos de vegetales participan de manera fundamental en los ciclos de la biosfera.',
+    'El COPINH exige a las autoridades judiciales y fiscales proceder judicialmente contra los alcaldes municipales, altos funcionarios de SERNA, y contra las empresas y demás sectores involucrados en esta agresión contra el pueblo lenca.',
+    'Durante la transpiración, el sudor elimina el calor del cuerpo humano por evaporación.',
+]
+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,  0.2274,  0.0939],
+#         [ 0.2274,  1.0000, -0.1173],
+#         [ 0.0939, -0.1173,  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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+
+* Dataset: `sts_eval`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.4344     |
+| **spearman_cosine** | **0.2597** |
+
+<!--
+## 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: 1,175,405 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                         | label                                                            |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | float                                                            |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 37.17 tokens</li><li>max: 290 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 38.26 tokens</li><li>max: 375 tokens</li></ul> | <ul><li>min: -0.75</li><li>mean: 0.17</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                  | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | label                            |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>Los ahorros de la jubilación podrán usarse para este fin.</code>                                                                                                                                      | <code>Sony Ericsson W8 además de todo eso presenta una pantalla táctil de tipo HVGA de 320 x 480 píxeles y la pantalla posee 16.777.216 colores.</code>                                                                                                                                                                                                                                                                                                                                                                                    | <code>0.2533760964870453</code>  |
+  | <code>Programas de desarrollo en el cerebelo La transición célula progenitora a neurona madura, implica una serie de cambios morfológicos y moleculares altamente regulada espacial y temporalmente.</code> | <code>Dos ejemplos en los que el principio de exclusión relaciona la materia con la ocupación del espacio son las estrellas enanas blancas y las estrellas de neutrones, que se analizan más adelante.</code>                                                                                                                                                                                                                                                                                                                              | <code>0.1902337223291397</code>  |
+  | <code>Bolsa inmobiliaria online en Distrito Federal df, inmuebles en venta y renta, casas, departamentos, locales, terrenos, inmobiliarias, desarrollos, anunciar inmuebles.</code>                         | <code>Otros prefieren hablar de "régimen" o "sistema feudal", para diferenciarlo sutilmente del feudalismo estricto, o de síntesis feudal, para marcar el hecho de que sobreviven en ella rasgos de la antigüedad clásica mezclados con contribuciones germánicas, implicando tanto a instituciones como a elementos productivos, y significó la especificidad del feudalismo europeo occidental como formación económico social frente a otras también feudales, con consecuencias trascendentales en el futuro devenir histórico.</code> | <code>0.21721388399600983</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
+
+- `eval_strategy`: steps
+- `max_grad_norm`: 2.0
+- `num_train_epochs`: 10
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 8
+- `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`: 2.0
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `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
+- `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>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step   | Training Loss | sts_eval_spearman_cosine |
+|:------:|:------:|:-------------:|:------------------------:|
+| 3.9714 | 583500 | 0.0253        | 0.2725                   |
+| 3.9748 | 584000 | 0.0274        | 0.2733                   |
+| 3.9782 | 584500 | 0.0279        | 0.2711                   |
+| 3.9816 | 585000 | 0.0248        | 0.2708                   |
+| 3.9850 | 585500 | 0.0264        | 0.2676                   |
+| 3.9884 | 586000 | 0.0267        | 0.2713                   |
+| 3.9918 | 586500 | 0.0276        | 0.2703                   |
+| 3.9952 | 587000 | 0.0273        | 0.2674                   |
+| 3.9986 | 587500 | 0.0278        | 0.2688                   |
+| 4.0    | 587704 | -             | 0.2672                   |
+| 4.0020 | 588000 | 0.0259        | 0.2675                   |
+| 4.0054 | 588500 | 0.0257        | 0.2697                   |
+| 4.0088 | 589000 | 0.0268        | 0.2694                   |
+| 4.0122 | 589500 | 0.0256        | 0.2706                   |
+| 4.0156 | 590000 | 0.0254        | 0.2706                   |
+| 4.0190 | 590500 | 0.0263        | 0.2695                   |
+| 4.0224 | 591000 | 0.0274        | 0.2691                   |
+| 4.0258 | 591500 | 0.0255        | 0.2712                   |
+| 4.0292 | 592000 | 0.0253        | 0.2696                   |
+| 4.0326 | 592500 | 0.025         | 0.2692                   |
+| 4.0360 | 593000 | 0.0263        | 0.2679                   |
+| 4.0394 | 593500 | 0.028         | 0.2689                   |
+| 4.0429 | 594000 | 0.0275        | 0.2696                   |
+| 4.0463 | 594500 | 0.0268        | 0.2699                   |
+| 4.0497 | 595000 | 0.025         | 0.2686                   |
+| 4.0531 | 595500 | 0.0277        | 0.2683                   |
+| 4.0565 | 596000 | 0.0276        | 0.2690                   |
+| 4.0599 | 596500 | 0.0242        | 0.2686                   |
+| 4.0633 | 597000 | 0.0264        | 0.2691                   |
+| 4.0667 | 597500 | 0.0273        | 0.2681                   |
+| 4.0701 | 598000 | 0.0269        | 0.2693                   |
+| 4.0735 | 598500 | 0.0274        | 0.2698                   |
+| 4.0769 | 599000 | 0.0252        | 0.2704                   |
+| 4.0803 | 599500 | 0.0268        | 0.2708                   |
+| 4.0837 | 600000 | 0.0259        | 0.2696                   |
+| 4.0871 | 600500 | 0.0277        | 0.2689                   |
+| 4.0905 | 601000 | 0.0262        | 0.2663                   |
+| 4.0939 | 601500 | 0.0266        | 0.2697                   |
+| 4.0973 | 602000 | 0.0269        | 0.2700                   |
+| 4.1007 | 602500 | 0.0253        | 0.2673                   |
+| 4.1041 | 603000 | 0.0281        | 0.2684                   |
+| 4.1075 | 603500 | 0.0263        | 0.2687                   |
+| 4.1109 | 604000 | 0.028         | 0.2677                   |
+| 4.1143 | 604500 | 0.0277        | 0.2701                   |
+| 4.1177 | 605000 | 0.0273        | 0.2686                   |
+| 4.1211 | 605500 | 0.0253        | 0.2681                   |
+| 4.1245 | 606000 | 0.0264        | 0.2694                   |
+| 4.1279 | 606500 | 0.0281        | 0.2706                   |
+| 4.1313 | 607000 | 0.0262        | 0.2714                   |
+| 4.1347 | 607500 | 0.0265        | 0.2673                   |
+| 4.1381 | 608000 | 0.0254        | 0.2685                   |
+| 4.1415 | 608500 | 0.0279        | 0.2674                   |
+| 4.1449 | 609000 | 0.0284        | 0.2692                   |
+| 4.1483 | 609500 | 0.0283        | 0.2680                   |
+| 4.1517 | 610000 | 0.0277        | 0.2673                   |
+| 4.1552 | 610500 | 0.0264        | 0.2692                   |
+| 4.1586 | 611000 | 0.0261        | 0.2687                   |
+| 4.1620 | 611500 | 0.0273        | 0.2697                   |
+| 4.1654 | 612000 | 0.027         | 0.2697                   |
+| 4.1688 | 612500 | 0.0274        | 0.2696                   |
+| 4.1722 | 613000 | 0.0273        | 0.2698                   |
+| 4.1756 | 613500 | 0.0255        | 0.2659                   |
+| 4.1790 | 614000 | 0.0274        | 0.2660                   |
+| 4.1824 | 614500 | 0.0284        | 0.2666                   |
+| 4.1858 | 615000 | 0.0268        | 0.2680                   |
+| 4.1892 | 615500 | 0.0278        | 0.2674                   |
+| 4.1926 | 616000 | 0.0276        | 0.2684                   |
+| 4.1960 | 616500 | 0.026         | 0.2700                   |
+| 4.1994 | 617000 | 0.0266        | 0.2686                   |
+| 4.2028 | 617500 | 0.0266        | 0.2680                   |
+| 4.2062 | 618000 | 0.0277        | 0.2678                   |
+| 4.2096 | 618500 | 0.0291        | 0.2649                   |
+| 4.2130 | 619000 | 0.0281        | 0.2635                   |
+| 4.2164 | 619500 | 0.0291        | 0.2659                   |
+| 4.2198 | 620000 | 0.0281        | 0.2672                   |
+| 4.2232 | 620500 | 0.0282        | 0.2655                   |
+| 4.2266 | 621000 | 0.0287        | 0.2648                   |
+| 4.2300 | 621500 | 0.0285        | 0.2640                   |
+| 4.2334 | 622000 | 0.0282        | 0.2645                   |
+| 4.2368 | 622500 | 0.027         | 0.2674                   |
+| 4.2402 | 623000 | 0.0268        | 0.2669                   |
+| 4.2436 | 623500 | 0.0291        | 0.2663                   |
+| 4.2470 | 624000 | 0.0291        | 0.2645                   |
+| 4.2504 | 624500 | 0.0277        | 0.2677                   |
+| 4.2538 | 625000 | 0.0273        | 0.2631                   |
+| 4.2572 | 625500 | 0.0265        | 0.2653                   |
+| 4.2606 | 626000 | 0.0276        | 0.2665                   |
+| 4.2641 | 626500 | 0.027         | 0.2654                   |
+| 4.2675 | 627000 | 0.0271        | 0.2659                   |
+| 4.2709 | 627500 | 0.0279        | 0.2659                   |
+| 4.2743 | 628000 | 0.0274        | 0.2648                   |
+| 4.2777 | 628500 | 0.0263        | 0.2659                   |
+| 4.2811 | 629000 | 0.0279        | 0.2665                   |
+| 4.2845 | 629500 | 0.028         | 0.2677                   |
+| 4.2879 | 630000 | 0.0299        | 0.2701                   |
+| 4.2913 | 630500 | 0.0284        | 0.2688                   |
+| 4.2947 | 631000 | 0.0269        | 0.2683                   |
+| 4.2981 | 631500 | 0.0271        | 0.2689                   |
+| 4.3015 | 632000 | 0.0288        | 0.2680                   |
+| 4.3049 | 632500 | 0.0274        | 0.2674                   |
+| 4.3083 | 633000 | 0.0277        | 0.2675                   |
+| 4.3117 | 633500 | 0.0282        | 0.2671                   |
+| 4.3151 | 634000 | 0.0266        | 0.2658                   |
+| 4.3185 | 634500 | 0.0284        | 0.2648                   |
+| 4.3219 | 635000 | 0.0283        | 0.2637                   |
+| 4.3253 | 635500 | 0.0283        | 0.2647                   |
+| 4.3287 | 636000 | 0.0281        | 0.2641                   |
+| 4.3321 | 636500 | 0.0275        | 0.2620                   |
+| 4.3355 | 637000 | 0.0272        | 0.2630                   |
+| 4.3389 | 637500 | 0.0282        | 0.2642                   |
+| 4.3423 | 638000 | 0.0294        | 0.2664                   |
+| 4.3457 | 638500 | 0.0283        | 0.2639                   |
+| 4.3491 | 639000 | 0.0262        | 0.2663                   |
+| 4.3525 | 639500 | 0.0275        | 0.2671                   |
+| 4.3559 | 640000 | 0.0298        | 0.2669                   |
+| 4.3593 | 640500 | 0.0292        | 0.2693                   |
+| 4.3627 | 641000 | 0.0283        | 0.2673                   |
+| 4.3661 | 641500 | 0.027         | 0.2687                   |
+| 4.3695 | 642000 | 0.0278        | 0.2663                   |
+| 4.3729 | 642500 | 0.0301        | 0.2652                   |
+| 4.3764 | 643000 | 0.0275        | 0.2676                   |
+| 4.3798 | 643500 | 0.0292        | 0.2680                   |
+| 4.3832 | 644000 | 0.0266        | 0.2680                   |
+| 4.3866 | 644500 | 0.0283        | 0.2668                   |
+| 4.3900 | 645000 | 0.0303        | 0.2677                   |
+| 4.3934 | 645500 | 0.0299        | 0.2701                   |
+| 4.3968 | 646000 | 0.0284        | 0.2680                   |
+| 4.4002 | 646500 | 0.0272        | 0.2664                   |
+| 4.4036 | 647000 | 0.0297        | 0.2662                   |
+| 4.4070 | 647500 | 0.029         | 0.2661                   |
+| 4.4104 | 648000 | 0.0281        | 0.2678                   |
+| 4.4138 | 648500 | 0.0282        | 0.2683                   |
+| 4.4172 | 649000 | 0.0278        | 0.2699                   |
+| 4.4206 | 649500 | 0.0309        | 0.2684                   |
+| 4.4240 | 650000 | 0.0288        | 0.2693                   |
+| 4.4274 | 650500 | 0.0307        | 0.2697                   |
+| 4.4308 | 651000 | 0.0272        | 0.2722                   |
+| 4.4342 | 651500 | 0.0289        | 0.2726                   |
+| 4.4376 | 652000 | 0.0288        | 0.2716                   |
+| 4.4410 | 652500 | 0.0289        | 0.2729                   |
+| 4.4444 | 653000 | 0.0297        | 0.2699                   |
+| 4.4478 | 653500 | 0.0286        | 0.2724                   |
+| 4.4512 | 654000 | 0.0298        | 0.2702                   |
+| 4.4546 | 654500 | 0.0302        | 0.2738                   |
+| 4.4580 | 655000 | 0.0292        | 0.2713                   |
+| 4.4614 | 655500 | 0.0297        | 0.2712                   |
+| 4.4648 | 656000 | 0.0286        | 0.2705                   |
+| 4.4682 | 656500 | 0.0285        | 0.2735                   |
+| 4.4716 | 657000 | 0.0294        | 0.2733                   |
+| 4.4750 | 657500 | 0.0291        | 0.2722                   |
+| 4.4784 | 658000 | 0.0283        | 0.2708                   |
+| 4.4818 | 658500 | 0.028         | 0.2714                   |
+| 4.4853 | 659000 | 0.0298        | 0.2716                   |
+| 4.4887 | 659500 | 0.0275        | 0.2721                   |
+| 4.4921 | 660000 | 0.0314        | 0.2731                   |
+| 4.4955 | 660500 | 0.0292        | 0.2730                   |
+| 4.4989 | 661000 | 0.029         | 0.2749                   |
+| 4.5023 | 661500 | 0.0305        | 0.2728                   |
+| 4.5057 | 662000 | 0.0323        | 0.2709                   |
+| 4.5091 | 662500 | 0.0276        | 0.2715                   |
+| 4.5125 | 663000 | 0.0294        | 0.2702                   |
+| 4.5159 | 663500 | 0.0286        | 0.2694                   |
+| 4.5193 | 664000 | 0.0282        | 0.2702                   |
+| 4.5227 | 664500 | 0.0287        | 0.2702                   |
+| 4.5261 | 665000 | 0.0289        | 0.2682                   |
+| 4.5295 | 665500 | 0.0299        | 0.2701                   |
+| 4.5329 | 666000 | 0.0301        | 0.2706                   |
+| 4.5363 | 666500 | 0.0287        | 0.2719                   |
+| 4.5397 | 667000 | 0.0292        | 0.2721                   |
+| 4.5431 | 667500 | 0.0284        | 0.2714                   |
+| 4.5465 | 668000 | 0.0286        | 0.2696                   |
+| 4.5499 | 668500 | 0.0299        | 0.2700                   |
+| 4.5533 | 669000 | 0.0282        | 0.2689                   |
+| 4.5567 | 669500 | 0.0288        | 0.2715                   |
+| 4.5601 | 670000 | 0.0298        | 0.2712                   |
+| 4.5635 | 670500 | 0.0302        | 0.2687                   |
+| 4.5669 | 671000 | 0.0298        | 0.2709                   |
+| 4.5703 | 671500 | 0.0297        | 0.2711                   |
+| 4.5737 | 672000 | 0.0297        | 0.2703                   |
+| 4.5771 | 672500 | 0.0288        | 0.2685                   |
+| 4.5805 | 673000 | 0.0293        | 0.2698                   |
+| 4.5839 | 673500 | 0.0293        | 0.2706                   |
+| 4.5873 | 674000 | 0.0292        | 0.2688                   |
+| 4.5907 | 674500 | 0.0288        | 0.2676                   |
+| 4.5941 | 675000 | 0.0294        | 0.2694                   |
+| 4.5976 | 675500 | 0.0308        | 0.2697                   |
+| 4.6010 | 676000 | 0.0297        | 0.2689                   |
+| 4.6044 | 676500 | 0.0287        | 0.2688                   |
+| 4.6078 | 677000 | 0.0276        | 0.2677                   |
+| 4.6112 | 677500 | 0.0307        | 0.2686                   |
+| 4.6146 | 678000 | 0.0301        | 0.2672                   |
+| 4.6180 | 678500 | 0.029         | 0.2689                   |
+| 4.6214 | 679000 | 0.0306        | 0.2683                   |
+| 4.6248 | 679500 | 0.0284        | 0.2689                   |
+| 4.6282 | 680000 | 0.0277        | 0.2698                   |
+| 4.6316 | 680500 | 0.0291        | 0.2694                   |
+| 4.6350 | 681000 | 0.0295        | 0.2660                   |
+| 4.6384 | 681500 | 0.0309        | 0.2683                   |
+| 4.6418 | 682000 | 0.0278        | 0.2703                   |
+| 4.6452 | 682500 | 0.0291        | 0.2690                   |
+| 4.6486 | 683000 | 0.0296        | 0.2699                   |
+| 4.6520 | 683500 | 0.0307        | 0.2689                   |
+| 4.6554 | 684000 | 0.0299        | 0.2679                   |
+| 4.6588 | 684500 | 0.03          | 0.2690                   |
+| 4.6622 | 685000 | 0.0291        | 0.2682                   |
+| 4.6656 | 685500 | 0.0304        | 0.2665                   |
+| 4.6690 | 686000 | 0.031         | 0.2657                   |
+| 4.6724 | 686500 | 0.03          | 0.2674                   |
+| 4.6758 | 687000 | 0.0293        | 0.2696                   |
+| 4.6792 | 687500 | 0.0299        | 0.2666                   |
+| 4.6826 | 688000 | 0.029         | 0.2668                   |
+| 4.6860 | 688500 | 0.0295        | 0.2669                   |
+| 4.6894 | 689000 | 0.0288        | 0.2680                   |
+| 4.6928 | 689500 | 0.0301        | 0.2674                   |
+| 4.6962 | 690000 | 0.03          | 0.2690                   |
+| 4.6996 | 690500 | 0.0298        | 0.2678                   |
+| 4.7030 | 691000 | 0.03          | 0.2705                   |
+| 4.7065 | 691500 | 0.0293        | 0.2692                   |
+| 4.7099 | 692000 | 0.0287        | 0.2693                   |
+| 4.7133 | 692500 | 0.0304        | 0.2660                   |
+| 4.7167 | 693000 | 0.0296        | 0.2662                   |
+| 4.7201 | 693500 | 0.0291        | 0.2668                   |
+| 4.7235 | 694000 | 0.0308        | 0.2677                   |
+| 4.7269 | 694500 | 0.0309        | 0.2668                   |
+| 4.7303 | 695000 | 0.0319        | 0.2692                   |
+| 4.7337 | 695500 | 0.0297        | 0.2678                   |
+| 4.7371 | 696000 | 0.0297        | 0.2672                   |
+| 4.7405 | 696500 | 0.0294        | 0.2673                   |
+| 4.7439 | 697000 | 0.0293        | 0.2671                   |
+| 4.7473 | 697500 | 0.0308        | 0.2687                   |
+| 4.7507 | 698000 | 0.0315        | 0.2694                   |
+| 4.7541 | 698500 | 0.0286        | 0.2676                   |
+| 4.7575 | 699000 | 0.0297        | 0.2687                   |
+| 4.7609 | 699500 | 0.0285        | 0.2668                   |
+| 4.7643 | 700000 | 0.0282        | 0.2682                   |
+| 4.7677 | 700500 | 0.0307        | 0.2667                   |
+| 4.7711 | 701000 | 0.0276        | 0.2719                   |
+| 4.7745 | 701500 | 0.0297        | 0.2706                   |
+| 4.7779 | 702000 | 0.0293        | 0.2691                   |
+| 4.7813 | 702500 | 0.029         | 0.2679                   |
+| 4.7847 | 703000 | 0.0319        | 0.2678                   |
+| 4.7881 | 703500 | 0.0303        | 0.2682                   |
+| 4.7915 | 704000 | 0.028         | 0.2688                   |
+| 4.7949 | 704500 | 0.031         | 0.2719                   |
+| 4.7983 | 705000 | 0.029         | 0.2692                   |
+| 4.8017 | 705500 | 0.0313        | 0.2661                   |
+| 4.8051 | 706000 | 0.0313        | 0.2685                   |
+| 4.8085 | 706500 | 0.0296        | 0.2689                   |
+| 4.8119 | 707000 | 0.0309        | 0.2705                   |
+| 4.8153 | 707500 | 0.0287        | 0.2691                   |
+| 4.8188 | 708000 | 0.031         | 0.2697                   |
+| 4.8222 | 708500 | 0.0295        | 0.2683                   |
+| 4.8256 | 709000 | 0.0293        | 0.2687                   |
+| 4.8290 | 709500 | 0.0316        | 0.2689                   |
+| 4.8324 | 710000 | 0.0289        | 0.2691                   |
+| 4.8358 | 710500 | 0.0287        | 0.2705                   |
+| 4.8392 | 711000 | 0.0292        | 0.2700                   |
+| 4.8426 | 711500 | 0.0309        | 0.2682                   |
+| 4.8460 | 712000 | 0.0306        | 0.2688                   |
+| 4.8494 | 712500 | 0.0304        | 0.2701                   |
+| 4.8528 | 713000 | 0.03          | 0.2679                   |
+| 4.8562 | 713500 | 0.0293        | 0.2713                   |
+| 4.8596 | 714000 | 0.03          | 0.2692                   |
+| 4.8630 | 714500 | 0.03          | 0.2700                   |
+| 4.8664 | 715000 | 0.0297        | 0.2699                   |
+| 4.8698 | 715500 | 0.0282        | 0.2709                   |
+| 4.8732 | 716000 | 0.0287        | 0.2715                   |
+| 4.8766 | 716500 | 0.0303        | 0.2718                   |
+| 4.8800 | 717000 | 0.0304        | 0.2710                   |
+| 4.8834 | 717500 | 0.0292        | 0.2720                   |
+| 4.8868 | 718000 | 0.0307        | 0.2700                   |
+| 4.8902 | 718500 | 0.0304        | 0.2698                   |
+| 4.8936 | 719000 | 0.0307        | 0.2681                   |
+| 4.8970 | 719500 | 0.0294        | 0.2693                   |
+| 4.9004 | 720000 | 0.0315        | 0.2701                   |
+| 4.9038 | 720500 | 0.0288        | 0.2702                   |
+| 4.9072 | 721000 | 0.0284        | 0.2710                   |
+| 4.9106 | 721500 | 0.0309        | 0.2697                   |
+| 4.9140 | 722000 | 0.0313        | 0.2698                   |
+| 4.9174 | 722500 | 0.0305        | 0.2687                   |
+| 4.9208 | 723000 | 0.0306        | 0.2681                   |
+| 4.9242 | 723500 | 0.0307        | 0.2702                   |
+| 4.9277 | 724000 | 0.0319        | 0.2687                   |
+| 4.9311 | 724500 | 0.0285        | 0.2698                   |
+| 4.9345 | 725000 | 0.0298        | 0.2697                   |
+| 4.9379 | 725500 | 0.0317        | 0.2701                   |
+| 4.9413 | 726000 | 0.0316        | 0.2702                   |
+| 4.9447 | 726500 | 0.0305        | 0.2691                   |
+| 4.9481 | 727000 | 0.0303        | 0.2694                   |
+| 4.9515 | 727500 | 0.0302        | 0.2688                   |
+| 4.9549 | 728000 | 0.029         | 0.2672                   |
+| 4.9583 | 728500 | 0.03          | 0.2690                   |
+| 4.9617 | 729000 | 0.0291        | 0.2687                   |
+| 4.9651 | 729500 | 0.0301        | 0.2682                   |
+| 4.9685 | 730000 | 0.0304        | 0.2680                   |
+| 4.9719 | 730500 | 0.0305        | 0.2655                   |
+| 4.9753 | 731000 | 0.0285        | 0.2668                   |
+| 4.9787 | 731500 | 0.0325        | 0.2672                   |
+| 4.9821 | 732000 | 0.0294        | 0.2677                   |
+| 4.9855 | 732500 | 0.0308        | 0.2648                   |
+| 4.9889 | 733000 | 0.0291        | 0.2672                   |
+| 4.9923 | 733500 | 0.0312        | 0.2663                   |
+| 4.9957 | 734000 | 0.0305        | 0.2671                   |
+| 4.9991 | 734500 | 0.0301        | 0.2677                   |
+| 5.0    | 734630 | -             | 0.2660                   |
+| 5.0025 | 735000 | 0.0214        | 0.2636                   |
+| 5.0059 | 735500 | 0.0186        | 0.2625                   |
+| 5.0093 | 736000 | 0.0186        | 0.2608                   |
+| 5.0127 | 736500 | 0.0189        | 0.2612                   |
+| 5.0161 | 737000 | 0.019         | 0.2589                   |
+| 5.0195 | 737500 | 0.0185        | 0.2594                   |
+| 5.0229 | 738000 | 0.0177        | 0.2604                   |
+| 5.0263 | 738500 | 0.0187        | 0.2595                   |
+| 5.0297 | 739000 | 0.0185        | 0.2569                   |
+| 5.0331 | 739500 | 0.0174        | 0.2569                   |
+| 5.0365 | 740000 | 0.0185        | 0.2588                   |
+| 5.0400 | 740500 | 0.0186        | 0.2554                   |
+| 5.0434 | 741000 | 0.0176        | 0.2574                   |
+| 5.0468 | 741500 | 0.0173        | 0.2581                   |
+| 5.0502 | 742000 | 0.0182        | 0.2591                   |
+| 5.0536 | 742500 | 0.0175        | 0.2585                   |
+| 5.0570 | 743000 | 0.0173        | 0.2589                   |
+| 5.0604 | 743500 | 0.0175        | 0.2589                   |
+| 5.0638 | 744000 | 0.0184        | 0.2612                   |
+| 5.0672 | 744500 | 0.019         | 0.2595                   |
+| 5.0706 | 745000 | 0.0183        | 0.2588                   |
+| 5.0740 | 745500 | 0.0187        | 0.2553                   |
+| 5.0774 | 746000 | 0.0183        | 0.2553                   |
+| 5.0808 | 746500 | 0.0178        | 0.2560                   |
+| 5.0842 | 747000 | 0.0194        | 0.2566                   |
+| 5.0876 | 747500 | 0.0187        | 0.2572                   |
+| 5.0910 | 748000 | 0.0188        | 0.2534                   |
+| 5.0944 | 748500 | 0.0195        | 0.2556                   |
+| 5.0978 | 749000 | 0.0187        | 0.2579                   |
+| 5.1012 | 749500 | 0.0182        | 0.2558                   |
+| 5.1046 | 750000 | 0.0188        | 0.2554                   |
+| 5.1080 | 750500 | 0.019         | 0.2566                   |
+| 5.1114 | 751000 | 0.0182        | 0.2538                   |
+| 5.1148 | 751500 | 0.0185        | 0.2537                   |
+| 5.1182 | 752000 | 0.0183        | 0.2559                   |
+| 5.1216 | 752500 | 0.0185        | 0.2567                   |
+| 5.1250 | 753000 | 0.0186        | 0.2551                   |
+| 5.1284 | 753500 | 0.0186        | 0.2574                   |
+| 5.1318 | 754000 | 0.0187        | 0.2559                   |
+| 5.1352 | 754500 | 0.019         | 0.2566                   |
+| 5.1386 | 755000 | 0.0179        | 0.2561                   |
+| 5.1420 | 755500 | 0.0186        | 0.2556                   |
+| 5.1454 | 756000 | 0.0186        | 0.2545                   |
+| 5.1489 | 756500 | 0.0198        | 0.2526                   |
+| 5.1523 | 757000 | 0.0195        | 0.2556                   |
+| 5.1557 | 757500 | 0.0189        | 0.2519                   |
+| 5.1591 | 758000 | 0.0186        | 0.2547                   |
+| 5.1625 | 758500 | 0.0186        | 0.2536                   |
+| 5.1659 | 759000 | 0.0186        | 0.2548                   |
+| 5.1693 | 759500 | 0.0198        | 0.2537                   |
+| 5.1727 | 760000 | 0.0179        | 0.2557                   |
+| 5.1761 | 760500 | 0.0183        | 0.2540                   |
+| 5.1795 | 761000 | 0.0192        | 0.2558                   |
+| 5.1829 | 761500 | 0.0199        | 0.2575                   |
+| 5.1863 | 762000 | 0.0197        | 0.2555                   |
+| 5.1897 | 762500 | 0.0187        | 0.2579                   |
+| 5.1931 | 763000 | 0.0191        | 0.2577                   |
+| 5.1965 | 763500 | 0.0192        | 0.2572                   |
+| 5.1999 | 764000 | 0.0187        | 0.2565                   |
+| 5.2033 | 764500 | 0.018         | 0.2565                   |
+| 5.2067 | 765000 | 0.0188        | 0.2552                   |
+| 5.2101 | 765500 | 0.0193        | 0.2568                   |
+| 5.2135 | 766000 | 0.0187        | 0.2574                   |
+| 5.2169 | 766500 | 0.0181        | 0.2577                   |
+| 5.2203 | 767000 | 0.0197        | 0.2595                   |
+| 5.2237 | 767500 | 0.019         | 0.2599                   |
+| 5.2271 | 768000 | 0.0196        | 0.2587                   |
+| 5.2305 | 768500 | 0.0196        | 0.2584                   |
+| 5.2339 | 769000 | 0.0186        | 0.2570                   |
+| 5.2373 | 769500 | 0.0193        | 0.2593                   |
+| 5.2407 | 770000 | 0.0198        | 0.2595                   |
+| 5.2441 | 770500 | 0.019         | 0.2561                   |
+| 5.2475 | 771000 | 0.0198        | 0.2584                   |
+| 5.2509 | 771500 | 0.0195        | 0.2584                   |
+| 5.2543 | 772000 | 0.0201        | 0.2579                   |
+| 5.2577 | 772500 | 0.02          | 0.2582                   |
+| 5.2612 | 773000 | 0.0194        | 0.2576                   |
+| 5.2646 | 773500 | 0.0194        | 0.2585                   |
+| 5.2680 | 774000 | 0.0192        | 0.2574                   |
+| 5.2714 | 774500 | 0.019         | 0.2559                   |
+| 5.2748 | 775000 | 0.0197        | 0.2556                   |
+| 5.2782 | 775500 | 0.0191        | 0.2553                   |
+| 5.2816 | 776000 | 0.0205        | 0.2577                   |
+| 5.2850 | 776500 | 0.0195        | 0.2572                   |
+| 5.2884 | 777000 | 0.0207        | 0.2566                   |
+| 5.2918 | 777500 | 0.0206        | 0.2571                   |
+| 5.2952 | 778000 | 0.0202        | 0.2580                   |
+| 5.2986 | 778500 | 0.0192        | 0.2570                   |
+| 5.3020 | 779000 | 0.0191        | 0.2558                   |
+| 5.3054 | 779500 | 0.0213        | 0.2570                   |
+| 5.3088 | 780000 | 0.0193        | 0.2578                   |
+| 5.3122 | 780500 | 0.0193        | 0.2567                   |
+| 5.3156 | 781000 | 0.0212        | 0.2579                   |
+| 5.3190 | 781500 | 0.0197        | 0.2563                   |
+| 5.3224 | 782000 | 0.0204        | 0.2592                   |
+| 5.3258 | 782500 | 0.0207        | 0.2596                   |
+| 5.3292 | 783000 | 0.0197        | 0.2570                   |
+| 5.3326 | 783500 | 0.0201        | 0.2590                   |
+| 5.3360 | 784000 | 0.0204        | 0.2570                   |
+| 5.3394 | 784500 | 0.0198        | 0.2586                   |
+| 5.3428 | 785000 | 0.0193        | 0.2597                   |
+| 5.3462 | 785500 | 0.0197        | 0.2594                   |
+| 5.3496 | 786000 | 0.0205        | 0.2595                   |
+| 5.3530 | 786500 | 0.0194        | 0.2603                   |
+| 5.3564 | 787000 | 0.0205        | 0.2593                   |
+| 5.3598 | 787500 | 0.0205        | 0.2586                   |
+| 5.3632 | 788000 | 0.0203        | 0.2583                   |
+| 5.3666 | 788500 | 0.0194        | 0.2610                   |
+| 5.3701 | 789000 | 0.0206        | 0.2626                   |
+| 5.3735 | 789500 | 0.0198        | 0.2602                   |
+| 5.3769 | 790000 | 0.0208        | 0.2597                   |
+| 5.3803 | 790500 | 0.0201        | 0.2578                   |
+| 5.3837 | 791000 | 0.0205        | 0.2578                   |
+| 5.3871 | 791500 | 0.0197        | 0.2569                   |
+| 5.3905 | 792000 | 0.0204        | 0.2546                   |
+| 5.3939 | 792500 | 0.02          | 0.2565                   |
+| 5.3973 | 793000 | 0.0202        | 0.2574                   |
+| 5.4007 | 793500 | 0.0198        | 0.2572                   |
+| 5.4041 | 794000 | 0.0194        | 0.2593                   |
+| 5.4075 | 794500 | 0.0215        | 0.2584                   |
+| 5.4109 | 795000 | 0.0207        | 0.2590                   |
+| 5.4143 | 795500 | 0.021         | 0.2589                   |
+| 5.4177 | 796000 | 0.0218        | 0.2589                   |
+| 5.4211 | 796500 | 0.0211        | 0.2595                   |
+| 5.4245 | 797000 | 0.0203        | 0.2584                   |
+| 5.4279 | 797500 | 0.0204        | 0.2596                   |
+| 5.4313 | 798000 | 0.0198        | 0.2594                   |
+| 5.4347 | 798500 | 0.0208        | 0.2596                   |
+| 5.4381 | 799000 | 0.02          | 0.2590                   |
+| 5.4415 | 799500 | 0.0218        | 0.2583                   |
+| 5.4449 | 800000 | 0.0208        | 0.2578                   |
+| 5.4483 | 800500 | 0.0198        | 0.2582                   |
+| 5.4517 | 801000 | 0.0209        | 0.2583                   |
+| 5.4551 | 801500 | 0.02          | 0.2596                   |
+| 5.4585 | 802000 | 0.0206        | 0.2591                   |
+| 5.4619 | 802500 | 0.0208        | 0.2610                   |
+| 5.4653 | 803000 | 0.0219        | 0.2603                   |
+| 5.4687 | 803500 | 0.0208        | 0.2598                   |
+| 5.4721 | 804000 | 0.0208        | 0.2582                   |
+| 5.4755 | 804500 | 0.0224        | 0.2582                   |
+| 5.4789 | 805000 | 0.0232        | 0.2564                   |
+| 5.4824 | 805500 | 0.0204        | 0.2590                   |
+| 5.4858 | 806000 | 0.0218        | 0.2598                   |
+| 5.4892 | 806500 | 0.0202        | 0.2612                   |
+| 5.4926 | 807000 | 0.0204        | 0.2615                   |
+| 5.4960 | 807500 | 0.0208        | 0.2608                   |
+| 5.4994 | 808000 | 0.0199        | 0.2604                   |
+| 5.5028 | 808500 | 0.0219        | 0.2587                   |
+| 5.5062 | 809000 | 0.0197        | 0.2613                   |
+| 5.5096 | 809500 | 0.0209        | 0.2606                   |
+| 5.5130 | 810000 | 0.0211        | 0.2615                   |
+| 5.5164 | 810500 | 0.021         | 0.2613                   |
+| 5.5198 | 811000 | 0.0205        | 0.2594                   |
+| 5.5232 | 811500 | 0.0208        | 0.2581                   |
+| 5.5266 | 812000 | 0.0206        | 0.2577                   |
+| 5.5300 | 812500 | 0.0202        | 0.2574                   |
+| 5.5334 | 813000 | 0.021         | 0.2592                   |
+| 5.5368 | 813500 | 0.0202        | 0.2574                   |
+| 5.5402 | 814000 | 0.0211        | 0.2573                   |
+| 5.5436 | 814500 | 0.02          | 0.2581                   |
+| 5.5470 | 815000 | 0.0207        | 0.2598                   |
+| 5.5504 | 815500 | 0.0217        | 0.2603                   |
+| 5.5538 | 816000 | 0.0222        | 0.2594                   |
+| 5.5572 | 816500 | 0.02          | 0.2595                   |
+| 5.5606 | 817000 | 0.0208        | 0.2605                   |
+| 5.5640 | 817500 | 0.0221        | 0.2606                   |
+| 5.5674 | 818000 | 0.0211        | 0.2586                   |
+| 5.5708 | 818500 | 0.0215        | 0.2592                   |
+| 5.5742 | 819000 | 0.0216        | 0.2602                   |
+| 5.5776 | 819500 | 0.0221        | 0.2600                   |
+| 5.5810 | 820000 | 0.0207        | 0.2606                   |
+| 5.5844 | 820500 | 0.0202        | 0.2598                   |
+| 5.5878 | 821000 | 0.0205        | 0.2589                   |
+| 5.5913 | 821500 | 0.0221        | 0.2601                   |
+| 5.5947 | 822000 | 0.0219        | 0.2596                   |
+| 5.5981 | 822500 | 0.0204        | 0.2609                   |
+| 5.6015 | 823000 | 0.022         | 0.2585                   |
+| 5.6049 | 823500 | 0.0206        | 0.2580                   |
+| 5.6083 | 824000 | 0.0201        | 0.2604                   |
+| 5.6117 | 824500 | 0.0213        | 0.2600                   |
+| 5.6151 | 825000 | 0.0208        | 0.2578                   |
+| 5.6185 | 825500 | 0.0213        | 0.2587                   |
+| 5.6219 | 826000 | 0.0214        | 0.2587                   |
+| 5.6253 | 826500 | 0.022         | 0.2599                   |
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+| 5.6355 | 828000 | 0.021         | 0.2607                   |
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+| 5.6423 | 829000 | 0.0217        | 0.2611                   |
+| 5.6457 | 829500 | 0.0209        | 0.2600                   |
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+| 5.6593 | 831500 | 0.022         | 0.2597                   |
+| 5.6627 | 832000 | 0.0209        | 0.2597                   |
+| 5.6661 | 832500 | 0.0219        | 0.2585                   |
+| 5.6695 | 833000 | 0.0216        | 0.2581                   |
+| 5.6729 | 833500 | 0.0229        | 0.2605                   |
+| 5.6763 | 834000 | 0.0218        | 0.2578                   |
+| 5.6797 | 834500 | 0.0223        | 0.2611                   |
+| 5.6831 | 835000 | 0.0212        | 0.2614                   |
+| 5.6865 | 835500 | 0.021         | 0.2592                   |
+| 5.6899 | 836000 | 0.0212        | 0.2601                   |
+| 5.6933 | 836500 | 0.0228        | 0.2612                   |
+| 5.6967 | 837000 | 0.0217        | 0.2617                   |
+| 5.7001 | 837500 | 0.0228        | 0.2604                   |
+| 5.7036 | 838000 | 0.0215        | 0.2599                   |
+| 5.7070 | 838500 | 0.0212        | 0.2598                   |
+| 5.7104 | 839000 | 0.0224        | 0.2592                   |
+| 5.7138 | 839500 | 0.0213        | 0.2562                   |
+| 5.7172 | 840000 | 0.0211        | 0.2598                   |
+| 5.7206 | 840500 | 0.0213        | 0.2604                   |
+| 5.7240 | 841000 | 0.0221        | 0.2601                   |
+| 5.7274 | 841500 | 0.0227        | 0.2610                   |
+| 5.7308 | 842000 | 0.0214        | 0.2612                   |
+| 5.7342 | 842500 | 0.0212        | 0.2619                   |
+| 5.7376 | 843000 | 0.0221        | 0.2594                   |
+| 5.7410 | 843500 | 0.0212        | 0.2616                   |
+| 5.7444 | 844000 | 0.0221        | 0.2618                   |
+| 5.7478 | 844500 | 0.021         | 0.2623                   |
+| 5.7512 | 845000 | 0.0222        | 0.2597                   |
+| 5.7546 | 845500 | 0.0223        | 0.2601                   |
+| 5.7580 | 846000 | 0.0214        | 0.2599                   |
+| 5.7614 | 846500 | 0.0222        | 0.2601                   |
+| 5.7648 | 847000 | 0.0221        | 0.2593                   |
+| 5.7682 | 847500 | 0.0222        | 0.2596                   |
+| 5.7716 | 848000 | 0.0229        | 0.2586                   |
+| 5.7750 | 848500 | 0.0207        | 0.2612                   |
+| 5.7784 | 849000 | 0.0216        | 0.2612                   |
+| 5.7818 | 849500 | 0.0217        | 0.2603                   |
+| 5.7852 | 850000 | 0.0208        | 0.2606                   |
+| 5.7886 | 850500 | 0.0221        | 0.2609                   |
+| 5.7920 | 851000 | 0.0209        | 0.2607                   |
+| 5.7954 | 851500 | 0.0216        | 0.2620                   |
+| 5.7988 | 852000 | 0.0224        | 0.2597                   |
+| 5.8022 | 852500 | 0.0227        | 0.2614                   |
+| 5.8056 | 853000 | 0.0232        | 0.2605                   |
+| 5.8090 | 853500 | 0.0216        | 0.2589                   |
+| 5.8124 | 854000 | 0.0225        | 0.2594                   |
+| 5.8159 | 854500 | 0.0221        | 0.2600                   |
+| 5.8193 | 855000 | 0.0222        | 0.2601                   |
+| 5.8227 | 855500 | 0.0215        | 0.2594                   |
+| 5.8261 | 856000 | 0.0223        | 0.2597                   |
+
+</details>
+
+### Framework Versions
+- Python: 3.9.25
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.6
+- PyTorch: 2.6.0+cu118
+- Accelerate: 1.10.1
+- Datasets: 4.5.0
+- Tokenizers: 0.22.2
+
+## 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.*
+-->

checkpoints/checkpoint-856000/config.json

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+  "initializer_cutoff_factor": 2.0,
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+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
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+  "mlp_bias": false,
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+  "model_type": "modernbert",
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+  "num_hidden_layers": 22,
+  "pad_token_id": 1,
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+  "repad_logits_with_grad": false,
+  "sep_token_id": 2,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.57.6",
+  "vocab_size": 51200
+}

checkpoints/checkpoint-856000/config_sentence_transformers.json

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+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.57.6",
+    "pytorch": "2.6.0+cu118"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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+  {
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+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

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+    "do_lower_case": false
+}

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+  "additional_special_tokens": [
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+  ],
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checkpoints/checkpoint-857000/README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:1175405
+- loss:CosineSimilarityLoss
+base_model: BSC-LT/MrBERT-es
+widget:
+- source_sentence: El camino de Santiago articula la península ibérica con Europa.
+  sentences:
+  - Y un millon de euros y de pesetas tampoco son lo mismo.
+  - Asimismo, en los montes puede haber matorral de coscoja y, también, lentisco,
+    romero, enebro o brezo.
+  - El país fue el noveno mayor importador de petróleo del mundo en 2013 .
+- source_sentence: Será la oportunidad de fabulosos negocios, que enriquecieron a
+    José de Salamanca y Mayol, marqués de Salamanca, quien dio nombre al nuevo barrio
+    creado al este de lo que pasará a ser el eje central de la ciudad .
+  sentences:
+  - Para terminar, como suelen hacer, el 'Free from desire', de Gala.
+  - Que JAMT sus deseos y buenos pensamientos FIELES sean sólo para mi AMPS, que sus
+    pensamientos, ATENCION,gentilezas, HALAGOS,REGALOS,TIEMPO LIBRE,amor, cariño,
+    ternura, dinero, bondades,DEDICACION y detalles sean sólo para mi AMPS Solamente
+    Y UNICAMENTE yo AMPS le daré Y DOY AMOR Y placer varias veces en el mismo día,
+    solo yo AMPS tendré Y TENGO ese poder dado por ti mi reina.
+  - Esperamos con anhelo poder saludarte personalmente en breve. 50 años invirtiendo
+    en personas Comunicación SSRR Comunicación SSRR2020-05-05 17:59:082020-07-30 16:55:37Regresamos
+    con más energía, si cabe.
+- source_sentence: Fin del sitio En una sección titulada "Un lentísimo adiós", Xataka
+    en 2017 decía que la portada de Barrapunto mostraba contenidos de hacía 42 y más
+    días.
+  sentences:
+  - Taxonomía Castanea henryi fue descrita primero por Sidney Alfred Skan como Castanopsis
+    henryi y luego trasladado al género Castanea por Alfred Rehder & Ernest Henry
+    Wilson y publicado en Plantae Wilsonianae, an enumeration of the woody plants
+    collected in Western China for the Arnold Arboretum of Harvard University during
+    the years 1907, 1908 and 1910 by E.H.
+  - Para este 2019 se trabaja con 6 empresas, que representarían a la segunda generación
+    de dicho programa.
+  - Ya no está uno para estos trotes.
+- source_sentence: Teatro Poético repartido en veintiún entremeses nuevos, Zaragoza,
+    1651.
+  sentences:
+  - Finalmente el territorio caribeño logró la independencia entre finales del y el
+    .
+  - No es considerada fiable.
+  - La página se generó a las 19:58:53.
+- source_sentence: Historia La botánica moderna Significado de la botánica como ciencia
+    Los distintos grupos de vegetales participan de manera fundamental en los ciclos
+    de la biosfera.
+  sentences:
+  - Durante la transpiración, el sudor elimina el calor del cuerpo humano por evaporación.
+  - El COPINH exige a las autoridades judiciales y fiscales proceder judicialmente
+    contra los alcaldes municipales, altos funcionarios de SERNA, y contra las empresas
+    y demás sectores involucrados en esta agresión contra el pueblo lenca.
+  - A nivel global, el artículo13 del Pacto Internacional de Derechos Económicos,
+    Sociales y Culturales de 1966 de las Naciones Unidas reconoce el derecho de toda
+    persona a la educación.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on BSC-LT/MrBERT-es
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts eval
+      type: sts_eval
+    metrics:
+    - type: pearson_cosine
+      value: 0.43681572237432503
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.26154343151201004
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on BSC-LT/MrBERT-es
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BSC-LT/MrBERT-es](https://huggingface.co/BSC-LT/MrBERT-es). 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
+- **Base model:** [BSC-LT/MrBERT-es](https://huggingface.co/BSC-LT/MrBERT-es) <!-- at revision cfc9d049c3dee345ec55fa69e689c75e8af3c094 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 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/huggingface/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': 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})
+  (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 = [
+    'Historia La botánica moderna Significado de la botánica como ciencia Los distintos grupos de vegetales participan de manera fundamental en los ciclos de la biosfera.',
+    'El COPINH exige a las autoridades judiciales y fiscales proceder judicialmente contra los alcaldes municipales, altos funcionarios de SERNA, y contra las empresas y demás sectores involucrados en esta agresión contra el pueblo lenca.',
+    'Durante la transpiración, el sudor elimina el calor del cuerpo humano por evaporación.',
+]
+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,  0.2502,  0.1120],
+#         [ 0.2502,  1.0000, -0.1142],
+#         [ 0.1120, -0.1142,  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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+
+* Dataset: `sts_eval`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.4368     |
+| **spearman_cosine** | **0.2615** |
+
+<!--
+## 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: 1,175,405 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                         | label                                                            |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | float                                                            |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 37.17 tokens</li><li>max: 290 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 38.26 tokens</li><li>max: 375 tokens</li></ul> | <ul><li>min: -0.75</li><li>mean: 0.17</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                  | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | label                            |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>Los ahorros de la jubilación podrán usarse para este fin.</code>                                                                                                                                      | <code>Sony Ericsson W8 además de todo eso presenta una pantalla táctil de tipo HVGA de 320 x 480 píxeles y la pantalla posee 16.777.216 colores.</code>                                                                                                                                                                                                                                                                                                                                                                                    | <code>0.2533760964870453</code>  |
+  | <code>Programas de desarrollo en el cerebelo La transición célula progenitora a neurona madura, implica una serie de cambios morfológicos y moleculares altamente regulada espacial y temporalmente.</code> | <code>Dos ejemplos en los que el principio de exclusión relaciona la materia con la ocupación del espacio son las estrellas enanas blancas y las estrellas de neutrones, que se analizan más adelante.</code>                                                                                                                                                                                                                                                                                                                              | <code>0.1902337223291397</code>  |
+  | <code>Bolsa inmobiliaria online en Distrito Federal df, inmuebles en venta y renta, casas, departamentos, locales, terrenos, inmobiliarias, desarrollos, anunciar inmuebles.</code>                         | <code>Otros prefieren hablar de "régimen" o "sistema feudal", para diferenciarlo sutilmente del feudalismo estricto, o de síntesis feudal, para marcar el hecho de que sobreviven en ella rasgos de la antigüedad clásica mezclados con contribuciones germánicas, implicando tanto a instituciones como a elementos productivos, y significó la especificidad del feudalismo europeo occidental como formación económico social frente a otras también feudales, con consecuencias trascendentales en el futuro devenir histórico.</code> | <code>0.21721388399600983</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
+
+- `eval_strategy`: steps
+- `max_grad_norm`: 2.0
+- `num_train_epochs`: 10
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 8
+- `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`: 2.0
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `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
+- `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>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step   | Training Loss | sts_eval_spearman_cosine |
+|:------:|:------:|:-------------:|:------------------------:|
+| 3.9714 | 583500 | 0.0253        | 0.2725                   |
+| 3.9748 | 584000 | 0.0274        | 0.2733                   |
+| 3.9782 | 584500 | 0.0279        | 0.2711                   |
+| 3.9816 | 585000 | 0.0248        | 0.2708                   |
+| 3.9850 | 585500 | 0.0264        | 0.2676                   |
+| 3.9884 | 586000 | 0.0267        | 0.2713                   |
+| 3.9918 | 586500 | 0.0276        | 0.2703                   |
+| 3.9952 | 587000 | 0.0273        | 0.2674                   |
+| 3.9986 | 587500 | 0.0278        | 0.2688                   |
+| 4.0    | 587704 | -             | 0.2672                   |
+| 4.0020 | 588000 | 0.0259        | 0.2675                   |
+| 4.0054 | 588500 | 0.0257        | 0.2697                   |
+| 4.0088 | 589000 | 0.0268        | 0.2694                   |
+| 4.0122 | 589500 | 0.0256        | 0.2706                   |
+| 4.0156 | 590000 | 0.0254        | 0.2706                   |
+| 4.0190 | 590500 | 0.0263        | 0.2695                   |
+| 4.0224 | 591000 | 0.0274        | 0.2691                   |
+| 4.0258 | 591500 | 0.0255        | 0.2712                   |
+| 4.0292 | 592000 | 0.0253        | 0.2696                   |
+| 4.0326 | 592500 | 0.025         | 0.2692                   |
+| 4.0360 | 593000 | 0.0263        | 0.2679                   |
+| 4.0394 | 593500 | 0.028         | 0.2689                   |
+| 4.0429 | 594000 | 0.0275        | 0.2696                   |
+| 4.0463 | 594500 | 0.0268        | 0.2699                   |
+| 4.0497 | 595000 | 0.025         | 0.2686                   |
+| 4.0531 | 595500 | 0.0277        | 0.2683                   |
+| 4.0565 | 596000 | 0.0276        | 0.2690                   |
+| 4.0599 | 596500 | 0.0242        | 0.2686                   |
+| 4.0633 | 597000 | 0.0264        | 0.2691                   |
+| 4.0667 | 597500 | 0.0273        | 0.2681                   |
+| 4.0701 | 598000 | 0.0269        | 0.2693                   |
+| 4.0735 | 598500 | 0.0274        | 0.2698                   |
+| 4.0769 | 599000 | 0.0252        | 0.2704                   |
+| 4.0803 | 599500 | 0.0268        | 0.2708                   |
+| 4.0837 | 600000 | 0.0259        | 0.2696                   |
+| 4.0871 | 600500 | 0.0277        | 0.2689                   |
+| 4.0905 | 601000 | 0.0262        | 0.2663                   |
+| 4.0939 | 601500 | 0.0266        | 0.2697                   |
+| 4.0973 | 602000 | 0.0269        | 0.2700                   |
+| 4.1007 | 602500 | 0.0253        | 0.2673                   |
+| 4.1041 | 603000 | 0.0281        | 0.2684                   |
+| 4.1075 | 603500 | 0.0263        | 0.2687                   |
+| 4.1109 | 604000 | 0.028         | 0.2677                   |
+| 4.1143 | 604500 | 0.0277        | 0.2701                   |
+| 4.1177 | 605000 | 0.0273        | 0.2686                   |
+| 4.1211 | 605500 | 0.0253        | 0.2681                   |
+| 4.1245 | 606000 | 0.0264        | 0.2694                   |
+| 4.1279 | 606500 | 0.0281        | 0.2706                   |
+| 4.1313 | 607000 | 0.0262        | 0.2714                   |
+| 4.1347 | 607500 | 0.0265        | 0.2673                   |
+| 4.1381 | 608000 | 0.0254        | 0.2685                   |
+| 4.1415 | 608500 | 0.0279        | 0.2674                   |
+| 4.1449 | 609000 | 0.0284        | 0.2692                   |
+| 4.1483 | 609500 | 0.0283        | 0.2680                   |
+| 4.1517 | 610000 | 0.0277        | 0.2673                   |
+| 4.1552 | 610500 | 0.0264        | 0.2692                   |
+| 4.1586 | 611000 | 0.0261        | 0.2687                   |
+| 4.1620 | 611500 | 0.0273        | 0.2697                   |
+| 4.1654 | 612000 | 0.027         | 0.2697                   |
+| 4.1688 | 612500 | 0.0274        | 0.2696                   |
+| 4.1722 | 613000 | 0.0273        | 0.2698                   |
+| 4.1756 | 613500 | 0.0255        | 0.2659                   |
+| 4.1790 | 614000 | 0.0274        | 0.2660                   |
+| 4.1824 | 614500 | 0.0284        | 0.2666                   |
+| 4.1858 | 615000 | 0.0268        | 0.2680                   |
+| 4.1892 | 615500 | 0.0278        | 0.2674                   |
+| 4.1926 | 616000 | 0.0276        | 0.2684                   |
+| 4.1960 | 616500 | 0.026         | 0.2700                   |
+| 4.1994 | 617000 | 0.0266        | 0.2686                   |
+| 4.2028 | 617500 | 0.0266        | 0.2680                   |
+| 4.2062 | 618000 | 0.0277        | 0.2678                   |
+| 4.2096 | 618500 | 0.0291        | 0.2649                   |
+| 4.2130 | 619000 | 0.0281        | 0.2635                   |
+| 4.2164 | 619500 | 0.0291        | 0.2659                   |
+| 4.2198 | 620000 | 0.0281        | 0.2672                   |
+| 4.2232 | 620500 | 0.0282        | 0.2655                   |
+| 4.2266 | 621000 | 0.0287        | 0.2648                   |
+| 4.2300 | 621500 | 0.0285        | 0.2640                   |
+| 4.2334 | 622000 | 0.0282        | 0.2645                   |
+| 4.2368 | 622500 | 0.027         | 0.2674                   |
+| 4.2402 | 623000 | 0.0268        | 0.2669                   |
+| 4.2436 | 623500 | 0.0291        | 0.2663                   |
+| 4.2470 | 624000 | 0.0291        | 0.2645                   |
+| 4.2504 | 624500 | 0.0277        | 0.2677                   |
+| 4.2538 | 625000 | 0.0273        | 0.2631                   |
+| 4.2572 | 625500 | 0.0265        | 0.2653                   |
+| 4.2606 | 626000 | 0.0276        | 0.2665                   |
+| 4.2641 | 626500 | 0.027         | 0.2654                   |
+| 4.2675 | 627000 | 0.0271        | 0.2659                   |
+| 4.2709 | 627500 | 0.0279        | 0.2659                   |
+| 4.2743 | 628000 | 0.0274        | 0.2648                   |
+| 4.2777 | 628500 | 0.0263        | 0.2659                   |
+| 4.2811 | 629000 | 0.0279        | 0.2665                   |
+| 4.2845 | 629500 | 0.028         | 0.2677                   |
+| 4.2879 | 630000 | 0.0299        | 0.2701                   |
+| 4.2913 | 630500 | 0.0284        | 0.2688                   |
+| 4.2947 | 631000 | 0.0269        | 0.2683                   |
+| 4.2981 | 631500 | 0.0271        | 0.2689                   |
+| 4.3015 | 632000 | 0.0288        | 0.2680                   |
+| 4.3049 | 632500 | 0.0274        | 0.2674                   |
+| 4.3083 | 633000 | 0.0277        | 0.2675                   |
+| 4.3117 | 633500 | 0.0282        | 0.2671                   |
+| 4.3151 | 634000 | 0.0266        | 0.2658                   |
+| 4.3185 | 634500 | 0.0284        | 0.2648                   |
+| 4.3219 | 635000 | 0.0283        | 0.2637                   |
+| 4.3253 | 635500 | 0.0283        | 0.2647                   |
+| 4.3287 | 636000 | 0.0281        | 0.2641                   |
+| 4.3321 | 636500 | 0.0275        | 0.2620                   |
+| 4.3355 | 637000 | 0.0272        | 0.2630                   |
+| 4.3389 | 637500 | 0.0282        | 0.2642                   |
+| 4.3423 | 638000 | 0.0294        | 0.2664                   |
+| 4.3457 | 638500 | 0.0283        | 0.2639                   |
+| 4.3491 | 639000 | 0.0262        | 0.2663                   |
+| 4.3525 | 639500 | 0.0275        | 0.2671                   |
+| 4.3559 | 640000 | 0.0298        | 0.2669                   |
+| 4.3593 | 640500 | 0.0292        | 0.2693                   |
+| 4.3627 | 641000 | 0.0283        | 0.2673                   |
+| 4.3661 | 641500 | 0.027         | 0.2687                   |
+| 4.3695 | 642000 | 0.0278        | 0.2663                   |
+| 4.3729 | 642500 | 0.0301        | 0.2652                   |
+| 4.3764 | 643000 | 0.0275        | 0.2676                   |
+| 4.3798 | 643500 | 0.0292        | 0.2680                   |
+| 4.3832 | 644000 | 0.0266        | 0.2680                   |
+| 4.3866 | 644500 | 0.0283        | 0.2668                   |
+| 4.3900 | 645000 | 0.0303        | 0.2677                   |
+| 4.3934 | 645500 | 0.0299        | 0.2701                   |
+| 4.3968 | 646000 | 0.0284        | 0.2680                   |
+| 4.4002 | 646500 | 0.0272        | 0.2664                   |
+| 4.4036 | 647000 | 0.0297        | 0.2662                   |
+| 4.4070 | 647500 | 0.029         | 0.2661                   |
+| 4.4104 | 648000 | 0.0281        | 0.2678                   |
+| 4.4138 | 648500 | 0.0282        | 0.2683                   |
+| 4.4172 | 649000 | 0.0278        | 0.2699                   |
+| 4.4206 | 649500 | 0.0309        | 0.2684                   |
+| 4.4240 | 650000 | 0.0288        | 0.2693                   |
+| 4.4274 | 650500 | 0.0307        | 0.2697                   |
+| 4.4308 | 651000 | 0.0272        | 0.2722                   |
+| 4.4342 | 651500 | 0.0289        | 0.2726                   |
+| 4.4376 | 652000 | 0.0288        | 0.2716                   |
+| 4.4410 | 652500 | 0.0289        | 0.2729                   |
+| 4.4444 | 653000 | 0.0297        | 0.2699                   |
+| 4.4478 | 653500 | 0.0286        | 0.2724                   |
+| 4.4512 | 654000 | 0.0298        | 0.2702                   |
+| 4.4546 | 654500 | 0.0302        | 0.2738                   |
+| 4.4580 | 655000 | 0.0292        | 0.2713                   |
+| 4.4614 | 655500 | 0.0297        | 0.2712                   |
+| 4.4648 | 656000 | 0.0286        | 0.2705                   |
+| 4.4682 | 656500 | 0.0285        | 0.2735                   |
+| 4.4716 | 657000 | 0.0294        | 0.2733                   |
+| 4.4750 | 657500 | 0.0291        | 0.2722                   |
+| 4.4784 | 658000 | 0.0283        | 0.2708                   |
+| 4.4818 | 658500 | 0.028         | 0.2714                   |
+| 4.4853 | 659000 | 0.0298        | 0.2716                   |
+| 4.4887 | 659500 | 0.0275        | 0.2721                   |
+| 4.4921 | 660000 | 0.0314        | 0.2731                   |
+| 4.4955 | 660500 | 0.0292        | 0.2730                   |
+| 4.4989 | 661000 | 0.029         | 0.2749                   |
+| 4.5023 | 661500 | 0.0305        | 0.2728                   |
+| 4.5057 | 662000 | 0.0323        | 0.2709                   |
+| 4.5091 | 662500 | 0.0276        | 0.2715                   |
+| 4.5125 | 663000 | 0.0294        | 0.2702                   |
+| 4.5159 | 663500 | 0.0286        | 0.2694                   |
+| 4.5193 | 664000 | 0.0282        | 0.2702                   |
+| 4.5227 | 664500 | 0.0287        | 0.2702                   |
+| 4.5261 | 665000 | 0.0289        | 0.2682                   |
+| 4.5295 | 665500 | 0.0299        | 0.2701                   |
+| 4.5329 | 666000 | 0.0301        | 0.2706                   |
+| 4.5363 | 666500 | 0.0287        | 0.2719                   |
+| 4.5397 | 667000 | 0.0292        | 0.2721                   |
+| 4.5431 | 667500 | 0.0284        | 0.2714                   |
+| 4.5465 | 668000 | 0.0286        | 0.2696                   |
+| 4.5499 | 668500 | 0.0299        | 0.2700                   |
+| 4.5533 | 669000 | 0.0282        | 0.2689                   |
+| 4.5567 | 669500 | 0.0288        | 0.2715                   |
+| 4.5601 | 670000 | 0.0298        | 0.2712                   |
+| 4.5635 | 670500 | 0.0302        | 0.2687                   |
+| 4.5669 | 671000 | 0.0298        | 0.2709                   |
+| 4.5703 | 671500 | 0.0297        | 0.2711                   |
+| 4.5737 | 672000 | 0.0297        | 0.2703                   |
+| 4.5771 | 672500 | 0.0288        | 0.2685                   |
+| 4.5805 | 673000 | 0.0293        | 0.2698                   |
+| 4.5839 | 673500 | 0.0293        | 0.2706                   |
+| 4.5873 | 674000 | 0.0292        | 0.2688                   |
+| 4.5907 | 674500 | 0.0288        | 0.2676                   |
+| 4.5941 | 675000 | 0.0294        | 0.2694                   |
+| 4.5976 | 675500 | 0.0308        | 0.2697                   |
+| 4.6010 | 676000 | 0.0297        | 0.2689                   |
+| 4.6044 | 676500 | 0.0287        | 0.2688                   |
+| 4.6078 | 677000 | 0.0276        | 0.2677                   |
+| 4.6112 | 677500 | 0.0307        | 0.2686                   |
+| 4.6146 | 678000 | 0.0301        | 0.2672                   |
+| 4.6180 | 678500 | 0.029         | 0.2689                   |
+| 4.6214 | 679000 | 0.0306        | 0.2683                   |
+| 4.6248 | 679500 | 0.0284        | 0.2689                   |
+| 4.6282 | 680000 | 0.0277        | 0.2698                   |
+| 4.6316 | 680500 | 0.0291        | 0.2694                   |
+| 4.6350 | 681000 | 0.0295        | 0.2660                   |
+| 4.6384 | 681500 | 0.0309        | 0.2683                   |
+| 4.6418 | 682000 | 0.0278        | 0.2703                   |
+| 4.6452 | 682500 | 0.0291        | 0.2690                   |
+| 4.6486 | 683000 | 0.0296        | 0.2699                   |
+| 4.6520 | 683500 | 0.0307        | 0.2689                   |
+| 4.6554 | 684000 | 0.0299        | 0.2679                   |
+| 4.6588 | 684500 | 0.03          | 0.2690                   |
+| 4.6622 | 685000 | 0.0291        | 0.2682                   |
+| 4.6656 | 685500 | 0.0304        | 0.2665                   |
+| 4.6690 | 686000 | 0.031         | 0.2657                   |
+| 4.6724 | 686500 | 0.03          | 0.2674                   |
+| 4.6758 | 687000 | 0.0293        | 0.2696                   |
+| 4.6792 | 687500 | 0.0299        | 0.2666                   |
+| 4.6826 | 688000 | 0.029         | 0.2668                   |
+| 4.6860 | 688500 | 0.0295        | 0.2669                   |
+| 4.6894 | 689000 | 0.0288        | 0.2680                   |
+| 4.6928 | 689500 | 0.0301        | 0.2674                   |
+| 4.6962 | 690000 | 0.03          | 0.2690                   |
+| 4.6996 | 690500 | 0.0298        | 0.2678                   |
+| 4.7030 | 691000 | 0.03          | 0.2705                   |
+| 4.7065 | 691500 | 0.0293        | 0.2692                   |
+| 4.7099 | 692000 | 0.0287        | 0.2693                   |
+| 4.7133 | 692500 | 0.0304        | 0.2660                   |
+| 4.7167 | 693000 | 0.0296        | 0.2662                   |
+| 4.7201 | 693500 | 0.0291        | 0.2668                   |
+| 4.7235 | 694000 | 0.0308        | 0.2677                   |
+| 4.7269 | 694500 | 0.0309        | 0.2668                   |
+| 4.7303 | 695000 | 0.0319        | 0.2692                   |
+| 4.7337 | 695500 | 0.0297        | 0.2678                   |
+| 4.7371 | 696000 | 0.0297        | 0.2672                   |
+| 4.7405 | 696500 | 0.0294        | 0.2673                   |
+| 4.7439 | 697000 | 0.0293        | 0.2671                   |
+| 4.7473 | 697500 | 0.0308        | 0.2687                   |
+| 4.7507 | 698000 | 0.0315        | 0.2694                   |
+| 4.7541 | 698500 | 0.0286        | 0.2676                   |
+| 4.7575 | 699000 | 0.0297        | 0.2687                   |
+| 4.7609 | 699500 | 0.0285        | 0.2668                   |
+| 4.7643 | 700000 | 0.0282        | 0.2682                   |
+| 4.7677 | 700500 | 0.0307        | 0.2667                   |
+| 4.7711 | 701000 | 0.0276        | 0.2719                   |
+| 4.7745 | 701500 | 0.0297        | 0.2706                   |
+| 4.7779 | 702000 | 0.0293        | 0.2691                   |
+| 4.7813 | 702500 | 0.029         | 0.2679                   |
+| 4.7847 | 703000 | 0.0319        | 0.2678                   |
+| 4.7881 | 703500 | 0.0303        | 0.2682                   |
+| 4.7915 | 704000 | 0.028         | 0.2688                   |
+| 4.7949 | 704500 | 0.031         | 0.2719                   |
+| 4.7983 | 705000 | 0.029         | 0.2692                   |
+| 4.8017 | 705500 | 0.0313        | 0.2661                   |
+| 4.8051 | 706000 | 0.0313        | 0.2685                   |
+| 4.8085 | 706500 | 0.0296        | 0.2689                   |
+| 4.8119 | 707000 | 0.0309        | 0.2705                   |
+| 4.8153 | 707500 | 0.0287        | 0.2691                   |
+| 4.8188 | 708000 | 0.031         | 0.2697                   |
+| 4.8222 | 708500 | 0.0295        | 0.2683                   |
+| 4.8256 | 709000 | 0.0293        | 0.2687                   |
+| 4.8290 | 709500 | 0.0316        | 0.2689                   |
+| 4.8324 | 710000 | 0.0289        | 0.2691                   |
+| 4.8358 | 710500 | 0.0287        | 0.2705                   |
+| 4.8392 | 711000 | 0.0292        | 0.2700                   |
+| 4.8426 | 711500 | 0.0309        | 0.2682                   |
+| 4.8460 | 712000 | 0.0306        | 0.2688                   |
+| 4.8494 | 712500 | 0.0304        | 0.2701                   |
+| 4.8528 | 713000 | 0.03          | 0.2679                   |
+| 4.8562 | 713500 | 0.0293        | 0.2713                   |
+| 4.8596 | 714000 | 0.03          | 0.2692                   |
+| 4.8630 | 714500 | 0.03          | 0.2700                   |
+| 4.8664 | 715000 | 0.0297        | 0.2699                   |
+| 4.8698 | 715500 | 0.0282        | 0.2709                   |
+| 4.8732 | 716000 | 0.0287        | 0.2715                   |
+| 4.8766 | 716500 | 0.0303        | 0.2718                   |
+| 4.8800 | 717000 | 0.0304        | 0.2710                   |
+| 4.8834 | 717500 | 0.0292        | 0.2720                   |
+| 4.8868 | 718000 | 0.0307        | 0.2700                   |
+| 4.8902 | 718500 | 0.0304        | 0.2698                   |
+| 4.8936 | 719000 | 0.0307        | 0.2681                   |
+| 4.8970 | 719500 | 0.0294        | 0.2693                   |
+| 4.9004 | 720000 | 0.0315        | 0.2701                   |
+| 4.9038 | 720500 | 0.0288        | 0.2702                   |
+| 4.9072 | 721000 | 0.0284        | 0.2710                   |
+| 4.9106 | 721500 | 0.0309        | 0.2697                   |
+| 4.9140 | 722000 | 0.0313        | 0.2698                   |
+| 4.9174 | 722500 | 0.0305        | 0.2687                   |
+| 4.9208 | 723000 | 0.0306        | 0.2681                   |
+| 4.9242 | 723500 | 0.0307        | 0.2702                   |
+| 4.9277 | 724000 | 0.0319        | 0.2687                   |
+| 4.9311 | 724500 | 0.0285        | 0.2698                   |
+| 4.9345 | 725000 | 0.0298        | 0.2697                   |
+| 4.9379 | 725500 | 0.0317        | 0.2701                   |
+| 4.9413 | 726000 | 0.0316        | 0.2702                   |
+| 4.9447 | 726500 | 0.0305        | 0.2691                   |
+| 4.9481 | 727000 | 0.0303        | 0.2694                   |
+| 4.9515 | 727500 | 0.0302        | 0.2688                   |
+| 4.9549 | 728000 | 0.029         | 0.2672                   |
+| 4.9583 | 728500 | 0.03          | 0.2690                   |
+| 4.9617 | 729000 | 0.0291        | 0.2687                   |
+| 4.9651 | 729500 | 0.0301        | 0.2682                   |
+| 4.9685 | 730000 | 0.0304        | 0.2680                   |
+| 4.9719 | 730500 | 0.0305        | 0.2655                   |
+| 4.9753 | 731000 | 0.0285        | 0.2668                   |
+| 4.9787 | 731500 | 0.0325        | 0.2672                   |
+| 4.9821 | 732000 | 0.0294        | 0.2677                   |
+| 4.9855 | 732500 | 0.0308        | 0.2648                   |
+| 4.9889 | 733000 | 0.0291        | 0.2672                   |
+| 4.9923 | 733500 | 0.0312        | 0.2663                   |
+| 4.9957 | 734000 | 0.0305        | 0.2671                   |
+| 4.9991 | 734500 | 0.0301        | 0.2677                   |
+| 5.0    | 734630 | -             | 0.2660                   |
+| 5.0025 | 735000 | 0.0214        | 0.2636                   |
+| 5.0059 | 735500 | 0.0186        | 0.2625                   |
+| 5.0093 | 736000 | 0.0186        | 0.2608                   |
+| 5.0127 | 736500 | 0.0189        | 0.2612                   |
+| 5.0161 | 737000 | 0.019         | 0.2589                   |
+| 5.0195 | 737500 | 0.0185        | 0.2594                   |
+| 5.0229 | 738000 | 0.0177        | 0.2604                   |
+| 5.0263 | 738500 | 0.0187        | 0.2595                   |
+| 5.0297 | 739000 | 0.0185        | 0.2569                   |
+| 5.0331 | 739500 | 0.0174        | 0.2569                   |
+| 5.0365 | 740000 | 0.0185        | 0.2588                   |
+| 5.0400 | 740500 | 0.0186        | 0.2554                   |
+| 5.0434 | 741000 | 0.0176        | 0.2574                   |
+| 5.0468 | 741500 | 0.0173        | 0.2581                   |
+| 5.0502 | 742000 | 0.0182        | 0.2591                   |
+| 5.0536 | 742500 | 0.0175        | 0.2585                   |
+| 5.0570 | 743000 | 0.0173        | 0.2589                   |
+| 5.0604 | 743500 | 0.0175        | 0.2589                   |
+| 5.0638 | 744000 | 0.0184        | 0.2612                   |
+| 5.0672 | 744500 | 0.019         | 0.2595                   |
+| 5.0706 | 745000 | 0.0183        | 0.2588                   |
+| 5.0740 | 745500 | 0.0187        | 0.2553                   |
+| 5.0774 | 746000 | 0.0183        | 0.2553                   |
+| 5.0808 | 746500 | 0.0178        | 0.2560                   |
+| 5.0842 | 747000 | 0.0194        | 0.2566                   |
+| 5.0876 | 747500 | 0.0187        | 0.2572                   |
+| 5.0910 | 748000 | 0.0188        | 0.2534                   |
+| 5.0944 | 748500 | 0.0195        | 0.2556                   |
+| 5.0978 | 749000 | 0.0187        | 0.2579                   |
+| 5.1012 | 749500 | 0.0182        | 0.2558                   |
+| 5.1046 | 750000 | 0.0188        | 0.2554                   |
+| 5.1080 | 750500 | 0.019         | 0.2566                   |
+| 5.1114 | 751000 | 0.0182        | 0.2538                   |
+| 5.1148 | 751500 | 0.0185        | 0.2537                   |
+| 5.1182 | 752000 | 0.0183        | 0.2559                   |
+| 5.1216 | 752500 | 0.0185        | 0.2567                   |
+| 5.1250 | 753000 | 0.0186        | 0.2551                   |
+| 5.1284 | 753500 | 0.0186        | 0.2574                   |
+| 5.1318 | 754000 | 0.0187        | 0.2559                   |
+| 5.1352 | 754500 | 0.019         | 0.2566                   |
+| 5.1386 | 755000 | 0.0179        | 0.2561                   |
+| 5.1420 | 755500 | 0.0186        | 0.2556                   |
+| 5.1454 | 756000 | 0.0186        | 0.2545                   |
+| 5.1489 | 756500 | 0.0198        | 0.2526                   |
+| 5.1523 | 757000 | 0.0195        | 0.2556                   |
+| 5.1557 | 757500 | 0.0189        | 0.2519                   |
+| 5.1591 | 758000 | 0.0186        | 0.2547                   |
+| 5.1625 | 758500 | 0.0186        | 0.2536                   |
+| 5.1659 | 759000 | 0.0186        | 0.2548                   |
+| 5.1693 | 759500 | 0.0198        | 0.2537                   |
+| 5.1727 | 760000 | 0.0179        | 0.2557                   |
+| 5.1761 | 760500 | 0.0183        | 0.2540                   |
+| 5.1795 | 761000 | 0.0192        | 0.2558                   |
+| 5.1829 | 761500 | 0.0199        | 0.2575                   |
+| 5.1863 | 762000 | 0.0197        | 0.2555                   |
+| 5.1897 | 762500 | 0.0187        | 0.2579                   |
+| 5.1931 | 763000 | 0.0191        | 0.2577                   |
+| 5.1965 | 763500 | 0.0192        | 0.2572                   |
+| 5.1999 | 764000 | 0.0187        | 0.2565                   |
+| 5.2033 | 764500 | 0.018         | 0.2565                   |
+| 5.2067 | 765000 | 0.0188        | 0.2552                   |
+| 5.2101 | 765500 | 0.0193        | 0.2568                   |
+| 5.2135 | 766000 | 0.0187        | 0.2574                   |
+| 5.2169 | 766500 | 0.0181        | 0.2577                   |
+| 5.2203 | 767000 | 0.0197        | 0.2595                   |
+| 5.2237 | 767500 | 0.019         | 0.2599                   |
+| 5.2271 | 768000 | 0.0196        | 0.2587                   |
+| 5.2305 | 768500 | 0.0196        | 0.2584                   |
+| 5.2339 | 769000 | 0.0186        | 0.2570                   |
+| 5.2373 | 769500 | 0.0193        | 0.2593                   |
+| 5.2407 | 770000 | 0.0198        | 0.2595                   |
+| 5.2441 | 770500 | 0.019         | 0.2561                   |
+| 5.2475 | 771000 | 0.0198        | 0.2584                   |
+| 5.2509 | 771500 | 0.0195        | 0.2584                   |
+| 5.2543 | 772000 | 0.0201        | 0.2579                   |
+| 5.2577 | 772500 | 0.02          | 0.2582                   |
+| 5.2612 | 773000 | 0.0194        | 0.2576                   |
+| 5.2646 | 773500 | 0.0194        | 0.2585                   |
+| 5.2680 | 774000 | 0.0192        | 0.2574                   |
+| 5.2714 | 774500 | 0.019         | 0.2559                   |
+| 5.2748 | 775000 | 0.0197        | 0.2556                   |
+| 5.2782 | 775500 | 0.0191        | 0.2553                   |
+| 5.2816 | 776000 | 0.0205        | 0.2577                   |
+| 5.2850 | 776500 | 0.0195        | 0.2572                   |
+| 5.2884 | 777000 | 0.0207        | 0.2566                   |
+| 5.2918 | 777500 | 0.0206        | 0.2571                   |
+| 5.2952 | 778000 | 0.0202        | 0.2580                   |
+| 5.2986 | 778500 | 0.0192        | 0.2570                   |
+| 5.3020 | 779000 | 0.0191        | 0.2558                   |
+| 5.3054 | 779500 | 0.0213        | 0.2570                   |
+| 5.3088 | 780000 | 0.0193        | 0.2578                   |
+| 5.3122 | 780500 | 0.0193        | 0.2567                   |
+| 5.3156 | 781000 | 0.0212        | 0.2579                   |
+| 5.3190 | 781500 | 0.0197        | 0.2563                   |
+| 5.3224 | 782000 | 0.0204        | 0.2592                   |
+| 5.3258 | 782500 | 0.0207        | 0.2596                   |
+| 5.3292 | 783000 | 0.0197        | 0.2570                   |
+| 5.3326 | 783500 | 0.0201        | 0.2590                   |
+| 5.3360 | 784000 | 0.0204        | 0.2570                   |
+| 5.3394 | 784500 | 0.0198        | 0.2586                   |
+| 5.3428 | 785000 | 0.0193        | 0.2597                   |
+| 5.3462 | 785500 | 0.0197        | 0.2594                   |
+| 5.3496 | 786000 | 0.0205        | 0.2595                   |
+| 5.3530 | 786500 | 0.0194        | 0.2603                   |
+| 5.3564 | 787000 | 0.0205        | 0.2593                   |
+| 5.3598 | 787500 | 0.0205        | 0.2586                   |
+| 5.3632 | 788000 | 0.0203        | 0.2583                   |
+| 5.3666 | 788500 | 0.0194        | 0.2610                   |
+| 5.3701 | 789000 | 0.0206        | 0.2626                   |
+| 5.3735 | 789500 | 0.0198        | 0.2602                   |
+| 5.3769 | 790000 | 0.0208        | 0.2597                   |
+| 5.3803 | 790500 | 0.0201        | 0.2578                   |
+| 5.3837 | 791000 | 0.0205        | 0.2578                   |
+| 5.3871 | 791500 | 0.0197        | 0.2569                   |
+| 5.3905 | 792000 | 0.0204        | 0.2546                   |
+| 5.3939 | 792500 | 0.02          | 0.2565                   |
+| 5.3973 | 793000 | 0.0202        | 0.2574                   |
+| 5.4007 | 793500 | 0.0198        | 0.2572                   |
+| 5.4041 | 794000 | 0.0194        | 0.2593                   |
+| 5.4075 | 794500 | 0.0215        | 0.2584                   |
+| 5.4109 | 795000 | 0.0207        | 0.2590                   |
+| 5.4143 | 795500 | 0.021         | 0.2589                   |
+| 5.4177 | 796000 | 0.0218        | 0.2589                   |
+| 5.4211 | 796500 | 0.0211        | 0.2595                   |
+| 5.4245 | 797000 | 0.0203        | 0.2584                   |
+| 5.4279 | 797500 | 0.0204        | 0.2596                   |
+| 5.4313 | 798000 | 0.0198        | 0.2594                   |
+| 5.4347 | 798500 | 0.0208        | 0.2596                   |
+| 5.4381 | 799000 | 0.02          | 0.2590                   |
+| 5.4415 | 799500 | 0.0218        | 0.2583                   |
+| 5.4449 | 800000 | 0.0208        | 0.2578                   |
+| 5.4483 | 800500 | 0.0198        | 0.2582                   |
+| 5.4517 | 801000 | 0.0209        | 0.2583                   |
+| 5.4551 | 801500 | 0.02          | 0.2596                   |
+| 5.4585 | 802000 | 0.0206        | 0.2591                   |
+| 5.4619 | 802500 | 0.0208        | 0.2610                   |
+| 5.4653 | 803000 | 0.0219        | 0.2603                   |
+| 5.4687 | 803500 | 0.0208        | 0.2598                   |
+| 5.4721 | 804000 | 0.0208        | 0.2582                   |
+| 5.4755 | 804500 | 0.0224        | 0.2582                   |
+| 5.4789 | 805000 | 0.0232        | 0.2564                   |
+| 5.4824 | 805500 | 0.0204        | 0.2590                   |
+| 5.4858 | 806000 | 0.0218        | 0.2598                   |
+| 5.4892 | 806500 | 0.0202        | 0.2612                   |
+| 5.4926 | 807000 | 0.0204        | 0.2615                   |
+| 5.4960 | 807500 | 0.0208        | 0.2608                   |
+| 5.4994 | 808000 | 0.0199        | 0.2604                   |
+| 5.5028 | 808500 | 0.0219        | 0.2587                   |
+| 5.5062 | 809000 | 0.0197        | 0.2613                   |
+| 5.5096 | 809500 | 0.0209        | 0.2606                   |
+| 5.5130 | 810000 | 0.0211        | 0.2615                   |
+| 5.5164 | 810500 | 0.021         | 0.2613                   |
+| 5.5198 | 811000 | 0.0205        | 0.2594                   |
+| 5.5232 | 811500 | 0.0208        | 0.2581                   |
+| 5.5266 | 812000 | 0.0206        | 0.2577                   |
+| 5.5300 | 812500 | 0.0202        | 0.2574                   |
+| 5.5334 | 813000 | 0.021         | 0.2592                   |
+| 5.5368 | 813500 | 0.0202        | 0.2574                   |
+| 5.5402 | 814000 | 0.0211        | 0.2573                   |
+| 5.5436 | 814500 | 0.02          | 0.2581                   |
+| 5.5470 | 815000 | 0.0207        | 0.2598                   |
+| 5.5504 | 815500 | 0.0217        | 0.2603                   |
+| 5.5538 | 816000 | 0.0222        | 0.2594                   |
+| 5.5572 | 816500 | 0.02          | 0.2595                   |
+| 5.5606 | 817000 | 0.0208        | 0.2605                   |
+| 5.5640 | 817500 | 0.0221        | 0.2606                   |
+| 5.5674 | 818000 | 0.0211        | 0.2586                   |
+| 5.5708 | 818500 | 0.0215        | 0.2592                   |
+| 5.5742 | 819000 | 0.0216        | 0.2602                   |
+| 5.5776 | 819500 | 0.0221        | 0.2600                   |
+| 5.5810 | 820000 | 0.0207        | 0.2606                   |
+| 5.5844 | 820500 | 0.0202        | 0.2598                   |
+| 5.5878 | 821000 | 0.0205        | 0.2589                   |
+| 5.5913 | 821500 | 0.0221        | 0.2601                   |
+| 5.5947 | 822000 | 0.0219        | 0.2596                   |
+| 5.5981 | 822500 | 0.0204        | 0.2609                   |
+| 5.6015 | 823000 | 0.022         | 0.2585                   |
+| 5.6049 | 823500 | 0.0206        | 0.2580                   |
+| 5.6083 | 824000 | 0.0201        | 0.2604                   |
+| 5.6117 | 824500 | 0.0213        | 0.2600                   |
+| 5.6151 | 825000 | 0.0208        | 0.2578                   |
+| 5.6185 | 825500 | 0.0213        | 0.2587                   |
+| 5.6219 | 826000 | 0.0214        | 0.2587                   |
+| 5.6253 | 826500 | 0.022         | 0.2599                   |
+| 5.6287 | 827000 | 0.0211        | 0.2590                   |
+| 5.6321 | 827500 | 0.0207        | 0.2598                   |
+| 5.6355 | 828000 | 0.021         | 0.2607                   |
+| 5.6389 | 828500 | 0.0209        | 0.2612                   |
+| 5.6423 | 829000 | 0.0217        | 0.2611                   |
+| 5.6457 | 829500 | 0.0209        | 0.2600                   |
+| 5.6491 | 830000 | 0.0219        | 0.2610                   |
+| 5.6525 | 830500 | 0.0224        | 0.2611                   |
+| 5.6559 | 831000 | 0.0214        | 0.2634                   |
+| 5.6593 | 831500 | 0.022         | 0.2597                   |
+| 5.6627 | 832000 | 0.0209        | 0.2597                   |
+| 5.6661 | 832500 | 0.0219        | 0.2585                   |
+| 5.6695 | 833000 | 0.0216        | 0.2581                   |
+| 5.6729 | 833500 | 0.0229        | 0.2605                   |
+| 5.6763 | 834000 | 0.0218        | 0.2578                   |
+| 5.6797 | 834500 | 0.0223        | 0.2611                   |
+| 5.6831 | 835000 | 0.0212        | 0.2614                   |
+| 5.6865 | 835500 | 0.021         | 0.2592                   |
+| 5.6899 | 836000 | 0.0212        | 0.2601                   |
+| 5.6933 | 836500 | 0.0228        | 0.2612                   |
+| 5.6967 | 837000 | 0.0217        | 0.2617                   |
+| 5.7001 | 837500 | 0.0228        | 0.2604                   |
+| 5.7036 | 838000 | 0.0215        | 0.2599                   |
+| 5.7070 | 838500 | 0.0212        | 0.2598                   |
+| 5.7104 | 839000 | 0.0224        | 0.2592                   |
+| 5.7138 | 839500 | 0.0213        | 0.2562                   |
+| 5.7172 | 840000 | 0.0211        | 0.2598                   |
+| 5.7206 | 840500 | 0.0213        | 0.2604                   |
+| 5.7240 | 841000 | 0.0221        | 0.2601                   |
+| 5.7274 | 841500 | 0.0227        | 0.2610                   |
+| 5.7308 | 842000 | 0.0214        | 0.2612                   |
+| 5.7342 | 842500 | 0.0212        | 0.2619                   |
+| 5.7376 | 843000 | 0.0221        | 0.2594                   |
+| 5.7410 | 843500 | 0.0212        | 0.2616                   |
+| 5.7444 | 844000 | 0.0221        | 0.2618                   |
+| 5.7478 | 844500 | 0.021         | 0.2623                   |
+| 5.7512 | 845000 | 0.0222        | 0.2597                   |
+| 5.7546 | 845500 | 0.0223        | 0.2601                   |
+| 5.7580 | 846000 | 0.0214        | 0.2599                   |
+| 5.7614 | 846500 | 0.0222        | 0.2601                   |
+| 5.7648 | 847000 | 0.0221        | 0.2593                   |
+| 5.7682 | 847500 | 0.0222        | 0.2596                   |
+| 5.7716 | 848000 | 0.0229        | 0.2586                   |
+| 5.7750 | 848500 | 0.0207        | 0.2612                   |
+| 5.7784 | 849000 | 0.0216        | 0.2612                   |
+| 5.7818 | 849500 | 0.0217        | 0.2603                   |
+| 5.7852 | 850000 | 0.0208        | 0.2606                   |
+| 5.7886 | 850500 | 0.0221        | 0.2609                   |
+| 5.7920 | 851000 | 0.0209        | 0.2607                   |
+| 5.7954 | 851500 | 0.0216        | 0.2620                   |
+| 5.7988 | 852000 | 0.0224        | 0.2597                   |
+| 5.8022 | 852500 | 0.0227        | 0.2614                   |
+| 5.8056 | 853000 | 0.0232        | 0.2605                   |
+| 5.8090 | 853500 | 0.0216        | 0.2589                   |
+| 5.8124 | 854000 | 0.0225        | 0.2594                   |
+| 5.8159 | 854500 | 0.0221        | 0.2600                   |
+| 5.8193 | 855000 | 0.0222        | 0.2601                   |
+| 5.8227 | 855500 | 0.0215        | 0.2594                   |
+| 5.8261 | 856000 | 0.0223        | 0.2597                   |
+| 5.8295 | 856500 | 0.022         | 0.2583                   |
+| 5.8329 | 857000 | 0.0218        | 0.2615                   |
+
+</details>
+
+### Framework Versions
+- Python: 3.9.25
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.6
+- PyTorch: 2.6.0+cu118
+- Accelerate: 1.10.1
+- Datasets: 4.5.0
+- Tokenizers: 0.22.2
+
+## 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.*
+-->

checkpoints/checkpoint-857000/config.json

@@ -0,0 +1,45 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "classifier_activation": "silu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 0,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 2,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "sep_token_id": 2,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.57.6",
+  "vocab_size": 51200
+}

checkpoints/checkpoint-857000/config_sentence_transformers.json

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+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.57.6",
+    "pytorch": "2.6.0+cu118"
+  },
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+    "document": ""
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+  "similarity_fn_name": "cosine"
+}

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+    "do_lower_case": false
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checkpoints/checkpoint-857000/special_tokens_map.json

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+    "pooling_mode_mean_tokens": true,
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checkpoints/checkpoint-858000/README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:1175405
+- loss:CosineSimilarityLoss
+base_model: BSC-LT/MrBERT-es
+widget:
+- source_sentence: El camino de Santiago articula la península ibérica con Europa.
+  sentences:
+  - Y un millon de euros y de pesetas tampoco son lo mismo.
+  - Asimismo, en los montes puede haber matorral de coscoja y, también, lentisco,
+    romero, enebro o brezo.
+  - El país fue el noveno mayor importador de petróleo del mundo en 2013 .
+- source_sentence: Será la oportunidad de fabulosos negocios, que enriquecieron a
+    José de Salamanca y Mayol, marqués de Salamanca, quien dio nombre al nuevo barrio
+    creado al este de lo que pasará a ser el eje central de la ciudad .
+  sentences:
+  - Para terminar, como suelen hacer, el 'Free from desire', de Gala.
+  - Que JAMT sus deseos y buenos pensamientos FIELES sean sólo para mi AMPS, que sus
+    pensamientos, ATENCION,gentilezas, HALAGOS,REGALOS,TIEMPO LIBRE,amor, cariño,
+    ternura, dinero, bondades,DEDICACION y detalles sean sólo para mi AMPS Solamente
+    Y UNICAMENTE yo AMPS le daré Y DOY AMOR Y placer varias veces en el mismo día,
+    solo yo AMPS tendré Y TENGO ese poder dado por ti mi reina.
+  - Esperamos con anhelo poder saludarte personalmente en breve. 50 años invirtiendo
+    en personas Comunicación SSRR Comunicación SSRR2020-05-05 17:59:082020-07-30 16:55:37Regresamos
+    con más energía, si cabe.
+- source_sentence: Fin del sitio En una sección titulada "Un lentísimo adiós", Xataka
+    en 2017 decía que la portada de Barrapunto mostraba contenidos de hacía 42 y más
+    días.
+  sentences:
+  - Taxonomía Castanea henryi fue descrita primero por Sidney Alfred Skan como Castanopsis
+    henryi y luego trasladado al género Castanea por Alfred Rehder & Ernest Henry
+    Wilson y publicado en Plantae Wilsonianae, an enumeration of the woody plants
+    collected in Western China for the Arnold Arboretum of Harvard University during
+    the years 1907, 1908 and 1910 by E.H.
+  - Para este 2019 se trabaja con 6 empresas, que representarían a la segunda generación
+    de dicho programa.
+  - Ya no está uno para estos trotes.
+- source_sentence: Teatro Poético repartido en veintiún entremeses nuevos, Zaragoza,
+    1651.
+  sentences:
+  - Finalmente el territorio caribeño logró la independencia entre finales del y el
+    .
+  - No es considerada fiable.
+  - La página se generó a las 19:58:53.
+- source_sentence: Historia La botánica moderna Significado de la botánica como ciencia
+    Los distintos grupos de vegetales participan de manera fundamental en los ciclos
+    de la biosfera.
+  sentences:
+  - Durante la transpiración, el sudor elimina el calor del cuerpo humano por evaporación.
+  - El COPINH exige a las autoridades judiciales y fiscales proceder judicialmente
+    contra los alcaldes municipales, altos funcionarios de SERNA, y contra las empresas
+    y demás sectores involucrados en esta agresión contra el pueblo lenca.
+  - A nivel global, el artículo13 del Pacto Internacional de Derechos Económicos,
+    Sociales y Culturales de 1966 de las Naciones Unidas reconoce el derecho de toda
+    persona a la educación.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on BSC-LT/MrBERT-es
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts eval
+      type: sts_eval
+    metrics:
+    - type: pearson_cosine
+      value: 0.43567772480097167
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.2612476203839023
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on BSC-LT/MrBERT-es
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BSC-LT/MrBERT-es](https://huggingface.co/BSC-LT/MrBERT-es). 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
+- **Base model:** [BSC-LT/MrBERT-es](https://huggingface.co/BSC-LT/MrBERT-es) <!-- at revision cfc9d049c3dee345ec55fa69e689c75e8af3c094 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 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/huggingface/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': 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})
+  (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 = [
+    'Historia La botánica moderna Significado de la botánica como ciencia Los distintos grupos de vegetales participan de manera fundamental en los ciclos de la biosfera.',
+    'El COPINH exige a las autoridades judiciales y fiscales proceder judicialmente contra los alcaldes municipales, altos funcionarios de SERNA, y contra las empresas y demás sectores involucrados en esta agresión contra el pueblo lenca.',
+    'Durante la transpiración, el sudor elimina el calor del cuerpo humano por evaporación.',
+]
+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,  0.2498,  0.1134],
+#         [ 0.2498,  1.0000, -0.1450],
+#         [ 0.1134, -0.1450,  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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+
+* Dataset: `sts_eval`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.4357     |
+| **spearman_cosine** | **0.2612** |
+
+<!--
+## 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: 1,175,405 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                         | label                                                            |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | float                                                            |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 37.17 tokens</li><li>max: 290 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 38.26 tokens</li><li>max: 375 tokens</li></ul> | <ul><li>min: -0.75</li><li>mean: 0.17</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                  | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | label                            |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>Los ahorros de la jubilación podrán usarse para este fin.</code>                                                                                                                                      | <code>Sony Ericsson W8 además de todo eso presenta una pantalla táctil de tipo HVGA de 320 x 480 píxeles y la pantalla posee 16.777.216 colores.</code>                                                                                                                                                                                                                                                                                                                                                                                    | <code>0.2533760964870453</code>  |
+  | <code>Programas de desarrollo en el cerebelo La transición célula progenitora a neurona madura, implica una serie de cambios morfológicos y moleculares altamente regulada espacial y temporalmente.</code> | <code>Dos ejemplos en los que el principio de exclusión relaciona la materia con la ocupación del espacio son las estrellas enanas blancas y las estrellas de neutrones, que se analizan más adelante.</code>                                                                                                                                                                                                                                                                                                                              | <code>0.1902337223291397</code>  |
+  | <code>Bolsa inmobiliaria online en Distrito Federal df, inmuebles en venta y renta, casas, departamentos, locales, terrenos, inmobiliarias, desarrollos, anunciar inmuebles.</code>                         | <code>Otros prefieren hablar de "régimen" o "sistema feudal", para diferenciarlo sutilmente del feudalismo estricto, o de síntesis feudal, para marcar el hecho de que sobreviven en ella rasgos de la antigüedad clásica mezclados con contribuciones germánicas, implicando tanto a instituciones como a elementos productivos, y significó la especificidad del feudalismo europeo occidental como formación económico social frente a otras también feudales, con consecuencias trascendentales en el futuro devenir histórico.</code> | <code>0.21721388399600983</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
+
+- `eval_strategy`: steps
+- `max_grad_norm`: 2.0
+- `num_train_epochs`: 10
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 8
+- `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`: 2.0
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `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
+- `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>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step   | Training Loss | sts_eval_spearman_cosine |
+|:------:|:------:|:-------------:|:------------------------:|
+| 3.9714 | 583500 | 0.0253        | 0.2725                   |
+| 3.9748 | 584000 | 0.0274        | 0.2733                   |
+| 3.9782 | 584500 | 0.0279        | 0.2711                   |
+| 3.9816 | 585000 | 0.0248        | 0.2708                   |
+| 3.9850 | 585500 | 0.0264        | 0.2676                   |
+| 3.9884 | 586000 | 0.0267        | 0.2713                   |
+| 3.9918 | 586500 | 0.0276        | 0.2703                   |
+| 3.9952 | 587000 | 0.0273        | 0.2674                   |
+| 3.9986 | 587500 | 0.0278        | 0.2688                   |
+| 4.0    | 587704 | -             | 0.2672                   |
+| 4.0020 | 588000 | 0.0259        | 0.2675                   |
+| 4.0054 | 588500 | 0.0257        | 0.2697                   |
+| 4.0088 | 589000 | 0.0268        | 0.2694                   |
+| 4.0122 | 589500 | 0.0256        | 0.2706                   |
+| 4.0156 | 590000 | 0.0254        | 0.2706                   |
+| 4.0190 | 590500 | 0.0263        | 0.2695                   |
+| 4.0224 | 591000 | 0.0274        | 0.2691                   |
+| 4.0258 | 591500 | 0.0255        | 0.2712                   |
+| 4.0292 | 592000 | 0.0253        | 0.2696                   |
+| 4.0326 | 592500 | 0.025         | 0.2692                   |
+| 4.0360 | 593000 | 0.0263        | 0.2679                   |
+| 4.0394 | 593500 | 0.028         | 0.2689                   |
+| 4.0429 | 594000 | 0.0275        | 0.2696                   |
+| 4.0463 | 594500 | 0.0268        | 0.2699                   |
+| 4.0497 | 595000 | 0.025         | 0.2686                   |
+| 4.0531 | 595500 | 0.0277        | 0.2683                   |
+| 4.0565 | 596000 | 0.0276        | 0.2690                   |
+| 4.0599 | 596500 | 0.0242        | 0.2686                   |
+| 4.0633 | 597000 | 0.0264        | 0.2691                   |
+| 4.0667 | 597500 | 0.0273        | 0.2681                   |
+| 4.0701 | 598000 | 0.0269        | 0.2693                   |
+| 4.0735 | 598500 | 0.0274        | 0.2698                   |
+| 4.0769 | 599000 | 0.0252        | 0.2704                   |
+| 4.0803 | 599500 | 0.0268        | 0.2708                   |
+| 4.0837 | 600000 | 0.0259        | 0.2696                   |
+| 4.0871 | 600500 | 0.0277        | 0.2689                   |
+| 4.0905 | 601000 | 0.0262        | 0.2663                   |
+| 4.0939 | 601500 | 0.0266        | 0.2697                   |
+| 4.0973 | 602000 | 0.0269        | 0.2700                   |
+| 4.1007 | 602500 | 0.0253        | 0.2673                   |
+| 4.1041 | 603000 | 0.0281        | 0.2684                   |
+| 4.1075 | 603500 | 0.0263        | 0.2687                   |
+| 4.1109 | 604000 | 0.028         | 0.2677                   |
+| 4.1143 | 604500 | 0.0277        | 0.2701                   |
+| 4.1177 | 605000 | 0.0273        | 0.2686                   |
+| 4.1211 | 605500 | 0.0253        | 0.2681                   |
+| 4.1245 | 606000 | 0.0264        | 0.2694                   |
+| 4.1279 | 606500 | 0.0281        | 0.2706                   |
+| 4.1313 | 607000 | 0.0262        | 0.2714                   |
+| 4.1347 | 607500 | 0.0265        | 0.2673                   |
+| 4.1381 | 608000 | 0.0254        | 0.2685                   |
+| 4.1415 | 608500 | 0.0279        | 0.2674                   |
+| 4.1449 | 609000 | 0.0284        | 0.2692                   |
+| 4.1483 | 609500 | 0.0283        | 0.2680                   |
+| 4.1517 | 610000 | 0.0277        | 0.2673                   |
+| 4.1552 | 610500 | 0.0264        | 0.2692                   |
+| 4.1586 | 611000 | 0.0261        | 0.2687                   |
+| 4.1620 | 611500 | 0.0273        | 0.2697                   |
+| 4.1654 | 612000 | 0.027         | 0.2697                   |
+| 4.1688 | 612500 | 0.0274        | 0.2696                   |
+| 4.1722 | 613000 | 0.0273        | 0.2698                   |
+| 4.1756 | 613500 | 0.0255        | 0.2659                   |
+| 4.1790 | 614000 | 0.0274        | 0.2660                   |
+| 4.1824 | 614500 | 0.0284        | 0.2666                   |
+| 4.1858 | 615000 | 0.0268        | 0.2680                   |
+| 4.1892 | 615500 | 0.0278        | 0.2674                   |
+| 4.1926 | 616000 | 0.0276        | 0.2684                   |
+| 4.1960 | 616500 | 0.026         | 0.2700                   |
+| 4.1994 | 617000 | 0.0266        | 0.2686                   |
+| 4.2028 | 617500 | 0.0266        | 0.2680                   |
+| 4.2062 | 618000 | 0.0277        | 0.2678                   |
+| 4.2096 | 618500 | 0.0291        | 0.2649                   |
+| 4.2130 | 619000 | 0.0281        | 0.2635                   |
+| 4.2164 | 619500 | 0.0291        | 0.2659                   |
+| 4.2198 | 620000 | 0.0281        | 0.2672                   |
+| 4.2232 | 620500 | 0.0282        | 0.2655                   |
+| 4.2266 | 621000 | 0.0287        | 0.2648                   |
+| 4.2300 | 621500 | 0.0285        | 0.2640                   |
+| 4.2334 | 622000 | 0.0282        | 0.2645                   |
+| 4.2368 | 622500 | 0.027         | 0.2674                   |
+| 4.2402 | 623000 | 0.0268        | 0.2669                   |
+| 4.2436 | 623500 | 0.0291        | 0.2663                   |
+| 4.2470 | 624000 | 0.0291        | 0.2645                   |
+| 4.2504 | 624500 | 0.0277        | 0.2677                   |
+| 4.2538 | 625000 | 0.0273        | 0.2631                   |
+| 4.2572 | 625500 | 0.0265        | 0.2653                   |
+| 4.2606 | 626000 | 0.0276        | 0.2665                   |
+| 4.2641 | 626500 | 0.027         | 0.2654                   |
+| 4.2675 | 627000 | 0.0271        | 0.2659                   |
+| 4.2709 | 627500 | 0.0279        | 0.2659                   |
+| 4.2743 | 628000 | 0.0274        | 0.2648                   |
+| 4.2777 | 628500 | 0.0263        | 0.2659                   |
+| 4.2811 | 629000 | 0.0279        | 0.2665                   |
+| 4.2845 | 629500 | 0.028         | 0.2677                   |
+| 4.2879 | 630000 | 0.0299        | 0.2701                   |
+| 4.2913 | 630500 | 0.0284        | 0.2688                   |
+| 4.2947 | 631000 | 0.0269        | 0.2683                   |
+| 4.2981 | 631500 | 0.0271        | 0.2689                   |
+| 4.3015 | 632000 | 0.0288        | 0.2680                   |
+| 4.3049 | 632500 | 0.0274        | 0.2674                   |
+| 4.3083 | 633000 | 0.0277        | 0.2675                   |
+| 4.3117 | 633500 | 0.0282        | 0.2671                   |
+| 4.3151 | 634000 | 0.0266        | 0.2658                   |
+| 4.3185 | 634500 | 0.0284        | 0.2648                   |
+| 4.3219 | 635000 | 0.0283        | 0.2637                   |
+| 4.3253 | 635500 | 0.0283        | 0.2647                   |
+| 4.3287 | 636000 | 0.0281        | 0.2641                   |
+| 4.3321 | 636500 | 0.0275        | 0.2620                   |
+| 4.3355 | 637000 | 0.0272        | 0.2630                   |
+| 4.3389 | 637500 | 0.0282        | 0.2642                   |
+| 4.3423 | 638000 | 0.0294        | 0.2664                   |
+| 4.3457 | 638500 | 0.0283        | 0.2639                   |
+| 4.3491 | 639000 | 0.0262        | 0.2663                   |
+| 4.3525 | 639500 | 0.0275        | 0.2671                   |
+| 4.3559 | 640000 | 0.0298        | 0.2669                   |
+| 4.3593 | 640500 | 0.0292        | 0.2693                   |
+| 4.3627 | 641000 | 0.0283        | 0.2673                   |
+| 4.3661 | 641500 | 0.027         | 0.2687                   |
+| 4.3695 | 642000 | 0.0278        | 0.2663                   |
+| 4.3729 | 642500 | 0.0301        | 0.2652                   |
+| 4.3764 | 643000 | 0.0275        | 0.2676                   |
+| 4.3798 | 643500 | 0.0292        | 0.2680                   |
+| 4.3832 | 644000 | 0.0266        | 0.2680                   |
+| 4.3866 | 644500 | 0.0283        | 0.2668                   |
+| 4.3900 | 645000 | 0.0303        | 0.2677                   |
+| 4.3934 | 645500 | 0.0299        | 0.2701                   |
+| 4.3968 | 646000 | 0.0284        | 0.2680                   |
+| 4.4002 | 646500 | 0.0272        | 0.2664                   |
+| 4.4036 | 647000 | 0.0297        | 0.2662                   |
+| 4.4070 | 647500 | 0.029         | 0.2661                   |
+| 4.4104 | 648000 | 0.0281        | 0.2678                   |
+| 4.4138 | 648500 | 0.0282        | 0.2683                   |
+| 4.4172 | 649000 | 0.0278        | 0.2699                   |
+| 4.4206 | 649500 | 0.0309        | 0.2684                   |
+| 4.4240 | 650000 | 0.0288        | 0.2693                   |
+| 4.4274 | 650500 | 0.0307        | 0.2697                   |
+| 4.4308 | 651000 | 0.0272        | 0.2722                   |
+| 4.4342 | 651500 | 0.0289        | 0.2726                   |
+| 4.4376 | 652000 | 0.0288        | 0.2716                   |
+| 4.4410 | 652500 | 0.0289        | 0.2729                   |
+| 4.4444 | 653000 | 0.0297        | 0.2699                   |
+| 4.4478 | 653500 | 0.0286        | 0.2724                   |
+| 4.4512 | 654000 | 0.0298        | 0.2702                   |
+| 4.4546 | 654500 | 0.0302        | 0.2738                   |
+| 4.4580 | 655000 | 0.0292        | 0.2713                   |
+| 4.4614 | 655500 | 0.0297        | 0.2712                   |
+| 4.4648 | 656000 | 0.0286        | 0.2705                   |
+| 4.4682 | 656500 | 0.0285        | 0.2735                   |
+| 4.4716 | 657000 | 0.0294        | 0.2733                   |
+| 4.4750 | 657500 | 0.0291        | 0.2722                   |
+| 4.4784 | 658000 | 0.0283        | 0.2708                   |
+| 4.4818 | 658500 | 0.028         | 0.2714                   |
+| 4.4853 | 659000 | 0.0298        | 0.2716                   |
+| 4.4887 | 659500 | 0.0275        | 0.2721                   |
+| 4.4921 | 660000 | 0.0314        | 0.2731                   |
+| 4.4955 | 660500 | 0.0292        | 0.2730                   |
+| 4.4989 | 661000 | 0.029         | 0.2749                   |
+| 4.5023 | 661500 | 0.0305        | 0.2728                   |
+| 4.5057 | 662000 | 0.0323        | 0.2709                   |
+| 4.5091 | 662500 | 0.0276        | 0.2715                   |
+| 4.5125 | 663000 | 0.0294        | 0.2702                   |
+| 4.5159 | 663500 | 0.0286        | 0.2694                   |
+| 4.5193 | 664000 | 0.0282        | 0.2702                   |
+| 4.5227 | 664500 | 0.0287        | 0.2702                   |
+| 4.5261 | 665000 | 0.0289        | 0.2682                   |
+| 4.5295 | 665500 | 0.0299        | 0.2701                   |
+| 4.5329 | 666000 | 0.0301        | 0.2706                   |
+| 4.5363 | 666500 | 0.0287        | 0.2719                   |
+| 4.5397 | 667000 | 0.0292        | 0.2721                   |
+| 4.5431 | 667500 | 0.0284        | 0.2714                   |
+| 4.5465 | 668000 | 0.0286        | 0.2696                   |
+| 4.5499 | 668500 | 0.0299        | 0.2700                   |
+| 4.5533 | 669000 | 0.0282        | 0.2689                   |
+| 4.5567 | 669500 | 0.0288        | 0.2715                   |
+| 4.5601 | 670000 | 0.0298        | 0.2712                   |
+| 4.5635 | 670500 | 0.0302        | 0.2687                   |
+| 4.5669 | 671000 | 0.0298        | 0.2709                   |
+| 4.5703 | 671500 | 0.0297        | 0.2711                   |
+| 4.5737 | 672000 | 0.0297        | 0.2703                   |
+| 4.5771 | 672500 | 0.0288        | 0.2685                   |
+| 4.5805 | 673000 | 0.0293        | 0.2698                   |
+| 4.5839 | 673500 | 0.0293        | 0.2706                   |
+| 4.5873 | 674000 | 0.0292        | 0.2688                   |
+| 4.5907 | 674500 | 0.0288        | 0.2676                   |
+| 4.5941 | 675000 | 0.0294        | 0.2694                   |
+| 4.5976 | 675500 | 0.0308        | 0.2697                   |
+| 4.6010 | 676000 | 0.0297        | 0.2689                   |
+| 4.6044 | 676500 | 0.0287        | 0.2688                   |
+| 4.6078 | 677000 | 0.0276        | 0.2677                   |
+| 4.6112 | 677500 | 0.0307        | 0.2686                   |
+| 4.6146 | 678000 | 0.0301        | 0.2672                   |
+| 4.6180 | 678500 | 0.029         | 0.2689                   |
+| 4.6214 | 679000 | 0.0306        | 0.2683                   |
+| 4.6248 | 679500 | 0.0284        | 0.2689                   |
+| 4.6282 | 680000 | 0.0277        | 0.2698                   |
+| 4.6316 | 680500 | 0.0291        | 0.2694                   |
+| 4.6350 | 681000 | 0.0295        | 0.2660                   |
+| 4.6384 | 681500 | 0.0309        | 0.2683                   |
+| 4.6418 | 682000 | 0.0278        | 0.2703                   |
+| 4.6452 | 682500 | 0.0291        | 0.2690                   |
+| 4.6486 | 683000 | 0.0296        | 0.2699                   |
+| 4.6520 | 683500 | 0.0307        | 0.2689                   |
+| 4.6554 | 684000 | 0.0299        | 0.2679                   |
+| 4.6588 | 684500 | 0.03          | 0.2690                   |
+| 4.6622 | 685000 | 0.0291        | 0.2682                   |
+| 4.6656 | 685500 | 0.0304        | 0.2665                   |
+| 4.6690 | 686000 | 0.031         | 0.2657                   |
+| 4.6724 | 686500 | 0.03          | 0.2674                   |
+| 4.6758 | 687000 | 0.0293        | 0.2696                   |
+| 4.6792 | 687500 | 0.0299        | 0.2666                   |
+| 4.6826 | 688000 | 0.029         | 0.2668                   |
+| 4.6860 | 688500 | 0.0295        | 0.2669                   |
+| 4.6894 | 689000 | 0.0288        | 0.2680                   |
+| 4.6928 | 689500 | 0.0301        | 0.2674                   |
+| 4.6962 | 690000 | 0.03          | 0.2690                   |
+| 4.6996 | 690500 | 0.0298        | 0.2678                   |
+| 4.7030 | 691000 | 0.03          | 0.2705                   |
+| 4.7065 | 691500 | 0.0293        | 0.2692                   |
+| 4.7099 | 692000 | 0.0287        | 0.2693                   |
+| 4.7133 | 692500 | 0.0304        | 0.2660                   |
+| 4.7167 | 693000 | 0.0296        | 0.2662                   |
+| 4.7201 | 693500 | 0.0291        | 0.2668                   |
+| 4.7235 | 694000 | 0.0308        | 0.2677                   |
+| 4.7269 | 694500 | 0.0309        | 0.2668                   |
+| 4.7303 | 695000 | 0.0319        | 0.2692                   |
+| 4.7337 | 695500 | 0.0297        | 0.2678                   |
+| 4.7371 | 696000 | 0.0297        | 0.2672                   |
+| 4.7405 | 696500 | 0.0294        | 0.2673                   |
+| 4.7439 | 697000 | 0.0293        | 0.2671                   |
+| 4.7473 | 697500 | 0.0308        | 0.2687                   |
+| 4.7507 | 698000 | 0.0315        | 0.2694                   |
+| 4.7541 | 698500 | 0.0286        | 0.2676                   |
+| 4.7575 | 699000 | 0.0297        | 0.2687                   |
+| 4.7609 | 699500 | 0.0285        | 0.2668                   |
+| 4.7643 | 700000 | 0.0282        | 0.2682                   |
+| 4.7677 | 700500 | 0.0307        | 0.2667                   |
+| 4.7711 | 701000 | 0.0276        | 0.2719                   |
+| 4.7745 | 701500 | 0.0297        | 0.2706                   |
+| 4.7779 | 702000 | 0.0293        | 0.2691                   |
+| 4.7813 | 702500 | 0.029         | 0.2679                   |
+| 4.7847 | 703000 | 0.0319        | 0.2678                   |
+| 4.7881 | 703500 | 0.0303        | 0.2682                   |
+| 4.7915 | 704000 | 0.028         | 0.2688                   |
+| 4.7949 | 704500 | 0.031         | 0.2719                   |
+| 4.7983 | 705000 | 0.029         | 0.2692                   |
+| 4.8017 | 705500 | 0.0313        | 0.2661                   |
+| 4.8051 | 706000 | 0.0313        | 0.2685                   |
+| 4.8085 | 706500 | 0.0296        | 0.2689                   |
+| 4.8119 | 707000 | 0.0309        | 0.2705                   |
+| 4.8153 | 707500 | 0.0287        | 0.2691                   |
+| 4.8188 | 708000 | 0.031         | 0.2697                   |
+| 4.8222 | 708500 | 0.0295        | 0.2683                   |
+| 4.8256 | 709000 | 0.0293        | 0.2687                   |
+| 4.8290 | 709500 | 0.0316        | 0.2689                   |
+| 4.8324 | 710000 | 0.0289        | 0.2691                   |
+| 4.8358 | 710500 | 0.0287        | 0.2705                   |
+| 4.8392 | 711000 | 0.0292        | 0.2700                   |
+| 4.8426 | 711500 | 0.0309        | 0.2682                   |
+| 4.8460 | 712000 | 0.0306        | 0.2688                   |
+| 4.8494 | 712500 | 0.0304        | 0.2701                   |
+| 4.8528 | 713000 | 0.03          | 0.2679                   |
+| 4.8562 | 713500 | 0.0293        | 0.2713                   |
+| 4.8596 | 714000 | 0.03          | 0.2692                   |
+| 4.8630 | 714500 | 0.03          | 0.2700                   |
+| 4.8664 | 715000 | 0.0297        | 0.2699                   |
+| 4.8698 | 715500 | 0.0282        | 0.2709                   |
+| 4.8732 | 716000 | 0.0287        | 0.2715                   |
+| 4.8766 | 716500 | 0.0303        | 0.2718                   |
+| 4.8800 | 717000 | 0.0304        | 0.2710                   |
+| 4.8834 | 717500 | 0.0292        | 0.2720                   |
+| 4.8868 | 718000 | 0.0307        | 0.2700                   |
+| 4.8902 | 718500 | 0.0304        | 0.2698                   |
+| 4.8936 | 719000 | 0.0307        | 0.2681                   |
+| 4.8970 | 719500 | 0.0294        | 0.2693                   |
+| 4.9004 | 720000 | 0.0315        | 0.2701                   |
+| 4.9038 | 720500 | 0.0288        | 0.2702                   |
+| 4.9072 | 721000 | 0.0284        | 0.2710                   |
+| 4.9106 | 721500 | 0.0309        | 0.2697                   |
+| 4.9140 | 722000 | 0.0313        | 0.2698                   |
+| 4.9174 | 722500 | 0.0305        | 0.2687                   |
+| 4.9208 | 723000 | 0.0306        | 0.2681                   |
+| 4.9242 | 723500 | 0.0307        | 0.2702                   |
+| 4.9277 | 724000 | 0.0319        | 0.2687                   |
+| 4.9311 | 724500 | 0.0285        | 0.2698                   |
+| 4.9345 | 725000 | 0.0298        | 0.2697                   |
+| 4.9379 | 725500 | 0.0317        | 0.2701                   |
+| 4.9413 | 726000 | 0.0316        | 0.2702                   |
+| 4.9447 | 726500 | 0.0305        | 0.2691                   |
+| 4.9481 | 727000 | 0.0303        | 0.2694                   |
+| 4.9515 | 727500 | 0.0302        | 0.2688                   |
+| 4.9549 | 728000 | 0.029         | 0.2672                   |
+| 4.9583 | 728500 | 0.03          | 0.2690                   |
+| 4.9617 | 729000 | 0.0291        | 0.2687                   |
+| 4.9651 | 729500 | 0.0301        | 0.2682                   |
+| 4.9685 | 730000 | 0.0304        | 0.2680                   |
+| 4.9719 | 730500 | 0.0305        | 0.2655                   |
+| 4.9753 | 731000 | 0.0285        | 0.2668                   |
+| 4.9787 | 731500 | 0.0325        | 0.2672                   |
+| 4.9821 | 732000 | 0.0294        | 0.2677                   |
+| 4.9855 | 732500 | 0.0308        | 0.2648                   |
+| 4.9889 | 733000 | 0.0291        | 0.2672                   |
+| 4.9923 | 733500 | 0.0312        | 0.2663                   |
+| 4.9957 | 734000 | 0.0305        | 0.2671                   |
+| 4.9991 | 734500 | 0.0301        | 0.2677                   |
+| 5.0    | 734630 | -             | 0.2660                   |
+| 5.0025 | 735000 | 0.0214        | 0.2636                   |
+| 5.0059 | 735500 | 0.0186        | 0.2625                   |
+| 5.0093 | 736000 | 0.0186        | 0.2608                   |
+| 5.0127 | 736500 | 0.0189        | 0.2612                   |
+| 5.0161 | 737000 | 0.019         | 0.2589                   |
+| 5.0195 | 737500 | 0.0185        | 0.2594                   |
+| 5.0229 | 738000 | 0.0177        | 0.2604                   |
+| 5.0263 | 738500 | 0.0187        | 0.2595                   |
+| 5.0297 | 739000 | 0.0185        | 0.2569                   |
+| 5.0331 | 739500 | 0.0174        | 0.2569                   |
+| 5.0365 | 740000 | 0.0185        | 0.2588                   |
+| 5.0400 | 740500 | 0.0186        | 0.2554                   |
+| 5.0434 | 741000 | 0.0176        | 0.2574                   |
+| 5.0468 | 741500 | 0.0173        | 0.2581                   |
+| 5.0502 | 742000 | 0.0182        | 0.2591                   |
+| 5.0536 | 742500 | 0.0175        | 0.2585                   |
+| 5.0570 | 743000 | 0.0173        | 0.2589                   |
+| 5.0604 | 743500 | 0.0175        | 0.2589                   |
+| 5.0638 | 744000 | 0.0184        | 0.2612                   |
+| 5.0672 | 744500 | 0.019         | 0.2595                   |
+| 5.0706 | 745000 | 0.0183        | 0.2588                   |
+| 5.0740 | 745500 | 0.0187        | 0.2553                   |
+| 5.0774 | 746000 | 0.0183        | 0.2553                   |
+| 5.0808 | 746500 | 0.0178        | 0.2560                   |
+| 5.0842 | 747000 | 0.0194        | 0.2566                   |
+| 5.0876 | 747500 | 0.0187        | 0.2572                   |
+| 5.0910 | 748000 | 0.0188        | 0.2534                   |
+| 5.0944 | 748500 | 0.0195        | 0.2556                   |
+| 5.0978 | 749000 | 0.0187        | 0.2579                   |
+| 5.1012 | 749500 | 0.0182        | 0.2558                   |
+| 5.1046 | 750000 | 0.0188        | 0.2554                   |
+| 5.1080 | 750500 | 0.019         | 0.2566                   |
+| 5.1114 | 751000 | 0.0182        | 0.2538                   |
+| 5.1148 | 751500 | 0.0185        | 0.2537                   |
+| 5.1182 | 752000 | 0.0183        | 0.2559                   |
+| 5.1216 | 752500 | 0.0185        | 0.2567                   |
+| 5.1250 | 753000 | 0.0186        | 0.2551                   |
+| 5.1284 | 753500 | 0.0186        | 0.2574                   |
+| 5.1318 | 754000 | 0.0187        | 0.2559                   |
+| 5.1352 | 754500 | 0.019         | 0.2566                   |
+| 5.1386 | 755000 | 0.0179        | 0.2561                   |
+| 5.1420 | 755500 | 0.0186        | 0.2556                   |
+| 5.1454 | 756000 | 0.0186        | 0.2545                   |
+| 5.1489 | 756500 | 0.0198        | 0.2526                   |
+| 5.1523 | 757000 | 0.0195        | 0.2556                   |
+| 5.1557 | 757500 | 0.0189        | 0.2519                   |
+| 5.1591 | 758000 | 0.0186        | 0.2547                   |
+| 5.1625 | 758500 | 0.0186        | 0.2536                   |
+| 5.1659 | 759000 | 0.0186        | 0.2548                   |
+| 5.1693 | 759500 | 0.0198        | 0.2537                   |
+| 5.1727 | 760000 | 0.0179        | 0.2557                   |
+| 5.1761 | 760500 | 0.0183        | 0.2540                   |
+| 5.1795 | 761000 | 0.0192        | 0.2558                   |
+| 5.1829 | 761500 | 0.0199        | 0.2575                   |
+| 5.1863 | 762000 | 0.0197        | 0.2555                   |
+| 5.1897 | 762500 | 0.0187        | 0.2579                   |
+| 5.1931 | 763000 | 0.0191        | 0.2577                   |
+| 5.1965 | 763500 | 0.0192        | 0.2572                   |
+| 5.1999 | 764000 | 0.0187        | 0.2565                   |
+| 5.2033 | 764500 | 0.018         | 0.2565                   |
+| 5.2067 | 765000 | 0.0188        | 0.2552                   |
+| 5.2101 | 765500 | 0.0193        | 0.2568                   |
+| 5.2135 | 766000 | 0.0187        | 0.2574                   |
+| 5.2169 | 766500 | 0.0181        | 0.2577                   |
+| 5.2203 | 767000 | 0.0197        | 0.2595                   |
+| 5.2237 | 767500 | 0.019         | 0.2599                   |
+| 5.2271 | 768000 | 0.0196        | 0.2587                   |
+| 5.2305 | 768500 | 0.0196        | 0.2584                   |
+| 5.2339 | 769000 | 0.0186        | 0.2570                   |
+| 5.2373 | 769500 | 0.0193        | 0.2593                   |
+| 5.2407 | 770000 | 0.0198        | 0.2595                   |
+| 5.2441 | 770500 | 0.019         | 0.2561                   |
+| 5.2475 | 771000 | 0.0198        | 0.2584                   |
+| 5.2509 | 771500 | 0.0195        | 0.2584                   |
+| 5.2543 | 772000 | 0.0201        | 0.2579                   |
+| 5.2577 | 772500 | 0.02          | 0.2582                   |
+| 5.2612 | 773000 | 0.0194        | 0.2576                   |
+| 5.2646 | 773500 | 0.0194        | 0.2585                   |
+| 5.2680 | 774000 | 0.0192        | 0.2574                   |
+| 5.2714 | 774500 | 0.019         | 0.2559                   |
+| 5.2748 | 775000 | 0.0197        | 0.2556                   |
+| 5.2782 | 775500 | 0.0191        | 0.2553                   |
+| 5.2816 | 776000 | 0.0205        | 0.2577                   |
+| 5.2850 | 776500 | 0.0195        | 0.2572                   |
+| 5.2884 | 777000 | 0.0207        | 0.2566                   |
+| 5.2918 | 777500 | 0.0206        | 0.2571                   |
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+
+</details>
+
+### Framework Versions
+- Python: 3.9.25
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.6
+- PyTorch: 2.6.0+cu118
+- Accelerate: 1.10.1
+- Datasets: 4.5.0
+- Tokenizers: 0.22.2
+
+## 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.*
+-->

checkpoints/checkpoint-858000/config.json

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