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README.md

@@ -5,67 +5,59 @@ tags:
 - feature-extraction
 - dense
 - generated_from_trainer
-- dataset_size:1822829
+- dataset_size:1175405
 - loss:CosineSimilarityLoss
 base_model: BSC-LT/MrBERT-es
 widget:
-- source_sentence: 'Giordano Bruno fue uno de los primeros pensadores que prefiguró
-    las ideas modernas: decía que la creación es infinita, no hay centro ni límites
-    –ni Dios ni hombre–, todo es movimiento, dinamismo.'
+- source_sentence: El camino de Santiago articula la península ibérica con Europa.
   sentences:
-  - Representante del pragmatismo, John Dewey, en Arte como experiencia , definió
-    el arte como "culminación de la naturaleza", defendiendo que la base de la estética
-    es la experiencia sensorial.
-  - Una niña vuela.
-  - 'Desde gestos Multi-Touch, Mission Control, pasando por el soporte de pantalla
-    completa y la integración del Launchpad, Parallels Desktop le hará creer que sus
-    aplicaciones Windows se concibieron para Mac.La integración de Mountain Lion le
-    proporciona los siguientes beneficios: El nuevo Presentation Wizard le permite
-    realizar presentaciones de forma fácil y elegante mediante su Mac en cualquier
-    monitor o proyector externo.'
-- source_sentence: El camino budista sirve para que la persona pueda liberarse de
-    esa cadena de causas y efectos.
+  - 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:
-  - Nada bueno sale de "lo fácil".
-  - Esos permisos, que deben renovar cada dos años, además de protegerlos frente a
-    la deportación les permiten trabajar.
-  - Quizá por la centralidad que la palabra tiene en la teoría de la antropología,
-    el término ha sido desarrollado de diversas maneras, que suponen el uso de una
-    metodología analítica basada en premisas que en ocasiones distan mucho las unas
-    de las otras.
-- source_sentence: Los privilegios que los defiendan los privilegiados.
+  - 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:
-  - El 29 de mayo de 1453 la ciudad Bizantina de Constantinopla Cae a manos de los
-    Turcos Otomanos.
-  - Cuando el pensar se para, de repente, en una particular constelación que se halle
-    saturada de tensiones, se le produce un shock mediante el cual él se cristaliza
-    como mónada.
-  - Como no podía ser de otra manera y ante las expectativas generadas por tan gran
-    inversión, desde el Ayuntamiento de Almuñécar se pretende que las necesidades
-    de personal futuras vinculadas al proyecto Bahía Fenicia o a cualquier otro proyecto
-    empresarial se satisfagan en su totalidad con mano de obra local.
-- source_sentence: Pero a mediados de 1990, el gobierno del Presidente Carlos Saúl
-    Menem los vendió a operadores extranjeros como el Citibank, de Nueva York, y el
-    Fleet Bank, de Boston.
+  - 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:
-  - El primer fanzine de medios de comunicación fue una publicación fan de Star Trek
-    llamada Spockanalia, publicada en septiembre de 1967 por miembros del grupo Lunarians.
-  - En San Francisco a 81 kilometros al sudoeste hay 3 estaciones entre las marcas
-    Blanca y ESSO.
-  - Es en ese marco que el mismo lunes que la Presidenta emitió su parecer por Facebook.
-- source_sentence: También ha influido en las palabras de las lenguas modernas debido
-    a que durante muchos siglos, después de la caída del Imperio romano, continuó
-    usándose en toda Europa como lingua franca para las ciencias y la política, sin
-    ser seriamente amenazada en esa función por otras lenguas en auge , hasta prácticamente
-    el .
+  - 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:
-  - Esta fue inaugurada por el Presidente Gabriel González Videla que arribó en el
-    transporte "Presidente Pinto" el 18 de febrero de 1948 y recibió el nombre de
-    General Bernardo O ́Higgins.
-  - Por otro lado, los noxii eran condenados a pelear en la arena con poco o ningún
-    entrenamiento, a menudo desarmados y sin ninguna expectativa de supervivencia.
-  - La palabra árabe kīmiyaˀ, sin el artículo, ha dado lugar a «química» en castellano
-    y otras lenguas, y al-kīmiyaˀ significa, en árabe moderno, «la química».
+  - 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:
@@ -82,10 +74,10 @@ model-index:
       type: sts_eval
     metrics:
     - type: pearson_cosine
-      value: 0.4921118528951735
+      value: 0.4667587301064259
       name: Pearson Cosine
     - type: spearman_cosine
-      value: 0.28487997818063493
+      value: 0.2738305461400082
       name: Spearman Cosine
 ---
 
@@ -139,9 +131,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
-    'También ha influido en las palabras de las lenguas modernas debido a que durante muchos siglos, después de la caída del Imperio romano, continuó usándose en toda Europa como lingua franca para las ciencias y la política, sin ser seriamente amenazada en esa función por otras lenguas en auge , hasta prácticamente el .',
-    'La palabra árabe kīmiyaˀ, sin el artículo, ha dado lugar a «química» en castellano y otras lenguas, y al-kīmiyaˀ significa, en árabe moderno, «la química».',
-    'Esta fue inaugurada por el Presidente Gabriel González Videla que arribó en el transporte "Presidente Pinto" el 18 de febrero de 1948 y recibió el nombre de General Bernardo O ́Higgins.',
+    '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)
@@ -150,9 +142,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 0.3905, 0.0918],
-#         [0.3905, 1.0000, 0.0894],
-#         [0.0918, 0.0894, 1.0000]])
+# tensor([[ 1.0000,  0.1673,  0.1974],
+#         [ 0.1673,  1.0000, -0.0618],
+#         [ 0.1974, -0.0618,  1.0000]])
 ```
 
 <!--
@@ -190,8 +182,8 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| pearson_cosine      | 0.4921     |
-| **spearman_cosine** | **0.2849** |
+| pearson_cosine      | 0.4668     |
+| **spearman_cosine** | **0.2738** |
 
 <!--
 ## Bias, Risks and Limitations
@@ -211,19 +203,19 @@ You can finetune this model on your own dataset.
 
 #### Unnamed Dataset
 
-* Size: 1,822,829 training samples
+* 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: 4 tokens</li><li>mean: 37.7 tokens</li><li>max: 476 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 37.84 tokens</li><li>max: 415 tokens</li></ul> | <ul><li>min: -0.81</li><li>mean: 0.18</li><li>max: 1.0</li></ul> |
+  |         | 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>Incluyo a continuación un link donde se pueden ver fotos de sus apartamentos.</code>                                                                | <code>Se encuentra disponible y desarrollado por el W3C a través de las Pautas de Accesibilidad al Contenido Web 1.0 WCAG , aunque muchos países tienen especificaciones propias, como es el caso de España con la Norma UNE 139803.</code> | <code>0.018207663670182228</code> |
-  | <code>Algunas de esas críticas están basadas en la carencia de evidencias, de fuentes externas, que confirmen lo que la Biblia asevera.</code>            | <code>Asimismo Hotel Miramar Barcelona S.A. no se hace responsable de la transmisión de cualquier virus o perjuicio en su equipo que pueda producirse a raíz del acceso a la presente Web.</code>                                           | <code>-0.47830161452293396</code> |
-  | <code>En estos territorios el nombre oficial de la lengua es «catalán», salvo en la Comunidad Valenciana, donde el nombre oficial es «valenciano».</code> | <code>Los socios de este club pueden disponer de las instalaciones que tienen para realizar eventos o encuentros de negocios.</code>                                                                                                        | <code>0.23044659197330475</code>  |
+  | 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
   {
@@ -236,7 +228,7 @@ You can finetune this model on your own dataset.
 
 - `eval_strategy`: steps
 - `max_grad_norm`: 2.0
-- `num_train_epochs`: 4
+- `num_train_epochs`: 10
 - `multi_dataset_batch_sampler`: round_robin
 
 #### All Hyperparameters
@@ -259,7 +251,7 @@ You can finetune this model on your own dataset.
 - `adam_beta2`: 0.999
 - `adam_epsilon`: 1e-08
 - `max_grad_norm`: 2.0
-- `num_train_epochs`: 4
+- `num_train_epochs`: 10
 - `max_steps`: -1
 - `lr_scheduler_type`: linear
 - `lr_scheduler_kwargs`: None
@@ -369,117 +361,150 @@ You can finetune this model on your own dataset.
 
 | Epoch  | Step   | Training Loss | sts_eval_spearman_cosine |
 |:------:|:------:|:-------------:|:------------------------:|
-| 1.9684 | 448500 | 0.0314        | 0.2740                   |
-| 1.9706 | 449000 | 0.0299        | 0.2744                   |
-| 1.9728 | 449500 | 0.0322        | 0.2764                   |
-| 1.9749 | 450000 | 0.0305        | 0.2718                   |
-| 1.9771 | 450500 | 0.0312        | 0.2736                   |
-| 1.9793 | 451000 | 0.0297        | 0.2723                   |
-| 1.9815 | 451500 | 0.0295        | 0.2717                   |
-| 1.9837 | 452000 | 0.0311        | 0.2710                   |
-| 1.9859 | 452500 | 0.0312        | 0.2703                   |
-| 1.9881 | 453000 | 0.0305        | 0.2728                   |
-| 1.9903 | 453500 | 0.0317        | 0.2711                   |
-| 1.9925 | 454000 | 0.0341        | 0.2727                   |
-| 1.9947 | 454500 | 0.0318        | 0.2736                   |
-| 1.9969 | 455000 | 0.0305        | 0.2737                   |
-| 1.9991 | 455500 | 0.0302        | 0.2740                   |
-| 2.0    | 455708 | -             | 0.2739                   |
-| 2.0013 | 456000 | 0.0303        | 0.2722                   |
-| 2.0035 | 456500 | 0.0301        | 0.2723                   |
-| 2.0057 | 457000 | 0.0293        | 0.2712                   |
-| 2.0079 | 457500 | 0.0301        | 0.2747                   |
-| 2.0101 | 458000 | 0.0304        | 0.2734                   |
-| 2.0123 | 458500 | 0.0321        | 0.2708                   |
-| 2.0144 | 459000 | 0.0298        | 0.2727                   |
-| 2.0166 | 459500 | 0.0312        | 0.2726                   |
-| 2.0188 | 460000 | 0.0292        | 0.2751                   |
-| 2.0210 | 460500 | 0.0306        | 0.2705                   |
-| 2.0232 | 461000 | 0.029         | 0.2733                   |
-| 2.0254 | 461500 | 0.0318        | 0.2748                   |
-| 2.0276 | 462000 | 0.0313        | 0.2752                   |
-| 2.0298 | 462500 | 0.0312        | 0.2756                   |
-| 2.0320 | 463000 | 0.0322        | 0.2727                   |
-| 2.0342 | 463500 | 0.0314        | 0.2724                   |
-| 2.0364 | 464000 | 0.0308        | 0.2745                   |
-| 2.0386 | 464500 | 0.0306        | 0.2733                   |
-| 2.0408 | 465000 | 0.0315        | 0.2703                   |
-| 2.0430 | 465500 | 0.0312        | 0.2708                   |
-| 2.0452 | 466000 | 0.0314        | 0.2737                   |
-| 2.0474 | 466500 | 0.0309        | 0.2709                   |
-| 2.0496 | 467000 | 0.0325        | 0.2702                   |
-| 2.0518 | 467500 | 0.0302        | 0.2711                   |
-| 2.0539 | 468000 | 0.0315        | 0.2719                   |
-| 2.0561 | 468500 | 0.0296        | 0.2707                   |
-| 2.0583 | 469000 | 0.0314        | 0.2688                   |
-| 2.0605 | 469500 | 0.0306        | 0.2707                   |
-| 2.0627 | 470000 | 0.0333        | 0.2721                   |
-| 2.0649 | 470500 | 0.0312        | 0.2720                   |
-| 2.0671 | 471000 | 0.0311        | 0.2718                   |
-| 2.0693 | 471500 | 0.0314        | 0.2733                   |
-| 2.0715 | 472000 | 0.0308        | 0.2701                   |
-| 2.0737 | 472500 | 0.0321        | 0.2701                   |
-| 2.0759 | 473000 | 0.0315        | 0.2722                   |
-| 2.0781 | 473500 | 0.0332        | 0.2709                   |
-| 2.0803 | 474000 | 0.0306        | 0.2718                   |
-| 2.0825 | 474500 | 0.0324        | 0.2734                   |
-| 2.0847 | 475000 | 0.0325        | 0.2706                   |
-| 2.0869 | 475500 | 0.0301        | 0.2733                   |
-| 2.0891 | 476000 | 0.0329        | 0.2765                   |
-| 2.0913 | 476500 | 0.0299        | 0.2776                   |
-| 2.0934 | 477000 | 0.0332        | 0.2738                   |
-| 2.0956 | 477500 | 0.0336        | 0.2742                   |
-| 2.0978 | 478000 | 0.0319        | 0.2763                   |
-| 2.1000 | 478500 | 0.0306        | 0.2779                   |
-| 2.1022 | 479000 | 0.0319        | 0.2770                   |
-| 2.1044 | 479500 | 0.0314        | 0.2758                   |
-| 2.1066 | 480000 | 0.0308        | 0.2743                   |
-| 2.1088 | 480500 | 0.0315        | 0.2744                   |
-| 2.1110 | 481000 | 0.03          | 0.2736                   |
-| 2.1132 | 481500 | 0.0319        | 0.2760                   |
-| 2.1154 | 482000 | 0.0316        | 0.2737                   |
-| 2.1176 | 482500 | 0.0309        | 0.2740                   |
-| 2.1198 | 483000 | 0.0311        | 0.2742                   |
-| 2.1220 | 483500 | 0.0316        | 0.2739                   |
-| 2.1242 | 484000 | 0.0325        | 0.2733                   |
-| 2.1264 | 484500 | 0.0325        | 0.2732                   |
-| 2.1286 | 485000 | 0.0318        | 0.2736                   |
-| 2.1308 | 485500 | 0.0318        | 0.2716                   |
-| 2.1329 | 486000 | 0.0309        | 0.2734                   |
-| 2.1351 | 486500 | 0.0316        | 0.2734                   |
-| 2.1373 | 487000 | 0.0316        | 0.2743                   |
-| 2.1395 | 487500 | 0.0327        | 0.2746                   |
-| 2.1417 | 488000 | 0.0335        | 0.2734                   |
-| 2.1439 | 488500 | 0.0312        | 0.2759                   |
-| 2.1461 | 489000 | 0.0297        | 0.2775                   |
-| 2.1483 | 489500 | 0.0335        | 0.2787                   |
-| 2.1505 | 490000 | 0.0322        | 0.2768                   |
-| 2.1527 | 490500 | 0.031         | 0.2767                   |
-| 2.1549 | 491000 | 0.0326        | 0.2744                   |
-| 2.1571 | 491500 | 0.0323        | 0.2738                   |
-| 2.1593 | 492000 | 0.0327        | 0.2749                   |
-| 2.1615 | 492500 | 0.0312        | 0.2755                   |
-| 2.1637 | 493000 | 0.0326        | 0.2738                   |
-| 2.1659 | 493500 | 0.032         | 0.2760                   |
-| 2.1681 | 494000 | 0.0321        | 0.2790                   |
-| 2.1702 | 494500 | 0.0318        | 0.2767                   |
-| 2.1724 | 495000 | 0.033         | 0.2743                   |
-| 2.1746 | 495500 | 0.0311        | 0.2743                   |
-| 2.1768 | 496000 | 0.0326        | 0.2741                   |
-| 2.1790 | 496500 | 0.0302        | 0.2773                   |
-| 2.1812 | 497000 | 0.0321        | 0.2771                   |
-| 2.1834 | 497500 | 0.0316        | 0.2764                   |
-| 2.1856 | 498000 | 0.0321        | 0.2793                   |
-| 2.1878 | 498500 | 0.0323        | 0.2795                   |
-| 2.1900 | 499000 | 0.0337        | 0.2792                   |
-| 2.1922 | 499500 | 0.0317        | 0.2779                   |
-| 2.1944 | 500000 | 0.0325        | 0.2768                   |
-| 2.1966 | 500500 | 0.0309        | 0.2774                   |
-| 2.1988 | 501000 | 0.0325        | 0.2756                   |
-| 2.2010 | 501500 | 0.0328        | 0.2798                   |
-| 2.2032 | 502000 | 0.0331        | 0.2807                   |
-| 2.2054 | 502500 | 0.0317        | 0.2812                   |
-| 2.2076 | 503000 | 0.031         | 0.2849                   |
+| 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                   |
 
 </details>
 

checkpoints/checkpoint-658000/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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-658000/README.md

@@ -0,0 +1,560 @@
+---
+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: 0.46253845649402
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.27084335936357
+      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,  0.1852,  0.1889],
+#         [ 0.1852,  1.0000, -0.0450],
+#         [ 0.1889, -0.0450,  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      | 0.4625     |
+| **spearman_cosine** | **0.2708** |
+
+<!--
+## 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                   |
+
+</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-658000/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-658000/config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "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"
+}

checkpoints/checkpoint-658000/modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

checkpoints/checkpoint-658000/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09da87e49829332cd9fda79aca6c994238f581deb96b0f05bc900b1758052dbf
+size 1064

checkpoints/checkpoint-658000/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 8192,
+    "do_lower_case": false
+}

checkpoints/checkpoint-658000/special_tokens_map.json

@@ -0,0 +1,40 @@
+{
+  "additional_special_tokens": [
+    "<|translation|>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoints/checkpoint-658000/tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed8dc3e139a6f2c6e1781996aabfef34c32241dcff263dbc66cf69b4760aeee9
+size 1074422

checkpoints/checkpoint-659000/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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-659000/README.md

@@ -0,0 +1,562 @@
+---
+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: 0.46454685943553947
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.2716248923550805
+      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,  0.1967,  0.2340],
+#         [ 0.1967,  1.0000, -0.0174],
+#         [ 0.2340, -0.0174,  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      | 0.4645     |
+| **spearman_cosine** | **0.2716** |
+
+<!--
+## 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                   |
+
+</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-659000/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-659000/config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "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"
+}

checkpoints/checkpoint-659000/modules.json

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

checkpoints/checkpoint-659000/rng_state.pth

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checkpoints/checkpoint-659000/sentence_bert_config.json

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+    "do_lower_case": false
+}

checkpoints/checkpoint-659000/special_tokens_map.json

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+  "additional_special_tokens": [
+    "<|translation|>"
+  ],
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "content": "<unk>",
+    "lstrip": false,
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+    "single_word": false
+  }
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checkpoints/checkpoint-660000/1_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-660000/README.md

@@ -0,0 +1,564 @@
+---
+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: 0.45990299528045375
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.27310402116372645
+      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, 0.2142, 0.2037],
+#         [0.2142, 1.0000, 0.0261],
+#         [0.2037, 0.0261, 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      | 0.4599     |
+| **spearman_cosine** | **0.2731** |
+
+<!--
+## 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                   |
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+| 4.4921 | 660000 | 0.0314        | 0.2731                   |
+
+</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-660000/config.json

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checkpoints/checkpoint-660000/config_sentence_transformers.json

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+{
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checkpoints/checkpoint-660000/modules.json

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checkpoints/checkpoint-660000/sentence_bert_config.json

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checkpoints/checkpoint-660000/special_tokens_map.json

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eval/similarity_evaluation_sts_eval_results.csv

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