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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
+}

README.md

@@ -1,3 +1,886 @@
----
-license: apache-2.0
----
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:1618413
+- loss:CosineSimilarityLoss
+base_model: BSC-LT/MrBERT-es
+widget:
+- source_sentence: El término empezó como una noción informal y no rigurosa originalmente
+    pensada como una "cantidad infinitamente pequeña", y originalmente fundamentó
+    ciertos razonamientos del cálculo infinitesimal.
+  sentences:
+  - Esto obligó a Knuth a dedicar un tiempo considerable en el estudio de la tipografía.
+  - Véase también Ingreso por domiciliación Transferencia bancaria Zona Única de Pagos
+    en Euros Referencias Banca
+  - ARTÍCULO 20o.
+- source_sentence: En su deambular, Leto encontró la recién creada isla flotante de
+    Delos, que no era el continente ni una isla real, y dio a luz allí.
+  sentences:
+  - El andador para adultos tiene frenos, es regulable en altura y el respaldo y el
+    asiento vienen recubiertos de esponja.
+  - Agricultura biodinámica La agricultura biológico-dinámica, creada en 1924 por
+    Rudolf Steiner y denominada agricultura biodinámica se basa en los fundamentos
+    y propuestas de estudio vinculados a la vertiente filosófica antroposofía, cuyo
+    autor es el mismo Steiner.
+  - '"El incremento de plazas se considera inmediato", advierten los jueces.'
+- source_sentence: Piensa en Mí... pensaré en ti.
+  sentences:
+  - Para quienes participan en un proceso educativo ambiental , los hallazgos y logros
+    son el inicio de nuevas preguntas y posibilidades.
+  - El transporte externo sobre pájaros o mamíferos es facilitado por modificaciones
+    del papus como las cerdas con barbas retrorsas, crecimientos del fruto como ganchos
+    o espinas, o brácteas involucrales especializadas.
+  - C. provocó, más que en cualquier otra parte, una simbiosis entre Grecia, Irán
+    y la India.
+- source_sentence: Fuimos un grupo mixto de amigos a pasar un finde en mojacar sin
+    más intención que pasarlo bien.
+  sentences:
+  - Suena el timbre para volver a clases, todos comentan que ha sido un gran partido
+    de fútbol, el resultado final ha sido de dos a cero, mañana continuará el campeonato
+    comentan los organizadores, aun continúo extasiado por aquel encuentro, la inspectora
+    me regaña y me ordena subir a la sala, ¡Qué aburrido!, ahora viene clase de historia,
+    nos toca ver las guerras mundiales, me pregunto ¿Para qué?, con tantas batallas
+    uno se pone bélico y ve guerras donde no las hay.
+  - El museo fue creado en el año 1922 por el Dr.
+  - Hasta su muerte vivió en las inmediaciones de la casa donde nació, en Maguncia.
+- source_sentence: Muchos jugadores de poker cuenta de que cuando juegan Hold'em en
+    línea que están recibiendo mucho más que simplemente un par de horas de entretenimiento.
+    sitios web de póquer en ofrecer a los jugadores una gran variedad de métodos para
+    disfrutar de sus juegos a favor, con la posibilidad de ganar dinero en serio.
+  sentences:
+  - Las primeras máquinas programables se desarrollaron en el mundo musulmán.
+  - Las leyendas urbanas suelen dejarnos una moraleja o enseñanza y casi siempre quienes
+    las narran las modifican ligeramente o versionan para adaptarlas a la cultura
+    o idiosincrasia del lugar en el que se cuenten o difundan, de allí que existan
+    un sinfín de versiones de un mismo relato, dependiendo de la región o país del
+    que se trate.
+  - Kepler definió la inercia sólo en términos de resistencia al movimiento, basándose
+    una vez más en la presunción de que el reposo era un estado natural que no necesitaba
+    explicación.
+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: 0.5045556099993986
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.2890314273993812
+      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 = [
+    "Muchos jugadores de poker cuenta de que cuando juegan Hold'em en línea que están recibiendo mucho más que simplemente un par de horas de entretenimiento. sitios web de póquer en ofrecer a los jugadores una gran variedad de métodos para disfrutar de sus juegos a favor, con la posibilidad de ganar dinero en serio.",
+    'Las leyendas urbanas suelen dejarnos una moraleja o enseñanza y casi siempre quienes las narran las modifican ligeramente o versionan para adaptarlas a la cultura o idiosincrasia del lugar en el que se cuenten o difundan, de allí que existan un sinfín de versiones de un mismo relato, dependiendo de la región o país del que se trate.',
+    'Kepler definió la inercia sólo en términos de resistencia al movimiento, basándose una vez más en la presunción de que el reposo era un estado natural que no necesitaba explicación.',
+]
+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, 0.4663, 0.1126],
+#         [0.4663, 1.0000, 0.1658],
+#         [0.1126, 0.1658, 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      | 0.5046    |
+| **spearman_cosine** | **0.289** |
+
+<!--
+## 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: 1,618,413 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: 38.33 tokens</li><li>max: 435 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 36.8 tokens</li><li>max: 261 tokens</li></ul> | <ul><li>min: -0.8</li><li>mean: 0.16</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                        | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                       | label                            |
+  |:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>Estadísticas Estadísticas El almacenamiento o acceso técnico que es utilizado exclusivamente con fines estadísticos.</code> | <code>Connect within Simplemente instale la aplicación, seleccione su servidor y conéctese para conectarse en cuestión de segundos.</code>                                                                                                                                                                                                                                                                                                                       | <code>0.10739796608686447</code> |
+  | <code>Saludamos con la cabeza y sonreímos.</code>                                                                                 | <code>Simbolismo: corriente de corte fantástico y onírico, surgió como reacción al naturalismo de la corriente realista e impresionista, poniendo especial énfasis en el mundo de los sueños, así como en aspectos satánicos y terroríficos, el sexo y la perversión.</code>                                                                                                                                                                                     | <code>0.05001075938344002</code> |
+  | <code>La lista de nominados se anunció hace escasos días.</code>                                                                  | <code>Es muy probable que el topónimo Egipto derive de la transcripción fonética de uno de los nombres o epítetos de Menfis, capital del antiguo Kemet bajo la Dinastía III, a saber: Hout Ka-Ptah , que quiere decir "Casa del ka de Ptah", en alusión al principal templo consagrado a este dios, que pasó al griego como Aígyptos, que, con el tiempo, designó primero al barrio en el que se encontraba, luego a toda la ciudad y más tarde al reino.</code> | <code>0.14469777047634125</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
+- `num_train_epochs`: 4
+- `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`: 1.0
+- `num_train_epochs`: 4
+- `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 |
+|:------:|:------:|:-------------:|:------------------------:|
+| 0.4671 | 94500  | 0.043         | 0.2705                   |
+| 0.4696 | 95000  | 0.0425        | 0.2708                   |
+| 0.4721 | 95500  | 0.0428        | 0.2703                   |
+| 0.4745 | 96000  | 0.0411        | 0.2689                   |
+| 0.4770 | 96500  | 0.0434        | 0.2760                   |
+| 0.4795 | 97000  | 0.0407        | 0.2733                   |
+| 0.4820 | 97500  | 0.0445        | 0.2740                   |
+| 0.4844 | 98000  | 0.0447        | 0.2694                   |
+| 0.4869 | 98500  | 0.044         | 0.2755                   |
+| 0.4894 | 99000  | 0.0425        | 0.2705                   |
+| 0.4918 | 99500  | 0.0444        | 0.2729                   |
+| 0.4943 | 100000 | 0.0448        | 0.2690                   |
+| 0.4968 | 100500 | 0.0432        | 0.2747                   |
+| 0.4993 | 101000 | 0.0426        | 0.2739                   |
+| 0.5017 | 101500 | 0.043         | 0.2773                   |
+| 0.5042 | 102000 | 0.0432        | 0.2774                   |
+| 0.5067 | 102500 | 0.042         | 0.2776                   |
+| 0.5091 | 103000 | 0.0441        | 0.2755                   |
+| 0.5116 | 103500 | 0.0441        | 0.2782                   |
+| 0.5141 | 104000 | 0.0436        | 0.2784                   |
+| 0.5166 | 104500 | 0.0446        | 0.2763                   |
+| 0.5190 | 105000 | 0.0435        | 0.2779                   |
+| 0.5215 | 105500 | 0.0434        | 0.2763                   |
+| 0.5240 | 106000 | 0.0426        | 0.2744                   |
+| 0.5264 | 106500 | 0.0442        | 0.2748                   |
+| 0.5289 | 107000 | 0.0468        | 0.2743                   |
+| 0.5314 | 107500 | 0.0428        | 0.2684                   |
+| 0.5339 | 108000 | 0.042         | 0.2719                   |
+| 0.5363 | 108500 | 0.0435        | 0.2761                   |
+| 0.5388 | 109000 | 0.0438        | 0.2765                   |
+| 0.5413 | 109500 | 0.0442        | 0.2731                   |
+| 0.5437 | 110000 | 0.0443        | 0.2719                   |
+| 0.5462 | 110500 | 0.0411        | 0.2721                   |
+| 0.5487 | 111000 | 0.0431        | 0.2752                   |
+| 0.5512 | 111500 | 0.0435        | 0.2743                   |
+| 0.5536 | 112000 | 0.0444        | 0.2701                   |
+| 0.5561 | 112500 | 0.0423        | 0.2720                   |
+| 0.5586 | 113000 | 0.0446        | 0.2728                   |
+| 0.5610 | 113500 | 0.042         | 0.2734                   |
+| 0.5635 | 114000 | 0.0438        | 0.2763                   |
+| 0.5660 | 114500 | 0.0412        | 0.2774                   |
+| 0.5685 | 115000 | 0.0417        | 0.2787                   |
+| 0.5709 | 115500 | 0.0448        | 0.2775                   |
+| 0.5734 | 116000 | 0.0446        | 0.2755                   |
+| 0.5759 | 116500 | 0.0419        | 0.2768                   |
+| 0.5783 | 117000 | 0.0435        | 0.2753                   |
+| 0.5808 | 117500 | 0.0451        | 0.2747                   |
+| 0.5833 | 118000 | 0.0439        | 0.2768                   |
+| 0.5858 | 118500 | 0.0438        | 0.2773                   |
+| 0.5882 | 119000 | 0.043         | 0.2770                   |
+| 0.5907 | 119500 | 0.0458        | 0.2736                   |
+| 0.5932 | 120000 | 0.0419        | 0.2746                   |
+| 0.5956 | 120500 | 0.0441        | 0.2730                   |
+| 0.5981 | 121000 | 0.0425        | 0.2752                   |
+| 0.6006 | 121500 | 0.0414        | 0.2733                   |
+| 0.6031 | 122000 | 0.0407        | 0.2732                   |
+| 0.6055 | 122500 | 0.0445        | 0.2732                   |
+| 0.6080 | 123000 | 0.0434        | 0.2713                   |
+| 0.6105 | 123500 | 0.0439        | 0.2720                   |
+| 0.6129 | 124000 | 0.0438        | 0.2761                   |
+| 0.6154 | 124500 | 0.0418        | 0.2754                   |
+| 0.6179 | 125000 | 0.0423        | 0.2763                   |
+| 0.6204 | 125500 | 0.0426        | 0.2773                   |
+| 0.6228 | 126000 | 0.0448        | 0.2732                   |
+| 0.6253 | 126500 | 0.0424        | 0.2691                   |
+| 0.6278 | 127000 | 0.0451        | 0.2760                   |
+| 0.6302 | 127500 | 0.0437        | 0.2713                   |
+| 0.6327 | 128000 | 0.0429        | 0.2690                   |
+| 0.6352 | 128500 | 0.0439        | 0.2691                   |
+| 0.6377 | 129000 | 0.0442        | 0.2769                   |
+| 0.6401 | 129500 | 0.042         | 0.2750                   |
+| 0.6426 | 130000 | 0.0462        | 0.2731                   |
+| 0.6451 | 130500 | 0.043         | 0.2750                   |
+| 0.6475 | 131000 | 0.0445        | 0.2770                   |
+| 0.6500 | 131500 | 0.0424        | 0.2753                   |
+| 0.6525 | 132000 | 0.0451        | 0.2734                   |
+| 0.6550 | 132500 | 0.0453        | 0.2768                   |
+| 0.6574 | 133000 | 0.0453        | 0.2820                   |
+| 0.6599 | 133500 | 0.0424        | 0.2821                   |
+| 0.6624 | 134000 | 0.0446        | 0.2806                   |
+| 0.6648 | 134500 | 0.0433        | 0.2781                   |
+| 0.6673 | 135000 | 0.0443        | 0.2792                   |
+| 0.6698 | 135500 | 0.0447        | 0.2770                   |
+| 0.6723 | 136000 | 0.0396        | 0.2718                   |
+| 0.6747 | 136500 | 0.0418        | 0.2736                   |
+| 0.6772 | 137000 | 0.0428        | 0.2774                   |
+| 0.6797 | 137500 | 0.0444        | 0.2762                   |
+| 0.6821 | 138000 | 0.0409        | 0.2725                   |
+| 0.6846 | 138500 | 0.0429        | 0.2731                   |
+| 0.6871 | 139000 | 0.0447        | 0.2769                   |
+| 0.6896 | 139500 | 0.0454        | 0.2744                   |
+| 0.6920 | 140000 | 0.0443        | 0.2822                   |
+| 0.6945 | 140500 | 0.045         | 0.2753                   |
+| 0.6970 | 141000 | 0.043         | 0.2780                   |
+| 0.6994 | 141500 | 0.042         | 0.2765                   |
+| 0.7019 | 142000 | 0.0427        | 0.2762                   |
+| 0.7044 | 142500 | 0.0404        | 0.2809                   |
+| 0.7069 | 143000 | 0.045         | 0.2784                   |
+| 0.7093 | 143500 | 0.046         | 0.2781                   |
+| 0.7118 | 144000 | 0.0449        | 0.2733                   |
+| 0.7143 | 144500 | 0.0414        | 0.2736                   |
+| 0.7168 | 145000 | 0.0472        | 0.2751                   |
+| 0.7192 | 145500 | 0.0429        | 0.2782                   |
+| 0.7217 | 146000 | 0.0429        | 0.2781                   |
+| 0.7242 | 146500 | 0.0446        | 0.2750                   |
+| 0.7266 | 147000 | 0.0429        | 0.2773                   |
+| 0.7291 | 147500 | 0.0484        | 0.2808                   |
+| 0.7316 | 148000 | 0.0439        | 0.2759                   |
+| 0.7341 | 148500 | 0.0429        | 0.2764                   |
+| 0.7365 | 149000 | 0.0453        | 0.2785                   |
+| 0.7390 | 149500 | 0.043         | 0.2756                   |
+| 0.7415 | 150000 | 0.0438        | 0.2765                   |
+| 0.7439 | 150500 | 0.0446        | 0.2731                   |
+| 0.7464 | 151000 | 0.0443        | 0.2759                   |
+| 0.7489 | 151500 | 0.0438        | 0.2725                   |
+| 0.7514 | 152000 | 0.0463        | 0.2756                   |
+| 0.7538 | 152500 | 0.046         | 0.2774                   |
+| 0.7563 | 153000 | 0.0423        | 0.2769                   |
+| 0.7588 | 153500 | 0.0453        | 0.2752                   |
+| 0.7612 | 154000 | 0.046         | 0.2726                   |
+| 0.7637 | 154500 | 0.0432        | 0.2763                   |
+| 0.7662 | 155000 | 0.0462        | 0.2786                   |
+| 0.7687 | 155500 | 0.0455        | 0.2775                   |
+| 0.7711 | 156000 | 0.043         | 0.2783                   |
+| 0.7736 | 156500 | 0.0442        | 0.2784                   |
+| 0.7761 | 157000 | 0.0437        | 0.2769                   |
+| 0.7785 | 157500 | 0.044         | 0.2812                   |
+| 0.7810 | 158000 | 0.0443        | 0.2797                   |
+| 0.7835 | 158500 | 0.0436        | 0.2783                   |
+| 0.7860 | 159000 | 0.0435        | 0.2847                   |
+| 0.7884 | 159500 | 0.0438        | 0.2835                   |
+| 0.7909 | 160000 | 0.0446        | 0.2815                   |
+| 0.7934 | 160500 | 0.0434        | 0.2840                   |
+| 0.7958 | 161000 | 0.0455        | 0.2833                   |
+| 0.7983 | 161500 | 0.043         | 0.2845                   |
+| 0.8008 | 162000 | 0.0436        | 0.2845                   |
+| 0.8033 | 162500 | 0.0443        | 0.2823                   |
+| 0.8057 | 163000 | 0.0441        | 0.2812                   |
+| 0.8082 | 163500 | 0.0435        | 0.2777                   |
+| 0.8107 | 164000 | 0.0421        | 0.2740                   |
+| 0.8131 | 164500 | 0.0437        | 0.2738                   |
+| 0.8156 | 165000 | 0.0457        | 0.2745                   |
+| 0.8181 | 165500 | 0.0453        | 0.2815                   |
+| 0.8206 | 166000 | 0.0427        | 0.2788                   |
+| 0.8230 | 166500 | 0.045         | 0.2809                   |
+| 0.8255 | 167000 | 0.0439        | 0.2818                   |
+| 0.8280 | 167500 | 0.045         | 0.2795                   |
+| 0.8304 | 168000 | 0.0422        | 0.2802                   |
+| 0.8329 | 168500 | 0.0449        | 0.2783                   |
+| 0.8354 | 169000 | 0.0437        | 0.2765                   |
+| 0.8379 | 169500 | 0.0445        | 0.2788                   |
+| 0.8403 | 170000 | 0.0419        | 0.2832                   |
+| 0.8428 | 170500 | 0.0423        | 0.2775                   |
+| 0.8453 | 171000 | 0.0411        | 0.2804                   |
+| 0.8477 | 171500 | 0.0437        | 0.2755                   |
+| 0.8502 | 172000 | 0.044         | 0.2774                   |
+| 0.8527 | 172500 | 0.0447        | 0.2740                   |
+| 0.8552 | 173000 | 0.0444        | 0.2757                   |
+| 0.8576 | 173500 | 0.0419        | 0.2750                   |
+| 0.8601 | 174000 | 0.0461        | 0.2743                   |
+| 0.8626 | 174500 | 0.0455        | 0.2761                   |
+| 0.8650 | 175000 | 0.042         | 0.2745                   |
+| 0.8675 | 175500 | 0.0466        | 0.2757                   |
+| 0.8700 | 176000 | 0.0439        | 0.2744                   |
+| 0.8725 | 176500 | 0.0423        | 0.2771                   |
+| 0.8749 | 177000 | 0.0438        | 0.2723                   |
+| 0.8774 | 177500 | 0.0438        | 0.2771                   |
+| 0.8799 | 178000 | 0.0417        | 0.2777                   |
+| 0.8823 | 178500 | 0.044         | 0.2780                   |
+| 0.8848 | 179000 | 0.0426        | 0.2746                   |
+| 0.8873 | 179500 | 0.0446        | 0.2758                   |
+| 0.8898 | 180000 | 0.0451        | 0.2767                   |
+| 0.8922 | 180500 | 0.0432        | 0.2770                   |
+| 0.8947 | 181000 | 0.0425        | 0.2749                   |
+| 0.8972 | 181500 | 0.0447        | 0.2758                   |
+| 0.8996 | 182000 | 0.0422        | 0.2798                   |
+| 0.9021 | 182500 | 0.045         | 0.2789                   |
+| 0.9046 | 183000 | 0.044         | 0.2786                   |
+| 0.9071 | 183500 | 0.0436        | 0.2781                   |
+| 0.9095 | 184000 | 0.046         | 0.2777                   |
+| 0.9120 | 184500 | 0.0443        | 0.2773                   |
+| 0.9145 | 185000 | 0.0445        | 0.2753                   |
+| 0.9169 | 185500 | 0.043         | 0.2767                   |
+| 0.9194 | 186000 | 0.0454        | 0.2743                   |
+| 0.9219 | 186500 | 0.0433        | 0.2775                   |
+| 0.9244 | 187000 | 0.0443        | 0.2775                   |
+| 0.9268 | 187500 | 0.0432        | 0.2765                   |
+| 0.9293 | 188000 | 0.0434        | 0.2793                   |
+| 0.9318 | 188500 | 0.0463        | 0.2801                   |
+| 0.9342 | 189000 | 0.0439        | 0.2795                   |
+| 0.9367 | 189500 | 0.0423        | 0.2812                   |
+| 0.9392 | 190000 | 0.0441        | 0.2768                   |
+| 0.9417 | 190500 | 0.0446        | 0.2754                   |
+| 0.9441 | 191000 | 0.0436        | 0.2814                   |
+| 0.9466 | 191500 | 0.045         | 0.2795                   |
+| 0.9491 | 192000 | 0.0445        | 0.2794                   |
+| 0.9515 | 192500 | 0.0429        | 0.2827                   |
+| 0.9540 | 193000 | 0.043         | 0.2815                   |
+| 0.9565 | 193500 | 0.0446        | 0.2827                   |
+| 0.9590 | 194000 | 0.0456        | 0.2822                   |
+| 0.9614 | 194500 | 0.0406        | 0.2828                   |
+| 0.9639 | 195000 | 0.0444        | 0.2844                   |
+| 0.9664 | 195500 | 0.0448        | 0.2785                   |
+| 0.9688 | 196000 | 0.0427        | 0.2784                   |
+| 0.9713 | 196500 | 0.0453        | 0.2788                   |
+| 0.9738 | 197000 | 0.0443        | 0.2751                   |
+| 0.9763 | 197500 | 0.0444        | 0.2754                   |
+| 0.9787 | 198000 | 0.0448        | 0.2745                   |
+| 0.9812 | 198500 | 0.0445        | 0.2752                   |
+| 0.9837 | 199000 | 0.046         | 0.2710                   |
+| 0.9861 | 199500 | 0.0459        | 0.2732                   |
+| 0.9886 | 200000 | 0.0394        | 0.2729                   |
+| 0.9911 | 200500 | 0.045         | 0.2737                   |
+| 0.9936 | 201000 | 0.0434        | 0.2753                   |
+| 0.9960 | 201500 | 0.0465        | 0.2771                   |
+| 0.9985 | 202000 | 0.0443        | 0.2755                   |
+| 1.0    | 202302 | -             | 0.2735                   |
+| 1.0010 | 202500 | 0.0406        | 0.2746                   |
+| 1.0035 | 203000 | 0.0358        | 0.2751                   |
+| 1.0059 | 203500 | 0.039         | 0.2739                   |
+| 1.0084 | 204000 | 0.0389        | 0.2740                   |
+| 1.0109 | 204500 | 0.0382        | 0.2736                   |
+| 1.0133 | 205000 | 0.0374        | 0.2714                   |
+| 1.0158 | 205500 | 0.0393        | 0.2745                   |
+| 1.0183 | 206000 | 0.0388        | 0.2759                   |
+| 1.0208 | 206500 | 0.0398        | 0.2765                   |
+| 1.0232 | 207000 | 0.0399        | 0.2772                   |
+| 1.0257 | 207500 | 0.0403        | 0.2757                   |
+| 1.0282 | 208000 | 0.0383        | 0.2786                   |
+| 1.0306 | 208500 | 0.0376        | 0.2771                   |
+| 1.0331 | 209000 | 0.0418        | 0.2761                   |
+| 1.0356 | 209500 | 0.0381        | 0.2768                   |
+| 1.0381 | 210000 | 0.038         | 0.2761                   |
+| 1.0405 | 210500 | 0.0386        | 0.2735                   |
+| 1.0430 | 211000 | 0.0378        | 0.2768                   |
+| 1.0455 | 211500 | 0.0389        | 0.2764                   |
+| 1.0479 | 212000 | 0.0378        | 0.2757                   |
+| 1.0504 | 212500 | 0.039         | 0.2743                   |
+| 1.0529 | 213000 | 0.0367        | 0.2749                   |
+| 1.0554 | 213500 | 0.0394        | 0.2747                   |
+| 1.0578 | 214000 | 0.0372        | 0.2740                   |
+| 1.0603 | 214500 | 0.039         | 0.2757                   |
+| 1.0628 | 215000 | 0.0396        | 0.2813                   |
+| 1.0652 | 215500 | 0.0403        | 0.2794                   |
+| 1.0677 | 216000 | 0.0387        | 0.2771                   |
+| 1.0702 | 216500 | 0.0381        | 0.2733                   |
+| 1.0727 | 217000 | 0.0406        | 0.2717                   |
+| 1.0751 | 217500 | 0.0408        | 0.2749                   |
+| 1.0776 | 218000 | 0.0401        | 0.2750                   |
+| 1.0801 | 218500 | 0.0363        | 0.2724                   |
+| 1.0825 | 219000 | 0.0392        | 0.2745                   |
+| 1.0850 | 219500 | 0.0386        | 0.2726                   |
+| 1.0875 | 220000 | 0.0413        | 0.2741                   |
+| 1.0900 | 220500 | 0.04          | 0.2753                   |
+| 1.0924 | 221000 | 0.0371        | 0.2772                   |
+| 1.0949 | 221500 | 0.0392        | 0.2734                   |
+| 1.0974 | 222000 | 0.0397        | 0.2764                   |
+| 1.0998 | 222500 | 0.0406        | 0.2732                   |
+| 1.1023 | 223000 | 0.0396        | 0.2730                   |
+| 1.1048 | 223500 | 0.0396        | 0.2756                   |
+| 1.1073 | 224000 | 0.0389        | 0.2771                   |
+| 1.1097 | 224500 | 0.0402        | 0.2766                   |
+| 1.1122 | 225000 | 0.0386        | 0.2774                   |
+| 1.1147 | 225500 | 0.0389        | 0.2782                   |
+| 1.1171 | 226000 | 0.0372        | 0.2768                   |
+| 1.1196 | 226500 | 0.0384        | 0.2726                   |
+| 1.1221 | 227000 | 0.0424        | 0.2734                   |
+| 1.1246 | 227500 | 0.041         | 0.2732                   |
+| 1.1270 | 228000 | 0.0392        | 0.2717                   |
+| 1.1295 | 228500 | 0.039         | 0.2743                   |
+| 1.1320 | 229000 | 0.0402        | 0.2721                   |
+| 1.1344 | 229500 | 0.0403        | 0.2733                   |
+| 1.1369 | 230000 | 0.0393        | 0.2727                   |
+| 1.1394 | 230500 | 0.039         | 0.2755                   |
+| 1.1419 | 231000 | 0.0382        | 0.2757                   |
+| 1.1443 | 231500 | 0.036         | 0.2760                   |
+| 1.1468 | 232000 | 0.0408        | 0.2762                   |
+| 1.1493 | 232500 | 0.0393        | 0.2733                   |
+| 1.1517 | 233000 | 0.0385        | 0.2750                   |
+| 1.1542 | 233500 | 0.0398        | 0.2772                   |
+| 1.1567 | 234000 | 0.0411        | 0.2751                   |
+| 1.1592 | 234500 | 0.0404        | 0.2747                   |
+| 1.1616 | 235000 | 0.0393        | 0.2765                   |
+| 1.1641 | 235500 | 0.0389        | 0.2715                   |
+| 1.1666 | 236000 | 0.0379        | 0.2759                   |
+| 1.1690 | 236500 | 0.0392        | 0.2740                   |
+| 1.1715 | 237000 | 0.039         | 0.2732                   |
+| 1.1740 | 237500 | 0.041         | 0.2703                   |
+| 1.1765 | 238000 | 0.0403        | 0.2748                   |
+| 1.1789 | 238500 | 0.0388        | 0.2753                   |
+| 1.1814 | 239000 | 0.0405        | 0.2744                   |
+| 1.1839 | 239500 | 0.039         | 0.2769                   |
+| 1.1863 | 240000 | 0.0405        | 0.2746                   |
+| 1.1888 | 240500 | 0.0389        | 0.2738                   |
+| 1.1913 | 241000 | 0.0393        | 0.2781                   |
+| 1.1938 | 241500 | 0.0374        | 0.2794                   |
+| 1.1962 | 242000 | 0.0404        | 0.2747                   |
+| 1.1987 | 242500 | 0.0388        | 0.2763                   |
+| 1.2012 | 243000 | 0.0387        | 0.2775                   |
+| 1.2036 | 243500 | 0.0401        | 0.2723                   |
+| 1.2061 | 244000 | 0.0394        | 0.2695                   |
+| 1.2086 | 244500 | 0.0405        | 0.2735                   |
+| 1.2111 | 245000 | 0.0408        | 0.2754                   |
+| 1.2135 | 245500 | 0.0388        | 0.2708                   |
+| 1.2160 | 246000 | 0.0383        | 0.2738                   |
+| 1.2185 | 246500 | 0.0416        | 0.2736                   |
+| 1.2209 | 247000 | 0.0379        | 0.2763                   |
+| 1.2234 | 247500 | 0.0415        | 0.2756                   |
+| 1.2259 | 248000 | 0.0378        | 0.2754                   |
+| 1.2284 | 248500 | 0.0392        | 0.2772                   |
+| 1.2308 | 249000 | 0.0391        | 0.2757                   |
+| 1.2333 | 249500 | 0.0386        | 0.2717                   |
+| 1.2358 | 250000 | 0.0416        | 0.2769                   |
+| 1.2382 | 250500 | 0.0404        | 0.2734                   |
+| 1.2407 | 251000 | 0.0379        | 0.2749                   |
+| 1.2432 | 251500 | 0.0387        | 0.2743                   |
+| 1.2457 | 252000 | 0.0421        | 0.2751                   |
+| 1.2481 | 252500 | 0.0391        | 0.2753                   |
+| 1.2506 | 253000 | 0.039         | 0.2755                   |
+| 1.2531 | 253500 | 0.042         | 0.2725                   |
+| 1.2555 | 254000 | 0.0394        | 0.2731                   |
+| 1.2580 | 254500 | 0.0398        | 0.2758                   |
+| 1.2605 | 255000 | 0.0404        | 0.2786                   |
+| 1.2630 | 255500 | 0.0398        | 0.2783                   |
+| 1.2654 | 256000 | 0.0392        | 0.2779                   |
+| 1.2679 | 256500 | 0.0386        | 0.2785                   |
+| 1.2704 | 257000 | 0.0402        | 0.2764                   |
+| 1.2728 | 257500 | 0.0376        | 0.2792                   |
+| 1.2753 | 258000 | 0.0387        | 0.2791                   |
+| 1.2778 | 258500 | 0.0397        | 0.2808                   |
+| 1.2803 | 259000 | 0.038         | 0.2802                   |
+| 1.2827 | 259500 | 0.0389        | 0.2795                   |
+| 1.2852 | 260000 | 0.0412        | 0.2771                   |
+| 1.2877 | 260500 | 0.0394        | 0.2777                   |
+| 1.2902 | 261000 | 0.0426        | 0.2792                   |
+| 1.2926 | 261500 | 0.0391        | 0.2772                   |
+| 1.2951 | 262000 | 0.0382        | 0.2783                   |
+| 1.2976 | 262500 | 0.0385        | 0.2789                   |
+| 1.3000 | 263000 | 0.0401        | 0.2812                   |
+| 1.3025 | 263500 | 0.0392        | 0.2826                   |
+| 1.3050 | 264000 | 0.0403        | 0.2813                   |
+| 1.3075 | 264500 | 0.0394        | 0.2779                   |
+| 1.3099 | 265000 | 0.0397        | 0.2832                   |
+| 1.3124 | 265500 | 0.0407        | 0.2785                   |
+| 1.3149 | 266000 | 0.0412        | 0.2809                   |
+| 1.3173 | 266500 | 0.0399        | 0.2805                   |
+| 1.3198 | 267000 | 0.0406        | 0.2803                   |
+| 1.3223 | 267500 | 0.0397        | 0.2812                   |
+| 1.3248 | 268000 | 0.0413        | 0.2819                   |
+| 1.3272 | 268500 | 0.0398        | 0.2788                   |
+| 1.3297 | 269000 | 0.0402        | 0.2814                   |
+| 1.3322 | 269500 | 0.0387        | 0.2825                   |
+| 1.3346 | 270000 | 0.0425        | 0.2789                   |
+| 1.3371 | 270500 | 0.038         | 0.2793                   |
+| 1.3396 | 271000 | 0.0377        | 0.2775                   |
+| 1.3421 | 271500 | 0.0414        | 0.2769                   |
+| 1.3445 | 272000 | 0.0389        | 0.2735                   |
+| 1.3470 | 272500 | 0.0386        | 0.2785                   |
+| 1.3495 | 273000 | 0.0401        | 0.2813                   |
+| 1.3519 | 273500 | 0.0383        | 0.2801                   |
+| 1.3544 | 274000 | 0.0396        | 0.2796                   |
+| 1.3569 | 274500 | 0.0396        | 0.2793                   |
+| 1.3594 | 275000 | 0.0424        | 0.2814                   |
+| 1.3618 | 275500 | 0.0418        | 0.2814                   |
+| 1.3643 | 276000 | 0.0383        | 0.2787                   |
+| 1.3668 | 276500 | 0.04          | 0.2797                   |
+| 1.3692 | 277000 | 0.0414        | 0.2810                   |
+| 1.3717 | 277500 | 0.0379        | 0.2848                   |
+| 1.3742 | 278000 | 0.0381        | 0.2846                   |
+| 1.3767 | 278500 | 0.0383        | 0.2814                   |
+| 1.3791 | 279000 | 0.039         | 0.2818                   |
+| 1.3816 | 279500 | 0.0388        | 0.2792                   |
+| 1.3841 | 280000 | 0.0408        | 0.2784                   |
+| 1.3865 | 280500 | 0.0389        | 0.2814                   |
+| 1.3890 | 281000 | 0.0426        | 0.2794                   |
+| 1.3915 | 281500 | 0.0392        | 0.2780                   |
+| 1.3940 | 282000 | 0.0405        | 0.2778                   |
+| 1.3964 | 282500 | 0.0407        | 0.2769                   |
+| 1.3989 | 283000 | 0.0396        | 0.2730                   |
+| 1.4014 | 283500 | 0.0376        | 0.2770                   |
+| 1.4038 | 284000 | 0.0399        | 0.2791                   |
+| 1.4063 | 284500 | 0.0405        | 0.2791                   |
+| 1.4088 | 285000 | 0.0382        | 0.2804                   |
+| 1.4113 | 285500 | 0.0388        | 0.2835                   |
+| 1.4137 | 286000 | 0.0394        | 0.2784                   |
+| 1.4162 | 286500 | 0.0388        | 0.2813                   |
+| 1.4187 | 287000 | 0.0397        | 0.2813                   |
+| 1.4211 | 287500 | 0.0404        | 0.2808                   |
+| 1.4236 | 288000 | 0.0374        | 0.2792                   |
+| 1.4261 | 288500 | 0.041         | 0.2724                   |
+| 1.4286 | 289000 | 0.0409        | 0.2770                   |
+| 1.4310 | 289500 | 0.04          | 0.2789                   |
+| 1.4335 | 290000 | 0.0412        | 0.2754                   |
+| 1.4360 | 290500 | 0.0404        | 0.2780                   |
+| 1.4384 | 291000 | 0.0406        | 0.2794                   |
+| 1.4409 | 291500 | 0.0387        | 0.2776                   |
+| 1.4434 | 292000 | 0.037         | 0.2801                   |
+| 1.4459 | 292500 | 0.0394        | 0.2778                   |
+| 1.4483 | 293000 | 0.0406        | 0.2786                   |
+| 1.4508 | 293500 | 0.0401        | 0.2827                   |
+| 1.4533 | 294000 | 0.0388        | 0.2770                   |
+| 1.4557 | 294500 | 0.0377        | 0.2768                   |
+| 1.4582 | 295000 | 0.0386        | 0.2773                   |
+| 1.4607 | 295500 | 0.04          | 0.2783                   |
+| 1.4632 | 296000 | 0.0402        | 0.2780                   |
+| 1.4656 | 296500 | 0.0401        | 0.2820                   |
+| 1.4681 | 297000 | 0.0393        | 0.2790                   |
+| 1.4706 | 297500 | 0.0394        | 0.2787                   |
+| 1.4730 | 298000 | 0.0392        | 0.2759                   |
+| 1.4755 | 298500 | 0.0396        | 0.2767                   |
+| 1.4780 | 299000 | 0.0379        | 0.2752                   |
+| 1.4805 | 299500 | 0.039         | 0.2742                   |
+| 1.4829 | 300000 | 0.0383        | 0.2750                   |
+| 1.4854 | 300500 | 0.0398        | 0.2741                   |
+| 1.4879 | 301000 | 0.0394        | 0.2749                   |
+| 1.4903 | 301500 | 0.0416        | 0.2728                   |
+| 1.4928 | 302000 | 0.0388        | 0.2751                   |
+| 1.4953 | 302500 | 0.041         | 0.2759                   |
+| 1.4978 | 303000 | 0.0405        | 0.2744                   |
+| 1.5002 | 303500 | 0.0397        | 0.2734                   |
+| 1.5027 | 304000 | 0.0413        | 0.2762                   |
+| 1.5052 | 304500 | 0.0412        | 0.2754                   |
+| 1.5076 | 305000 | 0.0386        | 0.2787                   |
+| 1.5101 | 305500 | 0.0377        | 0.2790                   |
+| 1.5126 | 306000 | 0.0395        | 0.2784                   |
+| 1.5151 | 306500 | 0.0423        | 0.2797                   |
+| 1.5175 | 307000 | 0.0396        | 0.2819                   |
+| 1.5200 | 307500 | 0.0395        | 0.2810                   |
+| 1.5225 | 308000 | 0.04          | 0.2828                   |
+| 1.5249 | 308500 | 0.0373        | 0.2840                   |
+| 1.5274 | 309000 | 0.0385        | 0.2873                   |
+| 1.5299 | 309500 | 0.0401        | 0.2854                   |
+| 1.5324 | 310000 | 0.0404        | 0.2851                   |
+| 1.5348 | 310500 | 0.0404        | 0.2849                   |
+| 1.5373 | 311000 | 0.0407        | 0.2840                   |
+| 1.5398 | 311500 | 0.0389        | 0.2855                   |
+| 1.5422 | 312000 | 0.0403        | 0.2855                   |
+| 1.5447 | 312500 | 0.0395        | 0.2830                   |
+| 1.5472 | 313000 | 0.0419        | 0.2824                   |
+| 1.5497 | 313500 | 0.0389        | 0.2822                   |
+| 1.5521 | 314000 | 0.0382        | 0.2857                   |
+| 1.5546 | 314500 | 0.0383        | 0.2844                   |
+| 1.5571 | 315000 | 0.0415        | 0.2819                   |
+| 1.5595 | 315500 | 0.04          | 0.2820                   |
+| 1.5620 | 316000 | 0.0395        | 0.2849                   |
+| 1.5645 | 316500 | 0.0392        | 0.2841                   |
+| 1.5670 | 317000 | 0.0408        | 0.2834                   |
+| 1.5694 | 317500 | 0.0415        | 0.2816                   |
+| 1.5719 | 318000 | 0.0386        | 0.2832                   |
+| 1.5744 | 318500 | 0.039         | 0.2823                   |
+| 1.5769 | 319000 | 0.0419        | 0.2836                   |
+| 1.5793 | 319500 | 0.0389        | 0.2845                   |
+| 1.5818 | 320000 | 0.0391        | 0.2853                   |
+| 1.5843 | 320500 | 0.0381        | 0.2845                   |
+| 1.5867 | 321000 | 0.0365        | 0.2815                   |
+| 1.5892 | 321500 | 0.0416        | 0.2843                   |
+| 1.5917 | 322000 | 0.039         | 0.2849                   |
+| 1.5942 | 322500 | 0.0419        | 0.2833                   |
+| 1.5966 | 323000 | 0.0393        | 0.2834                   |
+| 1.5991 | 323500 | 0.039         | 0.2857                   |
+| 1.6016 | 324000 | 0.0394        | 0.2835                   |
+| 1.6040 | 324500 | 0.0395        | 0.2820                   |
+| 1.6065 | 325000 | 0.0413        | 0.2827                   |
+| 1.6090 | 325500 | 0.0411        | 0.2839                   |
+| 1.6115 | 326000 | 0.0387        | 0.2844                   |
+| 1.6139 | 326500 | 0.0399        | 0.2873                   |
+| 1.6164 | 327000 | 0.0401        | 0.2871                   |
+| 1.6189 | 327500 | 0.0413        | 0.2840                   |
+| 1.6213 | 328000 | 0.0385        | 0.2846                   |
+| 1.6238 | 328500 | 0.0401        | 0.2855                   |
+| 1.6263 | 329000 | 0.0402        | 0.2836                   |
+| 1.6288 | 329500 | 0.0391        | 0.2845                   |
+| 1.6312 | 330000 | 0.0395        | 0.2850                   |
+| 1.6337 | 330500 | 0.0397        | 0.2847                   |
+| 1.6362 | 331000 | 0.0387        | 0.2890                   |
+
+</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-399000/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-399000/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-399000/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-399000/sentence_bert_config.json

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

checkpoints/checkpoint-399000/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-400000/README.md

@@ -0,0 +1,1024 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:1618413
+- loss:CosineSimilarityLoss
+base_model: BSC-LT/MrBERT-es
+widget:
+- source_sentence: El término empezó como una noción informal y no rigurosa originalmente
+    pensada como una "cantidad infinitamente pequeña", y originalmente fundamentó
+    ciertos razonamientos del cálculo infinitesimal.
+  sentences:
+  - Esto obligó a Knuth a dedicar un tiempo considerable en el estudio de la tipografía.
+  - Véase también Ingreso por domiciliación Transferencia bancaria Zona Única de Pagos
+    en Euros Referencias Banca
+  - ARTÍCULO 20o.
+- source_sentence: En su deambular, Leto encontró la recién creada isla flotante de
+    Delos, que no era el continente ni una isla real, y dio a luz allí.
+  sentences:
+  - El andador para adultos tiene frenos, es regulable en altura y el respaldo y el
+    asiento vienen recubiertos de esponja.
+  - Agricultura biodinámica La agricultura biológico-dinámica, creada en 1924 por
+    Rudolf Steiner y denominada agricultura biodinámica se basa en los fundamentos
+    y propuestas de estudio vinculados a la vertiente filosófica antroposofía, cuyo
+    autor es el mismo Steiner.
+  - '"El incremento de plazas se considera inmediato", advierten los jueces.'
+- source_sentence: Piensa en Mí... pensaré en ti.
+  sentences:
+  - Para quienes participan en un proceso educativo ambiental , los hallazgos y logros
+    son el inicio de nuevas preguntas y posibilidades.
+  - El transporte externo sobre pájaros o mamíferos es facilitado por modificaciones
+    del papus como las cerdas con barbas retrorsas, crecimientos del fruto como ganchos
+    o espinas, o brácteas involucrales especializadas.
+  - C. provocó, más que en cualquier otra parte, una simbiosis entre Grecia, Irán
+    y la India.
+- source_sentence: Fuimos un grupo mixto de amigos a pasar un finde en mojacar sin
+    más intención que pasarlo bien.
+  sentences:
+  - Suena el timbre para volver a clases, todos comentan que ha sido un gran partido
+    de fútbol, el resultado final ha sido de dos a cero, mañana continuará el campeonato
+    comentan los organizadores, aun continúo extasiado por aquel encuentro, la inspectora
+    me regaña y me ordena subir a la sala, ¡Qué aburrido!, ahora viene clase de historia,
+    nos toca ver las guerras mundiales, me pregunto ¿Para qué?, con tantas batallas
+    uno se pone bélico y ve guerras donde no las hay.
+  - El museo fue creado en el año 1922 por el Dr.
+  - Hasta su muerte vivió en las inmediaciones de la casa donde nació, en Maguncia.
+- source_sentence: Muchos jugadores de poker cuenta de que cuando juegan Hold'em en
+    línea que están recibiendo mucho más que simplemente un par de horas de entretenimiento.
+    sitios web de póquer en ofrecer a los jugadores una gran variedad de métodos para
+    disfrutar de sus juegos a favor, con la posibilidad de ganar dinero en serio.
+  sentences:
+  - Las primeras máquinas programables se desarrollaron en el mundo musulmán.
+  - Las leyendas urbanas suelen dejarnos una moraleja o enseñanza y casi siempre quienes
+    las narran las modifican ligeramente o versionan para adaptarlas a la cultura
+    o idiosincrasia del lugar en el que se cuenten o difundan, de allí que existan
+    un sinfín de versiones de un mismo relato, dependiendo de la región o país del
+    que se trate.
+  - Kepler definió la inercia sólo en términos de resistencia al movimiento, basándose
+    una vez más en la presunción de que el reposo era un estado natural que no necesitaba
+    explicación.
+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: 0.5042169751755124
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.2825612135027779
+      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 = [
+    "Muchos jugadores de poker cuenta de que cuando juegan Hold'em en línea que están recibiendo mucho más que simplemente un par de horas de entretenimiento. sitios web de póquer en ofrecer a los jugadores una gran variedad de métodos para disfrutar de sus juegos a favor, con la posibilidad de ganar dinero en serio.",
+    'Las leyendas urbanas suelen dejarnos una moraleja o enseñanza y casi siempre quienes las narran las modifican ligeramente o versionan para adaptarlas a la cultura o idiosincrasia del lugar en el que se cuenten o difundan, de allí que existan un sinfín de versiones de un mismo relato, dependiendo de la región o país del que se trate.',
+    'Kepler definió la inercia sólo en términos de resistencia al movimiento, basándose una vez más en la presunción de que el reposo era un estado natural que no necesitaba explicación.',
+]
+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, 0.4352, 0.1452],
+#         [0.4352, 1.0000, 0.1371],
+#         [0.1452, 0.1371, 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      | 0.5042     |
+| **spearman_cosine** | **0.2826** |
+
+<!--
+## 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: 1,618,413 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: 38.33 tokens</li><li>max: 435 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 36.8 tokens</li><li>max: 261 tokens</li></ul> | <ul><li>min: -0.8</li><li>mean: 0.16</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                        | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                       | label                            |
+  |:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>Estadísticas Estadísticas El almacenamiento o acceso técnico que es utilizado exclusivamente con fines estadísticos.</code> | <code>Connect within Simplemente instale la aplicación, seleccione su servidor y conéctese para conectarse en cuestión de segundos.</code>                                                                                                                                                                                                                                                                                                                       | <code>0.10739796608686447</code> |
+  | <code>Saludamos con la cabeza y sonreímos.</code>                                                                                 | <code>Simbolismo: corriente de corte fantástico y onírico, surgió como reacción al naturalismo de la corriente realista e impresionista, poniendo especial énfasis en el mundo de los sueños, así como en aspectos satánicos y terroríficos, el sexo y la perversión.</code>                                                                                                                                                                                     | <code>0.05001075938344002</code> |
+  | <code>La lista de nominados se anunció hace escasos días.</code>                                                                  | <code>Es muy probable que el topónimo Egipto derive de la transcripción fonética de uno de los nombres o epítetos de Menfis, capital del antiguo Kemet bajo la Dinastía III, a saber: Hout Ka-Ptah , que quiere decir "Casa del ka de Ptah", en alusión al principal templo consagrado a este dios, que pasó al griego como Aígyptos, que, con el tiempo, designó primero al barrio en el que se encontraba, luego a toda la ciudad y más tarde al reino.</code> | <code>0.14469777047634125</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
+- `num_train_epochs`: 4
+- `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`: 1.0
+- `num_train_epochs`: 4
+- `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 |
+|:------:|:------:|:-------------:|:------------------------:|
+| 0.4671 | 94500  | 0.043         | 0.2705                   |
+| 0.4696 | 95000  | 0.0425        | 0.2708                   |
+| 0.4721 | 95500  | 0.0428        | 0.2703                   |
+| 0.4745 | 96000  | 0.0411        | 0.2689                   |
+| 0.4770 | 96500  | 0.0434        | 0.2760                   |
+| 0.4795 | 97000  | 0.0407        | 0.2733                   |
+| 0.4820 | 97500  | 0.0445        | 0.2740                   |
+| 0.4844 | 98000  | 0.0447        | 0.2694                   |
+| 0.4869 | 98500  | 0.044         | 0.2755                   |
+| 0.4894 | 99000  | 0.0425        | 0.2705                   |
+| 0.4918 | 99500  | 0.0444        | 0.2729                   |
+| 0.4943 | 100000 | 0.0448        | 0.2690                   |
+| 0.4968 | 100500 | 0.0432        | 0.2747                   |
+| 0.4993 | 101000 | 0.0426        | 0.2739                   |
+| 0.5017 | 101500 | 0.043         | 0.2773                   |
+| 0.5042 | 102000 | 0.0432        | 0.2774                   |
+| 0.5067 | 102500 | 0.042         | 0.2776                   |
+| 0.5091 | 103000 | 0.0441        | 0.2755                   |
+| 0.5116 | 103500 | 0.0441        | 0.2782                   |
+| 0.5141 | 104000 | 0.0436        | 0.2784                   |
+| 0.5166 | 104500 | 0.0446        | 0.2763                   |
+| 0.5190 | 105000 | 0.0435        | 0.2779                   |
+| 0.5215 | 105500 | 0.0434        | 0.2763                   |
+| 0.5240 | 106000 | 0.0426        | 0.2744                   |
+| 0.5264 | 106500 | 0.0442        | 0.2748                   |
+| 0.5289 | 107000 | 0.0468        | 0.2743                   |
+| 0.5314 | 107500 | 0.0428        | 0.2684                   |
+| 0.5339 | 108000 | 0.042         | 0.2719                   |
+| 0.5363 | 108500 | 0.0435        | 0.2761                   |
+| 0.5388 | 109000 | 0.0438        | 0.2765                   |
+| 0.5413 | 109500 | 0.0442        | 0.2731                   |
+| 0.5437 | 110000 | 0.0443        | 0.2719                   |
+| 0.5462 | 110500 | 0.0411        | 0.2721                   |
+| 0.5487 | 111000 | 0.0431        | 0.2752                   |
+| 0.5512 | 111500 | 0.0435        | 0.2743                   |
+| 0.5536 | 112000 | 0.0444        | 0.2701                   |
+| 0.5561 | 112500 | 0.0423        | 0.2720                   |
+| 0.5586 | 113000 | 0.0446        | 0.2728                   |
+| 0.5610 | 113500 | 0.042         | 0.2734                   |
+| 0.5635 | 114000 | 0.0438        | 0.2763                   |
+| 0.5660 | 114500 | 0.0412        | 0.2774                   |
+| 0.5685 | 115000 | 0.0417        | 0.2787                   |
+| 0.5709 | 115500 | 0.0448        | 0.2775                   |
+| 0.5734 | 116000 | 0.0446        | 0.2755                   |
+| 0.5759 | 116500 | 0.0419        | 0.2768                   |
+| 0.5783 | 117000 | 0.0435        | 0.2753                   |
+| 0.5808 | 117500 | 0.0451        | 0.2747                   |
+| 0.5833 | 118000 | 0.0439        | 0.2768                   |
+| 0.5858 | 118500 | 0.0438        | 0.2773                   |
+| 0.5882 | 119000 | 0.043         | 0.2770                   |
+| 0.5907 | 119500 | 0.0458        | 0.2736                   |
+| 0.5932 | 120000 | 0.0419        | 0.2746                   |
+| 0.5956 | 120500 | 0.0441        | 0.2730                   |
+| 0.5981 | 121000 | 0.0425        | 0.2752                   |
+| 0.6006 | 121500 | 0.0414        | 0.2733                   |
+| 0.6031 | 122000 | 0.0407        | 0.2732                   |
+| 0.6055 | 122500 | 0.0445        | 0.2732                   |
+| 0.6080 | 123000 | 0.0434        | 0.2713                   |
+| 0.6105 | 123500 | 0.0439        | 0.2720                   |
+| 0.6129 | 124000 | 0.0438        | 0.2761                   |
+| 0.6154 | 124500 | 0.0418        | 0.2754                   |
+| 0.6179 | 125000 | 0.0423        | 0.2763                   |
+| 0.6204 | 125500 | 0.0426        | 0.2773                   |
+| 0.6228 | 126000 | 0.0448        | 0.2732                   |
+| 0.6253 | 126500 | 0.0424        | 0.2691                   |
+| 0.6278 | 127000 | 0.0451        | 0.2760                   |
+| 0.6302 | 127500 | 0.0437        | 0.2713                   |
+| 0.6327 | 128000 | 0.0429        | 0.2690                   |
+| 0.6352 | 128500 | 0.0439        | 0.2691                   |
+| 0.6377 | 129000 | 0.0442        | 0.2769                   |
+| 0.6401 | 129500 | 0.042         | 0.2750                   |
+| 0.6426 | 130000 | 0.0462        | 0.2731                   |
+| 0.6451 | 130500 | 0.043         | 0.2750                   |
+| 0.6475 | 131000 | 0.0445        | 0.2770                   |
+| 0.6500 | 131500 | 0.0424        | 0.2753                   |
+| 0.6525 | 132000 | 0.0451        | 0.2734                   |
+| 0.6550 | 132500 | 0.0453        | 0.2768                   |
+| 0.6574 | 133000 | 0.0453        | 0.2820                   |
+| 0.6599 | 133500 | 0.0424        | 0.2821                   |
+| 0.6624 | 134000 | 0.0446        | 0.2806                   |
+| 0.6648 | 134500 | 0.0433        | 0.2781                   |
+| 0.6673 | 135000 | 0.0443        | 0.2792                   |
+| 0.6698 | 135500 | 0.0447        | 0.2770                   |
+| 0.6723 | 136000 | 0.0396        | 0.2718                   |
+| 0.6747 | 136500 | 0.0418        | 0.2736                   |
+| 0.6772 | 137000 | 0.0428        | 0.2774                   |
+| 0.6797 | 137500 | 0.0444        | 0.2762                   |
+| 0.6821 | 138000 | 0.0409        | 0.2725                   |
+| 0.6846 | 138500 | 0.0429        | 0.2731                   |
+| 0.6871 | 139000 | 0.0447        | 0.2769                   |
+| 0.6896 | 139500 | 0.0454        | 0.2744                   |
+| 0.6920 | 140000 | 0.0443        | 0.2822                   |
+| 0.6945 | 140500 | 0.045         | 0.2753                   |
+| 0.6970 | 141000 | 0.043         | 0.2780                   |
+| 0.6994 | 141500 | 0.042         | 0.2765                   |
+| 0.7019 | 142000 | 0.0427        | 0.2762                   |
+| 0.7044 | 142500 | 0.0404        | 0.2809                   |
+| 0.7069 | 143000 | 0.045         | 0.2784                   |
+| 0.7093 | 143500 | 0.046         | 0.2781                   |
+| 0.7118 | 144000 | 0.0449        | 0.2733                   |
+| 0.7143 | 144500 | 0.0414        | 0.2736                   |
+| 0.7168 | 145000 | 0.0472        | 0.2751                   |
+| 0.7192 | 145500 | 0.0429        | 0.2782                   |
+| 0.7217 | 146000 | 0.0429        | 0.2781                   |
+| 0.7242 | 146500 | 0.0446        | 0.2750                   |
+| 0.7266 | 147000 | 0.0429        | 0.2773                   |
+| 0.7291 | 147500 | 0.0484        | 0.2808                   |
+| 0.7316 | 148000 | 0.0439        | 0.2759                   |
+| 0.7341 | 148500 | 0.0429        | 0.2764                   |
+| 0.7365 | 149000 | 0.0453        | 0.2785                   |
+| 0.7390 | 149500 | 0.043         | 0.2756                   |
+| 0.7415 | 150000 | 0.0438        | 0.2765                   |
+| 0.7439 | 150500 | 0.0446        | 0.2731                   |
+| 0.7464 | 151000 | 0.0443        | 0.2759                   |
+| 0.7489 | 151500 | 0.0438        | 0.2725                   |
+| 0.7514 | 152000 | 0.0463        | 0.2756                   |
+| 0.7538 | 152500 | 0.046         | 0.2774                   |
+| 0.7563 | 153000 | 0.0423        | 0.2769                   |
+| 0.7588 | 153500 | 0.0453        | 0.2752                   |
+| 0.7612 | 154000 | 0.046         | 0.2726                   |
+| 0.7637 | 154500 | 0.0432        | 0.2763                   |
+| 0.7662 | 155000 | 0.0462        | 0.2786                   |
+| 0.7687 | 155500 | 0.0455        | 0.2775                   |
+| 0.7711 | 156000 | 0.043         | 0.2783                   |
+| 0.7736 | 156500 | 0.0442        | 0.2784                   |
+| 0.7761 | 157000 | 0.0437        | 0.2769                   |
+| 0.7785 | 157500 | 0.044         | 0.2812                   |
+| 0.7810 | 158000 | 0.0443        | 0.2797                   |
+| 0.7835 | 158500 | 0.0436        | 0.2783                   |
+| 0.7860 | 159000 | 0.0435        | 0.2847                   |
+| 0.7884 | 159500 | 0.0438        | 0.2835                   |
+| 0.7909 | 160000 | 0.0446        | 0.2815                   |
+| 0.7934 | 160500 | 0.0434        | 0.2840                   |
+| 0.7958 | 161000 | 0.0455        | 0.2833                   |
+| 0.7983 | 161500 | 0.043         | 0.2845                   |
+| 0.8008 | 162000 | 0.0436        | 0.2845                   |
+| 0.8033 | 162500 | 0.0443        | 0.2823                   |
+| 0.8057 | 163000 | 0.0441        | 0.2812                   |
+| 0.8082 | 163500 | 0.0435        | 0.2777                   |
+| 0.8107 | 164000 | 0.0421        | 0.2740                   |
+| 0.8131 | 164500 | 0.0437        | 0.2738                   |
+| 0.8156 | 165000 | 0.0457        | 0.2745                   |
+| 0.8181 | 165500 | 0.0453        | 0.2815                   |
+| 0.8206 | 166000 | 0.0427        | 0.2788                   |
+| 0.8230 | 166500 | 0.045         | 0.2809                   |
+| 0.8255 | 167000 | 0.0439        | 0.2818                   |
+| 0.8280 | 167500 | 0.045         | 0.2795                   |
+| 0.8304 | 168000 | 0.0422        | 0.2802                   |
+| 0.8329 | 168500 | 0.0449        | 0.2783                   |
+| 0.8354 | 169000 | 0.0437        | 0.2765                   |
+| 0.8379 | 169500 | 0.0445        | 0.2788                   |
+| 0.8403 | 170000 | 0.0419        | 0.2832                   |
+| 0.8428 | 170500 | 0.0423        | 0.2775                   |
+| 0.8453 | 171000 | 0.0411        | 0.2804                   |
+| 0.8477 | 171500 | 0.0437        | 0.2755                   |
+| 0.8502 | 172000 | 0.044         | 0.2774                   |
+| 0.8527 | 172500 | 0.0447        | 0.2740                   |
+| 0.8552 | 173000 | 0.0444        | 0.2757                   |
+| 0.8576 | 173500 | 0.0419        | 0.2750                   |
+| 0.8601 | 174000 | 0.0461        | 0.2743                   |
+| 0.8626 | 174500 | 0.0455        | 0.2761                   |
+| 0.8650 | 175000 | 0.042         | 0.2745                   |
+| 0.8675 | 175500 | 0.0466        | 0.2757                   |
+| 0.8700 | 176000 | 0.0439        | 0.2744                   |
+| 0.8725 | 176500 | 0.0423        | 0.2771                   |
+| 0.8749 | 177000 | 0.0438        | 0.2723                   |
+| 0.8774 | 177500 | 0.0438        | 0.2771                   |
+| 0.8799 | 178000 | 0.0417        | 0.2777                   |
+| 0.8823 | 178500 | 0.044         | 0.2780                   |
+| 0.8848 | 179000 | 0.0426        | 0.2746                   |
+| 0.8873 | 179500 | 0.0446        | 0.2758                   |
+| 0.8898 | 180000 | 0.0451        | 0.2767                   |
+| 0.8922 | 180500 | 0.0432        | 0.2770                   |
+| 0.8947 | 181000 | 0.0425        | 0.2749                   |
+| 0.8972 | 181500 | 0.0447        | 0.2758                   |
+| 0.8996 | 182000 | 0.0422        | 0.2798                   |
+| 0.9021 | 182500 | 0.045         | 0.2789                   |
+| 0.9046 | 183000 | 0.044         | 0.2786                   |
+| 0.9071 | 183500 | 0.0436        | 0.2781                   |
+| 0.9095 | 184000 | 0.046         | 0.2777                   |
+| 0.9120 | 184500 | 0.0443        | 0.2773                   |
+| 0.9145 | 185000 | 0.0445        | 0.2753                   |
+| 0.9169 | 185500 | 0.043         | 0.2767                   |
+| 0.9194 | 186000 | 0.0454        | 0.2743                   |
+| 0.9219 | 186500 | 0.0433        | 0.2775                   |
+| 0.9244 | 187000 | 0.0443        | 0.2775                   |
+| 0.9268 | 187500 | 0.0432        | 0.2765                   |
+| 0.9293 | 188000 | 0.0434        | 0.2793                   |
+| 0.9318 | 188500 | 0.0463        | 0.2801                   |
+| 0.9342 | 189000 | 0.0439        | 0.2795                   |
+| 0.9367 | 189500 | 0.0423        | 0.2812                   |
+| 0.9392 | 190000 | 0.0441        | 0.2768                   |
+| 0.9417 | 190500 | 0.0446        | 0.2754                   |
+| 0.9441 | 191000 | 0.0436        | 0.2814                   |
+| 0.9466 | 191500 | 0.045         | 0.2795                   |
+| 0.9491 | 192000 | 0.0445        | 0.2794                   |
+| 0.9515 | 192500 | 0.0429        | 0.2827                   |
+| 0.9540 | 193000 | 0.043         | 0.2815                   |
+| 0.9565 | 193500 | 0.0446        | 0.2827                   |
+| 0.9590 | 194000 | 0.0456        | 0.2822                   |
+| 0.9614 | 194500 | 0.0406        | 0.2828                   |
+| 0.9639 | 195000 | 0.0444        | 0.2844                   |
+| 0.9664 | 195500 | 0.0448        | 0.2785                   |
+| 0.9688 | 196000 | 0.0427        | 0.2784                   |
+| 0.9713 | 196500 | 0.0453        | 0.2788                   |
+| 0.9738 | 197000 | 0.0443        | 0.2751                   |
+| 0.9763 | 197500 | 0.0444        | 0.2754                   |
+| 0.9787 | 198000 | 0.0448        | 0.2745                   |
+| 0.9812 | 198500 | 0.0445        | 0.2752                   |
+| 0.9837 | 199000 | 0.046         | 0.2710                   |
+| 0.9861 | 199500 | 0.0459        | 0.2732                   |
+| 0.9886 | 200000 | 0.0394        | 0.2729                   |
+| 0.9911 | 200500 | 0.045         | 0.2737                   |
+| 0.9936 | 201000 | 0.0434        | 0.2753                   |
+| 0.9960 | 201500 | 0.0465        | 0.2771                   |
+| 0.9985 | 202000 | 0.0443        | 0.2755                   |
+| 1.0    | 202302 | -             | 0.2735                   |
+| 1.0010 | 202500 | 0.0406        | 0.2746                   |
+| 1.0035 | 203000 | 0.0358        | 0.2751                   |
+| 1.0059 | 203500 | 0.039         | 0.2739                   |
+| 1.0084 | 204000 | 0.0389        | 0.2740                   |
+| 1.0109 | 204500 | 0.0382        | 0.2736                   |
+| 1.0133 | 205000 | 0.0374        | 0.2714                   |
+| 1.0158 | 205500 | 0.0393        | 0.2745                   |
+| 1.0183 | 206000 | 0.0388        | 0.2759                   |
+| 1.0208 | 206500 | 0.0398        | 0.2765                   |
+| 1.0232 | 207000 | 0.0399        | 0.2772                   |
+| 1.0257 | 207500 | 0.0403        | 0.2757                   |
+| 1.0282 | 208000 | 0.0383        | 0.2786                   |
+| 1.0306 | 208500 | 0.0376        | 0.2771                   |
+| 1.0331 | 209000 | 0.0418        | 0.2761                   |
+| 1.0356 | 209500 | 0.0381        | 0.2768                   |
+| 1.0381 | 210000 | 0.038         | 0.2761                   |
+| 1.0405 | 210500 | 0.0386        | 0.2735                   |
+| 1.0430 | 211000 | 0.0378        | 0.2768                   |
+| 1.0455 | 211500 | 0.0389        | 0.2764                   |
+| 1.0479 | 212000 | 0.0378        | 0.2757                   |
+| 1.0504 | 212500 | 0.039         | 0.2743                   |
+| 1.0529 | 213000 | 0.0367        | 0.2749                   |
+| 1.0554 | 213500 | 0.0394        | 0.2747                   |
+| 1.0578 | 214000 | 0.0372        | 0.2740                   |
+| 1.0603 | 214500 | 0.039         | 0.2757                   |
+| 1.0628 | 215000 | 0.0396        | 0.2813                   |
+| 1.0652 | 215500 | 0.0403        | 0.2794                   |
+| 1.0677 | 216000 | 0.0387        | 0.2771                   |
+| 1.0702 | 216500 | 0.0381        | 0.2733                   |
+| 1.0727 | 217000 | 0.0406        | 0.2717                   |
+| 1.0751 | 217500 | 0.0408        | 0.2749                   |
+| 1.0776 | 218000 | 0.0401        | 0.2750                   |
+| 1.0801 | 218500 | 0.0363        | 0.2724                   |
+| 1.0825 | 219000 | 0.0392        | 0.2745                   |
+| 1.0850 | 219500 | 0.0386        | 0.2726                   |
+| 1.0875 | 220000 | 0.0413        | 0.2741                   |
+| 1.0900 | 220500 | 0.04          | 0.2753                   |
+| 1.0924 | 221000 | 0.0371        | 0.2772                   |
+| 1.0949 | 221500 | 0.0392        | 0.2734                   |
+| 1.0974 | 222000 | 0.0397        | 0.2764                   |
+| 1.0998 | 222500 | 0.0406        | 0.2732                   |
+| 1.1023 | 223000 | 0.0396        | 0.2730                   |
+| 1.1048 | 223500 | 0.0396        | 0.2756                   |
+| 1.1073 | 224000 | 0.0389        | 0.2771                   |
+| 1.1097 | 224500 | 0.0402        | 0.2766                   |
+| 1.1122 | 225000 | 0.0386        | 0.2774                   |
+| 1.1147 | 225500 | 0.0389        | 0.2782                   |
+| 1.1171 | 226000 | 0.0372        | 0.2768                   |
+| 1.1196 | 226500 | 0.0384        | 0.2726                   |
+| 1.1221 | 227000 | 0.0424        | 0.2734                   |
+| 1.1246 | 227500 | 0.041         | 0.2732                   |
+| 1.1270 | 228000 | 0.0392        | 0.2717                   |
+| 1.1295 | 228500 | 0.039         | 0.2743                   |
+| 1.1320 | 229000 | 0.0402        | 0.2721                   |
+| 1.1344 | 229500 | 0.0403        | 0.2733                   |
+| 1.1369 | 230000 | 0.0393        | 0.2727                   |
+| 1.1394 | 230500 | 0.039         | 0.2755                   |
+| 1.1419 | 231000 | 0.0382        | 0.2757                   |
+| 1.1443 | 231500 | 0.036         | 0.2760                   |
+| 1.1468 | 232000 | 0.0408        | 0.2762                   |
+| 1.1493 | 232500 | 0.0393        | 0.2733                   |
+| 1.1517 | 233000 | 0.0385        | 0.2750                   |
+| 1.1542 | 233500 | 0.0398        | 0.2772                   |
+| 1.1567 | 234000 | 0.0411        | 0.2751                   |
+| 1.1592 | 234500 | 0.0404        | 0.2747                   |
+| 1.1616 | 235000 | 0.0393        | 0.2765                   |
+| 1.1641 | 235500 | 0.0389        | 0.2715                   |
+| 1.1666 | 236000 | 0.0379        | 0.2759                   |
+| 1.1690 | 236500 | 0.0392        | 0.2740                   |
+| 1.1715 | 237000 | 0.039         | 0.2732                   |
+| 1.1740 | 237500 | 0.041         | 0.2703                   |
+| 1.1765 | 238000 | 0.0403        | 0.2748                   |
+| 1.1789 | 238500 | 0.0388        | 0.2753                   |
+| 1.1814 | 239000 | 0.0405        | 0.2744                   |
+| 1.1839 | 239500 | 0.039         | 0.2769                   |
+| 1.1863 | 240000 | 0.0405        | 0.2746                   |
+| 1.1888 | 240500 | 0.0389        | 0.2738                   |
+| 1.1913 | 241000 | 0.0393        | 0.2781                   |
+| 1.1938 | 241500 | 0.0374        | 0.2794                   |
+| 1.1962 | 242000 | 0.0404        | 0.2747                   |
+| 1.1987 | 242500 | 0.0388        | 0.2763                   |
+| 1.2012 | 243000 | 0.0387        | 0.2775                   |
+| 1.2036 | 243500 | 0.0401        | 0.2723                   |
+| 1.2061 | 244000 | 0.0394        | 0.2695                   |
+| 1.2086 | 244500 | 0.0405        | 0.2735                   |
+| 1.2111 | 245000 | 0.0408        | 0.2754                   |
+| 1.2135 | 245500 | 0.0388        | 0.2708                   |
+| 1.2160 | 246000 | 0.0383        | 0.2738                   |
+| 1.2185 | 246500 | 0.0416        | 0.2736                   |
+| 1.2209 | 247000 | 0.0379        | 0.2763                   |
+| 1.2234 | 247500 | 0.0415        | 0.2756                   |
+| 1.2259 | 248000 | 0.0378        | 0.2754                   |
+| 1.2284 | 248500 | 0.0392        | 0.2772                   |
+| 1.2308 | 249000 | 0.0391        | 0.2757                   |
+| 1.2333 | 249500 | 0.0386        | 0.2717                   |
+| 1.2358 | 250000 | 0.0416        | 0.2769                   |
+| 1.2382 | 250500 | 0.0404        | 0.2734                   |
+| 1.2407 | 251000 | 0.0379        | 0.2749                   |
+| 1.2432 | 251500 | 0.0387        | 0.2743                   |
+| 1.2457 | 252000 | 0.0421        | 0.2751                   |
+| 1.2481 | 252500 | 0.0391        | 0.2753                   |
+| 1.2506 | 253000 | 0.039         | 0.2755                   |
+| 1.2531 | 253500 | 0.042         | 0.2725                   |
+| 1.2555 | 254000 | 0.0394        | 0.2731                   |
+| 1.2580 | 254500 | 0.0398        | 0.2758                   |
+| 1.2605 | 255000 | 0.0404        | 0.2786                   |
+| 1.2630 | 255500 | 0.0398        | 0.2783                   |
+| 1.2654 | 256000 | 0.0392        | 0.2779                   |
+| 1.2679 | 256500 | 0.0386        | 0.2785                   |
+| 1.2704 | 257000 | 0.0402        | 0.2764                   |
+| 1.2728 | 257500 | 0.0376        | 0.2792                   |
+| 1.2753 | 258000 | 0.0387        | 0.2791                   |
+| 1.2778 | 258500 | 0.0397        | 0.2808                   |
+| 1.2803 | 259000 | 0.038         | 0.2802                   |
+| 1.2827 | 259500 | 0.0389        | 0.2795                   |
+| 1.2852 | 260000 | 0.0412        | 0.2771                   |
+| 1.2877 | 260500 | 0.0394        | 0.2777                   |
+| 1.2902 | 261000 | 0.0426        | 0.2792                   |
+| 1.2926 | 261500 | 0.0391        | 0.2772                   |
+| 1.2951 | 262000 | 0.0382        | 0.2783                   |
+| 1.2976 | 262500 | 0.0385        | 0.2789                   |
+| 1.3000 | 263000 | 0.0401        | 0.2812                   |
+| 1.3025 | 263500 | 0.0392        | 0.2826                   |
+| 1.3050 | 264000 | 0.0403        | 0.2813                   |
+| 1.3075 | 264500 | 0.0394        | 0.2779                   |
+| 1.3099 | 265000 | 0.0397        | 0.2832                   |
+| 1.3124 | 265500 | 0.0407        | 0.2785                   |
+| 1.3149 | 266000 | 0.0412        | 0.2809                   |
+| 1.3173 | 266500 | 0.0399        | 0.2805                   |
+| 1.3198 | 267000 | 0.0406        | 0.2803                   |
+| 1.3223 | 267500 | 0.0397        | 0.2812                   |
+| 1.3248 | 268000 | 0.0413        | 0.2819                   |
+| 1.3272 | 268500 | 0.0398        | 0.2788                   |
+| 1.3297 | 269000 | 0.0402        | 0.2814                   |
+| 1.3322 | 269500 | 0.0387        | 0.2825                   |
+| 1.3346 | 270000 | 0.0425        | 0.2789                   |
+| 1.3371 | 270500 | 0.038         | 0.2793                   |
+| 1.3396 | 271000 | 0.0377        | 0.2775                   |
+| 1.3421 | 271500 | 0.0414        | 0.2769                   |
+| 1.3445 | 272000 | 0.0389        | 0.2735                   |
+| 1.3470 | 272500 | 0.0386        | 0.2785                   |
+| 1.3495 | 273000 | 0.0401        | 0.2813                   |
+| 1.3519 | 273500 | 0.0383        | 0.2801                   |
+| 1.3544 | 274000 | 0.0396        | 0.2796                   |
+| 1.3569 | 274500 | 0.0396        | 0.2793                   |
+| 1.3594 | 275000 | 0.0424        | 0.2814                   |
+| 1.3618 | 275500 | 0.0418        | 0.2814                   |
+| 1.3643 | 276000 | 0.0383        | 0.2787                   |
+| 1.3668 | 276500 | 0.04          | 0.2797                   |
+| 1.3692 | 277000 | 0.0414        | 0.2810                   |
+| 1.3717 | 277500 | 0.0379        | 0.2848                   |
+| 1.3742 | 278000 | 0.0381        | 0.2846                   |
+| 1.3767 | 278500 | 0.0383        | 0.2814                   |
+| 1.3791 | 279000 | 0.039         | 0.2818                   |
+| 1.3816 | 279500 | 0.0388        | 0.2792                   |
+| 1.3841 | 280000 | 0.0408        | 0.2784                   |
+| 1.3865 | 280500 | 0.0389        | 0.2814                   |
+| 1.3890 | 281000 | 0.0426        | 0.2794                   |
+| 1.3915 | 281500 | 0.0392        | 0.2780                   |
+| 1.3940 | 282000 | 0.0405        | 0.2778                   |
+| 1.3964 | 282500 | 0.0407        | 0.2769                   |
+| 1.3989 | 283000 | 0.0396        | 0.2730                   |
+| 1.4014 | 283500 | 0.0376        | 0.2770                   |
+| 1.4038 | 284000 | 0.0399        | 0.2791                   |
+| 1.4063 | 284500 | 0.0405        | 0.2791                   |
+| 1.4088 | 285000 | 0.0382        | 0.2804                   |
+| 1.4113 | 285500 | 0.0388        | 0.2835                   |
+| 1.4137 | 286000 | 0.0394        | 0.2784                   |
+| 1.4162 | 286500 | 0.0388        | 0.2813                   |
+| 1.4187 | 287000 | 0.0397        | 0.2813                   |
+| 1.4211 | 287500 | 0.0404        | 0.2808                   |
+| 1.4236 | 288000 | 0.0374        | 0.2792                   |
+| 1.4261 | 288500 | 0.041         | 0.2724                   |
+| 1.4286 | 289000 | 0.0409        | 0.2770                   |
+| 1.4310 | 289500 | 0.04          | 0.2789                   |
+| 1.4335 | 290000 | 0.0412        | 0.2754                   |
+| 1.4360 | 290500 | 0.0404        | 0.2780                   |
+| 1.4384 | 291000 | 0.0406        | 0.2794                   |
+| 1.4409 | 291500 | 0.0387        | 0.2776                   |
+| 1.4434 | 292000 | 0.037         | 0.2801                   |
+| 1.4459 | 292500 | 0.0394        | 0.2778                   |
+| 1.4483 | 293000 | 0.0406        | 0.2786                   |
+| 1.4508 | 293500 | 0.0401        | 0.2827                   |
+| 1.4533 | 294000 | 0.0388        | 0.2770                   |
+| 1.4557 | 294500 | 0.0377        | 0.2768                   |
+| 1.4582 | 295000 | 0.0386        | 0.2773                   |
+| 1.4607 | 295500 | 0.04          | 0.2783                   |
+| 1.4632 | 296000 | 0.0402        | 0.2780                   |
+| 1.4656 | 296500 | 0.0401        | 0.2820                   |
+| 1.4681 | 297000 | 0.0393        | 0.2790                   |
+| 1.4706 | 297500 | 0.0394        | 0.2787                   |
+| 1.4730 | 298000 | 0.0392        | 0.2759                   |
+| 1.4755 | 298500 | 0.0396        | 0.2767                   |
+| 1.4780 | 299000 | 0.0379        | 0.2752                   |
+| 1.4805 | 299500 | 0.039         | 0.2742                   |
+| 1.4829 | 300000 | 0.0383        | 0.2750                   |
+| 1.4854 | 300500 | 0.0398        | 0.2741                   |
+| 1.4879 | 301000 | 0.0394        | 0.2749                   |
+| 1.4903 | 301500 | 0.0416        | 0.2728                   |
+| 1.4928 | 302000 | 0.0388        | 0.2751                   |
+| 1.4953 | 302500 | 0.041         | 0.2759                   |
+| 1.4978 | 303000 | 0.0405        | 0.2744                   |
+| 1.5002 | 303500 | 0.0397        | 0.2734                   |
+| 1.5027 | 304000 | 0.0413        | 0.2762                   |
+| 1.5052 | 304500 | 0.0412        | 0.2754                   |
+| 1.5076 | 305000 | 0.0386        | 0.2787                   |
+| 1.5101 | 305500 | 0.0377        | 0.2790                   |
+| 1.5126 | 306000 | 0.0395        | 0.2784                   |
+| 1.5151 | 306500 | 0.0423        | 0.2797                   |
+| 1.5175 | 307000 | 0.0396        | 0.2819                   |
+| 1.5200 | 307500 | 0.0395        | 0.2810                   |
+| 1.5225 | 308000 | 0.04          | 0.2828                   |
+| 1.5249 | 308500 | 0.0373        | 0.2840                   |
+| 1.5274 | 309000 | 0.0385        | 0.2873                   |
+| 1.5299 | 309500 | 0.0401        | 0.2854                   |
+| 1.5324 | 310000 | 0.0404        | 0.2851                   |
+| 1.5348 | 310500 | 0.0404        | 0.2849                   |
+| 1.5373 | 311000 | 0.0407        | 0.2840                   |
+| 1.5398 | 311500 | 0.0389        | 0.2855                   |
+| 1.5422 | 312000 | 0.0403        | 0.2855                   |
+| 1.5447 | 312500 | 0.0395        | 0.2830                   |
+| 1.5472 | 313000 | 0.0419        | 0.2824                   |
+| 1.5497 | 313500 | 0.0389        | 0.2822                   |
+| 1.5521 | 314000 | 0.0382        | 0.2857                   |
+| 1.5546 | 314500 | 0.0383        | 0.2844                   |
+| 1.5571 | 315000 | 0.0415        | 0.2819                   |
+| 1.5595 | 315500 | 0.04          | 0.2820                   |
+| 1.5620 | 316000 | 0.0395        | 0.2849                   |
+| 1.5645 | 316500 | 0.0392        | 0.2841                   |
+| 1.5670 | 317000 | 0.0408        | 0.2834                   |
+| 1.5694 | 317500 | 0.0415        | 0.2816                   |
+| 1.5719 | 318000 | 0.0386        | 0.2832                   |
+| 1.5744 | 318500 | 0.039         | 0.2823                   |
+| 1.5769 | 319000 | 0.0419        | 0.2836                   |
+| 1.5793 | 319500 | 0.0389        | 0.2845                   |
+| 1.5818 | 320000 | 0.0391        | 0.2853                   |
+| 1.5843 | 320500 | 0.0381        | 0.2845                   |
+| 1.5867 | 321000 | 0.0365        | 0.2815                   |
+| 1.5892 | 321500 | 0.0416        | 0.2843                   |
+| 1.5917 | 322000 | 0.039         | 0.2849                   |
+| 1.5942 | 322500 | 0.0419        | 0.2833                   |
+| 1.5966 | 323000 | 0.0393        | 0.2834                   |
+| 1.5991 | 323500 | 0.039         | 0.2857                   |
+| 1.6016 | 324000 | 0.0394        | 0.2835                   |
+| 1.6040 | 324500 | 0.0395        | 0.2820                   |
+| 1.6065 | 325000 | 0.0413        | 0.2827                   |
+| 1.6090 | 325500 | 0.0411        | 0.2839                   |
+| 1.6115 | 326000 | 0.0387        | 0.2844                   |
+| 1.6139 | 326500 | 0.0399        | 0.2873                   |
+| 1.6164 | 327000 | 0.0401        | 0.2871                   |
+| 1.6189 | 327500 | 0.0413        | 0.2840                   |
+| 1.6213 | 328000 | 0.0385        | 0.2846                   |
+| 1.6238 | 328500 | 0.0401        | 0.2855                   |
+| 1.6263 | 329000 | 0.0402        | 0.2836                   |
+| 1.6288 | 329500 | 0.0391        | 0.2845                   |
+| 1.6312 | 330000 | 0.0395        | 0.2850                   |
+| 1.6337 | 330500 | 0.0397        | 0.2847                   |
+| 1.6362 | 331000 | 0.0387        | 0.2890                   |
+| 1.6386 | 331500 | 0.0387        | 0.2838                   |
+| 1.6411 | 332000 | 0.0395        | 0.2843                   |
+| 1.6436 | 332500 | 0.0381        | 0.2848                   |
+| 1.6461 | 333000 | 0.0389        | 0.2855                   |
+| 1.6485 | 333500 | 0.0377        | 0.2842                   |
+| 1.6510 | 334000 | 0.0385        | 0.2827                   |
+| 1.6535 | 334500 | 0.0408        | 0.2840                   |
+| 1.6559 | 335000 | 0.0396        | 0.2852                   |
+| 1.6584 | 335500 | 0.0395        | 0.2850                   |
+| 1.6609 | 336000 | 0.042         | 0.2832                   |
+| 1.6634 | 336500 | 0.0403        | 0.2855                   |
+| 1.6658 | 337000 | 0.0386        | 0.2840                   |
+| 1.6683 | 337500 | 0.0409        | 0.2804                   |
+| 1.6708 | 338000 | 0.0412        | 0.2847                   |
+| 1.6732 | 338500 | 0.0411        | 0.2826                   |
+| 1.6757 | 339000 | 0.0405        | 0.2841                   |
+| 1.6782 | 339500 | 0.0393        | 0.2810                   |
+| 1.6807 | 340000 | 0.0398        | 0.2841                   |
+| 1.6831 | 340500 | 0.0392        | 0.2844                   |
+| 1.6856 | 341000 | 0.0411        | 0.2842                   |
+| 1.6881 | 341500 | 0.0405        | 0.2851                   |
+| 1.6905 | 342000 | 0.041         | 0.2817                   |
+| 1.6930 | 342500 | 0.0388        | 0.2815                   |
+| 1.6955 | 343000 | 0.0413        | 0.2806                   |
+| 1.6980 | 343500 | 0.0388        | 0.2852                   |
+| 1.7004 | 344000 | 0.0413        | 0.2835                   |
+| 1.7029 | 344500 | 0.0405        | 0.2784                   |
+| 1.7054 | 345000 | 0.0396        | 0.2827                   |
+| 1.7078 | 345500 | 0.0403        | 0.2829                   |
+| 1.7103 | 346000 | 0.0411        | 0.2823                   |
+| 1.7128 | 346500 | 0.043         | 0.2827                   |
+| 1.7153 | 347000 | 0.0402        | 0.2820                   |
+| 1.7177 | 347500 | 0.0412        | 0.2820                   |
+| 1.7202 | 348000 | 0.0414        | 0.2804                   |
+| 1.7227 | 348500 | 0.0403        | 0.2796                   |
+| 1.7251 | 349000 | 0.0385        | 0.2799                   |
+| 1.7276 | 349500 | 0.0393        | 0.2800                   |
+| 1.7301 | 350000 | 0.0394        | 0.2790                   |
+| 1.7326 | 350500 | 0.0424        | 0.2812                   |
+| 1.7350 | 351000 | 0.0424        | 0.2832                   |
+| 1.7375 | 351500 | 0.0392        | 0.2852                   |
+| 1.7400 | 352000 | 0.0394        | 0.2858                   |
+| 1.7424 | 352500 | 0.04          | 0.2847                   |
+| 1.7449 | 353000 | 0.0405        | 0.2830                   |
+| 1.7474 | 353500 | 0.0401        | 0.2835                   |
+| 1.7499 | 354000 | 0.0379        | 0.2835                   |
+| 1.7523 | 354500 | 0.0397        | 0.2836                   |
+| 1.7548 | 355000 | 0.0395        | 0.2808                   |
+| 1.7573 | 355500 | 0.0399        | 0.2796                   |
+| 1.7597 | 356000 | 0.0381        | 0.2817                   |
+| 1.7622 | 356500 | 0.0394        | 0.2811                   |
+| 1.7647 | 357000 | 0.0397        | 0.2852                   |
+| 1.7672 | 357500 | 0.0416        | 0.2840                   |
+| 1.7696 | 358000 | 0.0393        | 0.2846                   |
+| 1.7721 | 358500 | 0.0398        | 0.2832                   |
+| 1.7746 | 359000 | 0.0411        | 0.2838                   |
+| 1.7770 | 359500 | 0.0394        | 0.2837                   |
+| 1.7795 | 360000 | 0.0398        | 0.2825                   |
+| 1.7820 | 360500 | 0.0413        | 0.2821                   |
+| 1.7845 | 361000 | 0.0382        | 0.2811                   |
+| 1.7869 | 361500 | 0.0399        | 0.2831                   |
+| 1.7894 | 362000 | 0.0414        | 0.2809                   |
+| 1.7919 | 362500 | 0.0391        | 0.2821                   |
+| 1.7943 | 363000 | 0.0392        | 0.2836                   |
+| 1.7968 | 363500 | 0.0377        | 0.2850                   |
+| 1.7993 | 364000 | 0.0376        | 0.2864                   |
+| 1.8018 | 364500 | 0.0391        | 0.2861                   |
+| 1.8042 | 365000 | 0.0403        | 0.2865                   |
+| 1.8067 | 365500 | 0.0398        | 0.2839                   |
+| 1.8092 | 366000 | 0.0404        | 0.2803                   |
+| 1.8116 | 366500 | 0.0378        | 0.2831                   |
+| 1.8141 | 367000 | 0.0398        | 0.2830                   |
+| 1.8166 | 367500 | 0.0389        | 0.2823                   |
+| 1.8191 | 368000 | 0.0401        | 0.2793                   |
+| 1.8215 | 368500 | 0.0402        | 0.2801                   |
+| 1.8240 | 369000 | 0.0364        | 0.2793                   |
+| 1.8265 | 369500 | 0.0414        | 0.2789                   |
+| 1.8289 | 370000 | 0.0386        | 0.2777                   |
+| 1.8314 | 370500 | 0.041         | 0.2812                   |
+| 1.8339 | 371000 | 0.0406        | 0.2806                   |
+| 1.8364 | 371500 | 0.0411        | 0.2798                   |
+| 1.8388 | 372000 | 0.0405        | 0.2800                   |
+| 1.8413 | 372500 | 0.0393        | 0.2814                   |
+| 1.8438 | 373000 | 0.0398        | 0.2823                   |
+| 1.8462 | 373500 | 0.0411        | 0.2817                   |
+| 1.8487 | 374000 | 0.0398        | 0.2843                   |
+| 1.8512 | 374500 | 0.041         | 0.2840                   |
+| 1.8537 | 375000 | 0.0375        | 0.2832                   |
+| 1.8561 | 375500 | 0.0397        | 0.2826                   |
+| 1.8586 | 376000 | 0.0417        | 0.2826                   |
+| 1.8611 | 376500 | 0.0396        | 0.2812                   |
+| 1.8636 | 377000 | 0.0386        | 0.2827                   |
+| 1.8660 | 377500 | 0.0403        | 0.2831                   |
+| 1.8685 | 378000 | 0.04          | 0.2831                   |
+| 1.8710 | 378500 | 0.0403        | 0.2800                   |
+| 1.8734 | 379000 | 0.0404        | 0.2817                   |
+| 1.8759 | 379500 | 0.0401        | 0.2825                   |
+| 1.8784 | 380000 | 0.0407        | 0.2818                   |
+| 1.8809 | 380500 | 0.0391        | 0.2807                   |
+| 1.8833 | 381000 | 0.0412        | 0.2813                   |
+| 1.8858 | 381500 | 0.0396        | 0.2824                   |
+| 1.8883 | 382000 | 0.0389        | 0.2801                   |
+| 1.8907 | 382500 | 0.0402        | 0.2801                   |
+| 1.8932 | 383000 | 0.041         | 0.2832                   |
+| 1.8957 | 383500 | 0.0418        | 0.2830                   |
+| 1.8982 | 384000 | 0.0389        | 0.2818                   |
+| 1.9006 | 384500 | 0.0377        | 0.2799                   |
+| 1.9031 | 385000 | 0.0377        | 0.2798                   |
+| 1.9056 | 385500 | 0.0359        | 0.2778                   |
+| 1.9080 | 386000 | 0.0411        | 0.2803                   |
+| 1.9105 | 386500 | 0.041         | 0.2785                   |
+| 1.9130 | 387000 | 0.04          | 0.2754                   |
+| 1.9155 | 387500 | 0.0396        | 0.2765                   |
+| 1.9179 | 388000 | 0.0406        | 0.2781                   |
+| 1.9204 | 388500 | 0.0411        | 0.2779                   |
+| 1.9229 | 389000 | 0.0424        | 0.2770                   |
+| 1.9253 | 389500 | 0.0393        | 0.2787                   |
+| 1.9278 | 390000 | 0.0407        | 0.2789                   |
+| 1.9303 | 390500 | 0.0386        | 0.2797                   |
+| 1.9328 | 391000 | 0.0399        | 0.2795                   |
+| 1.9352 | 391500 | 0.0398        | 0.2785                   |
+| 1.9377 | 392000 | 0.0411        | 0.2791                   |
+| 1.9402 | 392500 | 0.0397        | 0.2812                   |
+| 1.9426 | 393000 | 0.0393        | 0.2798                   |
+| 1.9451 | 393500 | 0.0407        | 0.2831                   |
+| 1.9476 | 394000 | 0.0394        | 0.2806                   |
+| 1.9501 | 394500 | 0.0414        | 0.2824                   |
+| 1.9525 | 395000 | 0.0391        | 0.2825                   |
+| 1.9550 | 395500 | 0.0396        | 0.2837                   |
+| 1.9575 | 396000 | 0.0396        | 0.2845                   |
+| 1.9599 | 396500 | 0.0418        | 0.2860                   |
+| 1.9624 | 397000 | 0.0389        | 0.2856                   |
+| 1.9649 | 397500 | 0.0402        | 0.2847                   |
+| 1.9674 | 398000 | 0.0396        | 0.2839                   |
+| 1.9698 | 398500 | 0.0399        | 0.2830                   |
+| 1.9723 | 399000 | 0.0406        | 0.2829                   |
+| 1.9748 | 399500 | 0.0414        | 0.2836                   |
+| 1.9772 | 400000 | 0.0397        | 0.2826                   |
+
+</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-400000/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-400000/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-400000/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-400000/sentence_bert_config.json

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

checkpoints/checkpoint-400000/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-401000/1_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-401000/README.md

@@ -0,0 +1,1026 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:1618413
+- loss:CosineSimilarityLoss
+base_model: BSC-LT/MrBERT-es
+widget:
+- source_sentence: El término empezó como una noción informal y no rigurosa originalmente
+    pensada como una "cantidad infinitamente pequeña", y originalmente fundamentó
+    ciertos razonamientos del cálculo infinitesimal.
+  sentences:
+  - Esto obligó a Knuth a dedicar un tiempo considerable en el estudio de la tipografía.
+  - Véase también Ingreso por domiciliación Transferencia bancaria Zona Única de Pagos
+    en Euros Referencias Banca
+  - ARTÍCULO 20o.
+- source_sentence: En su deambular, Leto encontró la recién creada isla flotante de
+    Delos, que no era el continente ni una isla real, y dio a luz allí.
+  sentences:
+  - El andador para adultos tiene frenos, es regulable en altura y el respaldo y el
+    asiento vienen recubiertos de esponja.
+  - Agricultura biodinámica La agricultura biológico-dinámica, creada en 1924 por
+    Rudolf Steiner y denominada agricultura biodinámica se basa en los fundamentos
+    y propuestas de estudio vinculados a la vertiente filosófica antroposofía, cuyo
+    autor es el mismo Steiner.
+  - '"El incremento de plazas se considera inmediato", advierten los jueces.'
+- source_sentence: Piensa en Mí... pensaré en ti.
+  sentences:
+  - Para quienes participan en un proceso educativo ambiental , los hallazgos y logros
+    son el inicio de nuevas preguntas y posibilidades.
+  - El transporte externo sobre pájaros o mamíferos es facilitado por modificaciones
+    del papus como las cerdas con barbas retrorsas, crecimientos del fruto como ganchos
+    o espinas, o brácteas involucrales especializadas.
+  - C. provocó, más que en cualquier otra parte, una simbiosis entre Grecia, Irán
+    y la India.
+- source_sentence: Fuimos un grupo mixto de amigos a pasar un finde en mojacar sin
+    más intención que pasarlo bien.
+  sentences:
+  - Suena el timbre para volver a clases, todos comentan que ha sido un gran partido
+    de fútbol, el resultado final ha sido de dos a cero, mañana continuará el campeonato
+    comentan los organizadores, aun continúo extasiado por aquel encuentro, la inspectora
+    me regaña y me ordena subir a la sala, ¡Qué aburrido!, ahora viene clase de historia,
+    nos toca ver las guerras mundiales, me pregunto ¿Para qué?, con tantas batallas
+    uno se pone bélico y ve guerras donde no las hay.
+  - El museo fue creado en el año 1922 por el Dr.
+  - Hasta su muerte vivió en las inmediaciones de la casa donde nació, en Maguncia.
+- source_sentence: Muchos jugadores de poker cuenta de que cuando juegan Hold'em en
+    línea que están recibiendo mucho más que simplemente un par de horas de entretenimiento.
+    sitios web de póquer en ofrecer a los jugadores una gran variedad de métodos para
+    disfrutar de sus juegos a favor, con la posibilidad de ganar dinero en serio.
+  sentences:
+  - Las primeras máquinas programables se desarrollaron en el mundo musulmán.
+  - Las leyendas urbanas suelen dejarnos una moraleja o enseñanza y casi siempre quienes
+    las narran las modifican ligeramente o versionan para adaptarlas a la cultura
+    o idiosincrasia del lugar en el que se cuenten o difundan, de allí que existan
+    un sinfín de versiones de un mismo relato, dependiendo de la región o país del
+    que se trate.
+  - Kepler definió la inercia sólo en términos de resistencia al movimiento, basándose
+    una vez más en la presunción de que el reposo era un estado natural que no necesitaba
+    explicación.
+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: 0.5045538034567716
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.2824634969738804
+      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 = [
+    "Muchos jugadores de poker cuenta de que cuando juegan Hold'em en línea que están recibiendo mucho más que simplemente un par de horas de entretenimiento. sitios web de póquer en ofrecer a los jugadores una gran variedad de métodos para disfrutar de sus juegos a favor, con la posibilidad de ganar dinero en serio.",
+    'Las leyendas urbanas suelen dejarnos una moraleja o enseñanza y casi siempre quienes las narran las modifican ligeramente o versionan para adaptarlas a la cultura o idiosincrasia del lugar en el que se cuenten o difundan, de allí que existan un sinfín de versiones de un mismo relato, dependiendo de la región o país del que se trate.',
+    'Kepler definió la inercia sólo en términos de resistencia al movimiento, basándose una vez más en la presunción de que el reposo era un estado natural que no necesitaba explicación.',
+]
+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, 0.4628, 0.1655],
+#         [0.4628, 1.0000, 0.1358],
+#         [0.1655, 0.1358, 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      | 0.5046     |
+| **spearman_cosine** | **0.2825** |
+
+<!--
+## 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: 1,618,413 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: 38.33 tokens</li><li>max: 435 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 36.8 tokens</li><li>max: 261 tokens</li></ul> | <ul><li>min: -0.8</li><li>mean: 0.16</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                        | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                       | label                            |
+  |:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>Estadísticas Estadísticas El almacenamiento o acceso técnico que es utilizado exclusivamente con fines estadísticos.</code> | <code>Connect within Simplemente instale la aplicación, seleccione su servidor y conéctese para conectarse en cuestión de segundos.</code>                                                                                                                                                                                                                                                                                                                       | <code>0.10739796608686447</code> |
+  | <code>Saludamos con la cabeza y sonreímos.</code>                                                                                 | <code>Simbolismo: corriente de corte fantástico y onírico, surgió como reacción al naturalismo de la corriente realista e impresionista, poniendo especial énfasis en el mundo de los sueños, así como en aspectos satánicos y terroríficos, el sexo y la perversión.</code>                                                                                                                                                                                     | <code>0.05001075938344002</code> |
+  | <code>La lista de nominados se anunció hace escasos días.</code>                                                                  | <code>Es muy probable que el topónimo Egipto derive de la transcripción fonética de uno de los nombres o epítetos de Menfis, capital del antiguo Kemet bajo la Dinastía III, a saber: Hout Ka-Ptah , que quiere decir "Casa del ka de Ptah", en alusión al principal templo consagrado a este dios, que pasó al griego como Aígyptos, que, con el tiempo, designó primero al barrio en el que se encontraba, luego a toda la ciudad y más tarde al reino.</code> | <code>0.14469777047634125</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
+- `num_train_epochs`: 4
+- `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`: 1.0
+- `num_train_epochs`: 4
+- `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 |
+|:------:|:------:|:-------------:|:------------------------:|
+| 0.4671 | 94500  | 0.043         | 0.2705                   |
+| 0.4696 | 95000  | 0.0425        | 0.2708                   |
+| 0.4721 | 95500  | 0.0428        | 0.2703                   |
+| 0.4745 | 96000  | 0.0411        | 0.2689                   |
+| 0.4770 | 96500  | 0.0434        | 0.2760                   |
+| 0.4795 | 97000  | 0.0407        | 0.2733                   |
+| 0.4820 | 97500  | 0.0445        | 0.2740                   |
+| 0.4844 | 98000  | 0.0447        | 0.2694                   |
+| 0.4869 | 98500  | 0.044         | 0.2755                   |
+| 0.4894 | 99000  | 0.0425        | 0.2705                   |
+| 0.4918 | 99500  | 0.0444        | 0.2729                   |
+| 0.4943 | 100000 | 0.0448        | 0.2690                   |
+| 0.4968 | 100500 | 0.0432        | 0.2747                   |
+| 0.4993 | 101000 | 0.0426        | 0.2739                   |
+| 0.5017 | 101500 | 0.043         | 0.2773                   |
+| 0.5042 | 102000 | 0.0432        | 0.2774                   |
+| 0.5067 | 102500 | 0.042         | 0.2776                   |
+| 0.5091 | 103000 | 0.0441        | 0.2755                   |
+| 0.5116 | 103500 | 0.0441        | 0.2782                   |
+| 0.5141 | 104000 | 0.0436        | 0.2784                   |
+| 0.5166 | 104500 | 0.0446        | 0.2763                   |
+| 0.5190 | 105000 | 0.0435        | 0.2779                   |
+| 0.5215 | 105500 | 0.0434        | 0.2763                   |
+| 0.5240 | 106000 | 0.0426        | 0.2744                   |
+| 0.5264 | 106500 | 0.0442        | 0.2748                   |
+| 0.5289 | 107000 | 0.0468        | 0.2743                   |
+| 0.5314 | 107500 | 0.0428        | 0.2684                   |
+| 0.5339 | 108000 | 0.042         | 0.2719                   |
+| 0.5363 | 108500 | 0.0435        | 0.2761                   |
+| 0.5388 | 109000 | 0.0438        | 0.2765                   |
+| 0.5413 | 109500 | 0.0442        | 0.2731                   |
+| 0.5437 | 110000 | 0.0443        | 0.2719                   |
+| 0.5462 | 110500 | 0.0411        | 0.2721                   |
+| 0.5487 | 111000 | 0.0431        | 0.2752                   |
+| 0.5512 | 111500 | 0.0435        | 0.2743                   |
+| 0.5536 | 112000 | 0.0444        | 0.2701                   |
+| 0.5561 | 112500 | 0.0423        | 0.2720                   |
+| 0.5586 | 113000 | 0.0446        | 0.2728                   |
+| 0.5610 | 113500 | 0.042         | 0.2734                   |
+| 0.5635 | 114000 | 0.0438        | 0.2763                   |
+| 0.5660 | 114500 | 0.0412        | 0.2774                   |
+| 0.5685 | 115000 | 0.0417        | 0.2787                   |
+| 0.5709 | 115500 | 0.0448        | 0.2775                   |
+| 0.5734 | 116000 | 0.0446        | 0.2755                   |
+| 0.5759 | 116500 | 0.0419        | 0.2768                   |
+| 0.5783 | 117000 | 0.0435        | 0.2753                   |
+| 0.5808 | 117500 | 0.0451        | 0.2747                   |
+| 0.5833 | 118000 | 0.0439        | 0.2768                   |
+| 0.5858 | 118500 | 0.0438        | 0.2773                   |
+| 0.5882 | 119000 | 0.043         | 0.2770                   |
+| 0.5907 | 119500 | 0.0458        | 0.2736                   |
+| 0.5932 | 120000 | 0.0419        | 0.2746                   |
+| 0.5956 | 120500 | 0.0441        | 0.2730                   |
+| 0.5981 | 121000 | 0.0425        | 0.2752                   |
+| 0.6006 | 121500 | 0.0414        | 0.2733                   |
+| 0.6031 | 122000 | 0.0407        | 0.2732                   |
+| 0.6055 | 122500 | 0.0445        | 0.2732                   |
+| 0.6080 | 123000 | 0.0434        | 0.2713                   |
+| 0.6105 | 123500 | 0.0439        | 0.2720                   |
+| 0.6129 | 124000 | 0.0438        | 0.2761                   |
+| 0.6154 | 124500 | 0.0418        | 0.2754                   |
+| 0.6179 | 125000 | 0.0423        | 0.2763                   |
+| 0.6204 | 125500 | 0.0426        | 0.2773                   |
+| 0.6228 | 126000 | 0.0448        | 0.2732                   |
+| 0.6253 | 126500 | 0.0424        | 0.2691                   |
+| 0.6278 | 127000 | 0.0451        | 0.2760                   |
+| 0.6302 | 127500 | 0.0437        | 0.2713                   |
+| 0.6327 | 128000 | 0.0429        | 0.2690                   |
+| 0.6352 | 128500 | 0.0439        | 0.2691                   |
+| 0.6377 | 129000 | 0.0442        | 0.2769                   |
+| 0.6401 | 129500 | 0.042         | 0.2750                   |
+| 0.6426 | 130000 | 0.0462        | 0.2731                   |
+| 0.6451 | 130500 | 0.043         | 0.2750                   |
+| 0.6475 | 131000 | 0.0445        | 0.2770                   |
+| 0.6500 | 131500 | 0.0424        | 0.2753                   |
+| 0.6525 | 132000 | 0.0451        | 0.2734                   |
+| 0.6550 | 132500 | 0.0453        | 0.2768                   |
+| 0.6574 | 133000 | 0.0453        | 0.2820                   |
+| 0.6599 | 133500 | 0.0424        | 0.2821                   |
+| 0.6624 | 134000 | 0.0446        | 0.2806                   |
+| 0.6648 | 134500 | 0.0433        | 0.2781                   |
+| 0.6673 | 135000 | 0.0443        | 0.2792                   |
+| 0.6698 | 135500 | 0.0447        | 0.2770                   |
+| 0.6723 | 136000 | 0.0396        | 0.2718                   |
+| 0.6747 | 136500 | 0.0418        | 0.2736                   |
+| 0.6772 | 137000 | 0.0428        | 0.2774                   |
+| 0.6797 | 137500 | 0.0444        | 0.2762                   |
+| 0.6821 | 138000 | 0.0409        | 0.2725                   |
+| 0.6846 | 138500 | 0.0429        | 0.2731                   |
+| 0.6871 | 139000 | 0.0447        | 0.2769                   |
+| 0.6896 | 139500 | 0.0454        | 0.2744                   |
+| 0.6920 | 140000 | 0.0443        | 0.2822                   |
+| 0.6945 | 140500 | 0.045         | 0.2753                   |
+| 0.6970 | 141000 | 0.043         | 0.2780                   |
+| 0.6994 | 141500 | 0.042         | 0.2765                   |
+| 0.7019 | 142000 | 0.0427        | 0.2762                   |
+| 0.7044 | 142500 | 0.0404        | 0.2809                   |
+| 0.7069 | 143000 | 0.045         | 0.2784                   |
+| 0.7093 | 143500 | 0.046         | 0.2781                   |
+| 0.7118 | 144000 | 0.0449        | 0.2733                   |
+| 0.7143 | 144500 | 0.0414        | 0.2736                   |
+| 0.7168 | 145000 | 0.0472        | 0.2751                   |
+| 0.7192 | 145500 | 0.0429        | 0.2782                   |
+| 0.7217 | 146000 | 0.0429        | 0.2781                   |
+| 0.7242 | 146500 | 0.0446        | 0.2750                   |
+| 0.7266 | 147000 | 0.0429        | 0.2773                   |
+| 0.7291 | 147500 | 0.0484        | 0.2808                   |
+| 0.7316 | 148000 | 0.0439        | 0.2759                   |
+| 0.7341 | 148500 | 0.0429        | 0.2764                   |
+| 0.7365 | 149000 | 0.0453        | 0.2785                   |
+| 0.7390 | 149500 | 0.043         | 0.2756                   |
+| 0.7415 | 150000 | 0.0438        | 0.2765                   |
+| 0.7439 | 150500 | 0.0446        | 0.2731                   |
+| 0.7464 | 151000 | 0.0443        | 0.2759                   |
+| 0.7489 | 151500 | 0.0438        | 0.2725                   |
+| 0.7514 | 152000 | 0.0463        | 0.2756                   |
+| 0.7538 | 152500 | 0.046         | 0.2774                   |
+| 0.7563 | 153000 | 0.0423        | 0.2769                   |
+| 0.7588 | 153500 | 0.0453        | 0.2752                   |
+| 0.7612 | 154000 | 0.046         | 0.2726                   |
+| 0.7637 | 154500 | 0.0432        | 0.2763                   |
+| 0.7662 | 155000 | 0.0462        | 0.2786                   |
+| 0.7687 | 155500 | 0.0455        | 0.2775                   |
+| 0.7711 | 156000 | 0.043         | 0.2783                   |
+| 0.7736 | 156500 | 0.0442        | 0.2784                   |
+| 0.7761 | 157000 | 0.0437        | 0.2769                   |
+| 0.7785 | 157500 | 0.044         | 0.2812                   |
+| 0.7810 | 158000 | 0.0443        | 0.2797                   |
+| 0.7835 | 158500 | 0.0436        | 0.2783                   |
+| 0.7860 | 159000 | 0.0435        | 0.2847                   |
+| 0.7884 | 159500 | 0.0438        | 0.2835                   |
+| 0.7909 | 160000 | 0.0446        | 0.2815                   |
+| 0.7934 | 160500 | 0.0434        | 0.2840                   |
+| 0.7958 | 161000 | 0.0455        | 0.2833                   |
+| 0.7983 | 161500 | 0.043         | 0.2845                   |
+| 0.8008 | 162000 | 0.0436        | 0.2845                   |
+| 0.8033 | 162500 | 0.0443        | 0.2823                   |
+| 0.8057 | 163000 | 0.0441        | 0.2812                   |
+| 0.8082 | 163500 | 0.0435        | 0.2777                   |
+| 0.8107 | 164000 | 0.0421        | 0.2740                   |
+| 0.8131 | 164500 | 0.0437        | 0.2738                   |
+| 0.8156 | 165000 | 0.0457        | 0.2745                   |
+| 0.8181 | 165500 | 0.0453        | 0.2815                   |
+| 0.8206 | 166000 | 0.0427        | 0.2788                   |
+| 0.8230 | 166500 | 0.045         | 0.2809                   |
+| 0.8255 | 167000 | 0.0439        | 0.2818                   |
+| 0.8280 | 167500 | 0.045         | 0.2795                   |
+| 0.8304 | 168000 | 0.0422        | 0.2802                   |
+| 0.8329 | 168500 | 0.0449        | 0.2783                   |
+| 0.8354 | 169000 | 0.0437        | 0.2765                   |
+| 0.8379 | 169500 | 0.0445        | 0.2788                   |
+| 0.8403 | 170000 | 0.0419        | 0.2832                   |
+| 0.8428 | 170500 | 0.0423        | 0.2775                   |
+| 0.8453 | 171000 | 0.0411        | 0.2804                   |
+| 0.8477 | 171500 | 0.0437        | 0.2755                   |
+| 0.8502 | 172000 | 0.044         | 0.2774                   |
+| 0.8527 | 172500 | 0.0447        | 0.2740                   |
+| 0.8552 | 173000 | 0.0444        | 0.2757                   |
+| 0.8576 | 173500 | 0.0419        | 0.2750                   |
+| 0.8601 | 174000 | 0.0461        | 0.2743                   |
+| 0.8626 | 174500 | 0.0455        | 0.2761                   |
+| 0.8650 | 175000 | 0.042         | 0.2745                   |
+| 0.8675 | 175500 | 0.0466        | 0.2757                   |
+| 0.8700 | 176000 | 0.0439        | 0.2744                   |
+| 0.8725 | 176500 | 0.0423        | 0.2771                   |
+| 0.8749 | 177000 | 0.0438        | 0.2723                   |
+| 0.8774 | 177500 | 0.0438        | 0.2771                   |
+| 0.8799 | 178000 | 0.0417        | 0.2777                   |
+| 0.8823 | 178500 | 0.044         | 0.2780                   |
+| 0.8848 | 179000 | 0.0426        | 0.2746                   |
+| 0.8873 | 179500 | 0.0446        | 0.2758                   |
+| 0.8898 | 180000 | 0.0451        | 0.2767                   |
+| 0.8922 | 180500 | 0.0432        | 0.2770                   |
+| 0.8947 | 181000 | 0.0425        | 0.2749                   |
+| 0.8972 | 181500 | 0.0447        | 0.2758                   |
+| 0.8996 | 182000 | 0.0422        | 0.2798                   |
+| 0.9021 | 182500 | 0.045         | 0.2789                   |
+| 0.9046 | 183000 | 0.044         | 0.2786                   |
+| 0.9071 | 183500 | 0.0436        | 0.2781                   |
+| 0.9095 | 184000 | 0.046         | 0.2777                   |
+| 0.9120 | 184500 | 0.0443        | 0.2773                   |
+| 0.9145 | 185000 | 0.0445        | 0.2753                   |
+| 0.9169 | 185500 | 0.043         | 0.2767                   |
+| 0.9194 | 186000 | 0.0454        | 0.2743                   |
+| 0.9219 | 186500 | 0.0433        | 0.2775                   |
+| 0.9244 | 187000 | 0.0443        | 0.2775                   |
+| 0.9268 | 187500 | 0.0432        | 0.2765                   |
+| 0.9293 | 188000 | 0.0434        | 0.2793                   |
+| 0.9318 | 188500 | 0.0463        | 0.2801                   |
+| 0.9342 | 189000 | 0.0439        | 0.2795                   |
+| 0.9367 | 189500 | 0.0423        | 0.2812                   |
+| 0.9392 | 190000 | 0.0441        | 0.2768                   |
+| 0.9417 | 190500 | 0.0446        | 0.2754                   |
+| 0.9441 | 191000 | 0.0436        | 0.2814                   |
+| 0.9466 | 191500 | 0.045         | 0.2795                   |
+| 0.9491 | 192000 | 0.0445        | 0.2794                   |
+| 0.9515 | 192500 | 0.0429        | 0.2827                   |
+| 0.9540 | 193000 | 0.043         | 0.2815                   |
+| 0.9565 | 193500 | 0.0446        | 0.2827                   |
+| 0.9590 | 194000 | 0.0456        | 0.2822                   |
+| 0.9614 | 194500 | 0.0406        | 0.2828                   |
+| 0.9639 | 195000 | 0.0444        | 0.2844                   |
+| 0.9664 | 195500 | 0.0448        | 0.2785                   |
+| 0.9688 | 196000 | 0.0427        | 0.2784                   |
+| 0.9713 | 196500 | 0.0453        | 0.2788                   |
+| 0.9738 | 197000 | 0.0443        | 0.2751                   |
+| 0.9763 | 197500 | 0.0444        | 0.2754                   |
+| 0.9787 | 198000 | 0.0448        | 0.2745                   |
+| 0.9812 | 198500 | 0.0445        | 0.2752                   |
+| 0.9837 | 199000 | 0.046         | 0.2710                   |
+| 0.9861 | 199500 | 0.0459        | 0.2732                   |
+| 0.9886 | 200000 | 0.0394        | 0.2729                   |
+| 0.9911 | 200500 | 0.045         | 0.2737                   |
+| 0.9936 | 201000 | 0.0434        | 0.2753                   |
+| 0.9960 | 201500 | 0.0465        | 0.2771                   |
+| 0.9985 | 202000 | 0.0443        | 0.2755                   |
+| 1.0    | 202302 | -             | 0.2735                   |
+| 1.0010 | 202500 | 0.0406        | 0.2746                   |
+| 1.0035 | 203000 | 0.0358        | 0.2751                   |
+| 1.0059 | 203500 | 0.039         | 0.2739                   |
+| 1.0084 | 204000 | 0.0389        | 0.2740                   |
+| 1.0109 | 204500 | 0.0382        | 0.2736                   |
+| 1.0133 | 205000 | 0.0374        | 0.2714                   |
+| 1.0158 | 205500 | 0.0393        | 0.2745                   |
+| 1.0183 | 206000 | 0.0388        | 0.2759                   |
+| 1.0208 | 206500 | 0.0398        | 0.2765                   |
+| 1.0232 | 207000 | 0.0399        | 0.2772                   |
+| 1.0257 | 207500 | 0.0403        | 0.2757                   |
+| 1.0282 | 208000 | 0.0383        | 0.2786                   |
+| 1.0306 | 208500 | 0.0376        | 0.2771                   |
+| 1.0331 | 209000 | 0.0418        | 0.2761                   |
+| 1.0356 | 209500 | 0.0381        | 0.2768                   |
+| 1.0381 | 210000 | 0.038         | 0.2761                   |
+| 1.0405 | 210500 | 0.0386        | 0.2735                   |
+| 1.0430 | 211000 | 0.0378        | 0.2768                   |
+| 1.0455 | 211500 | 0.0389        | 0.2764                   |
+| 1.0479 | 212000 | 0.0378        | 0.2757                   |
+| 1.0504 | 212500 | 0.039         | 0.2743                   |
+| 1.0529 | 213000 | 0.0367        | 0.2749                   |
+| 1.0554 | 213500 | 0.0394        | 0.2747                   |
+| 1.0578 | 214000 | 0.0372        | 0.2740                   |
+| 1.0603 | 214500 | 0.039         | 0.2757                   |
+| 1.0628 | 215000 | 0.0396        | 0.2813                   |
+| 1.0652 | 215500 | 0.0403        | 0.2794                   |
+| 1.0677 | 216000 | 0.0387        | 0.2771                   |
+| 1.0702 | 216500 | 0.0381        | 0.2733                   |
+| 1.0727 | 217000 | 0.0406        | 0.2717                   |
+| 1.0751 | 217500 | 0.0408        | 0.2749                   |
+| 1.0776 | 218000 | 0.0401        | 0.2750                   |
+| 1.0801 | 218500 | 0.0363        | 0.2724                   |
+| 1.0825 | 219000 | 0.0392        | 0.2745                   |
+| 1.0850 | 219500 | 0.0386        | 0.2726                   |
+| 1.0875 | 220000 | 0.0413        | 0.2741                   |
+| 1.0900 | 220500 | 0.04          | 0.2753                   |
+| 1.0924 | 221000 | 0.0371        | 0.2772                   |
+| 1.0949 | 221500 | 0.0392        | 0.2734                   |
+| 1.0974 | 222000 | 0.0397        | 0.2764                   |
+| 1.0998 | 222500 | 0.0406        | 0.2732                   |
+| 1.1023 | 223000 | 0.0396        | 0.2730                   |
+| 1.1048 | 223500 | 0.0396        | 0.2756                   |
+| 1.1073 | 224000 | 0.0389        | 0.2771                   |
+| 1.1097 | 224500 | 0.0402        | 0.2766                   |
+| 1.1122 | 225000 | 0.0386        | 0.2774                   |
+| 1.1147 | 225500 | 0.0389        | 0.2782                   |
+| 1.1171 | 226000 | 0.0372        | 0.2768                   |
+| 1.1196 | 226500 | 0.0384        | 0.2726                   |
+| 1.1221 | 227000 | 0.0424        | 0.2734                   |
+| 1.1246 | 227500 | 0.041         | 0.2732                   |
+| 1.1270 | 228000 | 0.0392        | 0.2717                   |
+| 1.1295 | 228500 | 0.039         | 0.2743                   |
+| 1.1320 | 229000 | 0.0402        | 0.2721                   |
+| 1.1344 | 229500 | 0.0403        | 0.2733                   |
+| 1.1369 | 230000 | 0.0393        | 0.2727                   |
+| 1.1394 | 230500 | 0.039         | 0.2755                   |
+| 1.1419 | 231000 | 0.0382        | 0.2757                   |
+| 1.1443 | 231500 | 0.036         | 0.2760                   |
+| 1.1468 | 232000 | 0.0408        | 0.2762                   |
+| 1.1493 | 232500 | 0.0393        | 0.2733                   |
+| 1.1517 | 233000 | 0.0385        | 0.2750                   |
+| 1.1542 | 233500 | 0.0398        | 0.2772                   |
+| 1.1567 | 234000 | 0.0411        | 0.2751                   |
+| 1.1592 | 234500 | 0.0404        | 0.2747                   |
+| 1.1616 | 235000 | 0.0393        | 0.2765                   |
+| 1.1641 | 235500 | 0.0389        | 0.2715                   |
+| 1.1666 | 236000 | 0.0379        | 0.2759                   |
+| 1.1690 | 236500 | 0.0392        | 0.2740                   |
+| 1.1715 | 237000 | 0.039         | 0.2732                   |
+| 1.1740 | 237500 | 0.041         | 0.2703                   |
+| 1.1765 | 238000 | 0.0403        | 0.2748                   |
+| 1.1789 | 238500 | 0.0388        | 0.2753                   |
+| 1.1814 | 239000 | 0.0405        | 0.2744                   |
+| 1.1839 | 239500 | 0.039         | 0.2769                   |
+| 1.1863 | 240000 | 0.0405        | 0.2746                   |
+| 1.1888 | 240500 | 0.0389        | 0.2738                   |
+| 1.1913 | 241000 | 0.0393        | 0.2781                   |
+| 1.1938 | 241500 | 0.0374        | 0.2794                   |
+| 1.1962 | 242000 | 0.0404        | 0.2747                   |
+| 1.1987 | 242500 | 0.0388        | 0.2763                   |
+| 1.2012 | 243000 | 0.0387        | 0.2775                   |
+| 1.2036 | 243500 | 0.0401        | 0.2723                   |
+| 1.2061 | 244000 | 0.0394        | 0.2695                   |
+| 1.2086 | 244500 | 0.0405        | 0.2735                   |
+| 1.2111 | 245000 | 0.0408        | 0.2754                   |
+| 1.2135 | 245500 | 0.0388        | 0.2708                   |
+| 1.2160 | 246000 | 0.0383        | 0.2738                   |
+| 1.2185 | 246500 | 0.0416        | 0.2736                   |
+| 1.2209 | 247000 | 0.0379        | 0.2763                   |
+| 1.2234 | 247500 | 0.0415        | 0.2756                   |
+| 1.2259 | 248000 | 0.0378        | 0.2754                   |
+| 1.2284 | 248500 | 0.0392        | 0.2772                   |
+| 1.2308 | 249000 | 0.0391        | 0.2757                   |
+| 1.2333 | 249500 | 0.0386        | 0.2717                   |
+| 1.2358 | 250000 | 0.0416        | 0.2769                   |
+| 1.2382 | 250500 | 0.0404        | 0.2734                   |
+| 1.2407 | 251000 | 0.0379        | 0.2749                   |
+| 1.2432 | 251500 | 0.0387        | 0.2743                   |
+| 1.2457 | 252000 | 0.0421        | 0.2751                   |
+| 1.2481 | 252500 | 0.0391        | 0.2753                   |
+| 1.2506 | 253000 | 0.039         | 0.2755                   |
+| 1.2531 | 253500 | 0.042         | 0.2725                   |
+| 1.2555 | 254000 | 0.0394        | 0.2731                   |
+| 1.2580 | 254500 | 0.0398        | 0.2758                   |
+| 1.2605 | 255000 | 0.0404        | 0.2786                   |
+| 1.2630 | 255500 | 0.0398        | 0.2783                   |
+| 1.2654 | 256000 | 0.0392        | 0.2779                   |
+| 1.2679 | 256500 | 0.0386        | 0.2785                   |
+| 1.2704 | 257000 | 0.0402        | 0.2764                   |
+| 1.2728 | 257500 | 0.0376        | 0.2792                   |
+| 1.2753 | 258000 | 0.0387        | 0.2791                   |
+| 1.2778 | 258500 | 0.0397        | 0.2808                   |
+| 1.2803 | 259000 | 0.038         | 0.2802                   |
+| 1.2827 | 259500 | 0.0389        | 0.2795                   |
+| 1.2852 | 260000 | 0.0412        | 0.2771                   |
+| 1.2877 | 260500 | 0.0394        | 0.2777                   |
+| 1.2902 | 261000 | 0.0426        | 0.2792                   |
+| 1.2926 | 261500 | 0.0391        | 0.2772                   |
+| 1.2951 | 262000 | 0.0382        | 0.2783                   |
+| 1.2976 | 262500 | 0.0385        | 0.2789                   |
+| 1.3000 | 263000 | 0.0401        | 0.2812                   |
+| 1.3025 | 263500 | 0.0392        | 0.2826                   |
+| 1.3050 | 264000 | 0.0403        | 0.2813                   |
+| 1.3075 | 264500 | 0.0394        | 0.2779                   |
+| 1.3099 | 265000 | 0.0397        | 0.2832                   |
+| 1.3124 | 265500 | 0.0407        | 0.2785                   |
+| 1.3149 | 266000 | 0.0412        | 0.2809                   |
+| 1.3173 | 266500 | 0.0399        | 0.2805                   |
+| 1.3198 | 267000 | 0.0406        | 0.2803                   |
+| 1.3223 | 267500 | 0.0397        | 0.2812                   |
+| 1.3248 | 268000 | 0.0413        | 0.2819                   |
+| 1.3272 | 268500 | 0.0398        | 0.2788                   |
+| 1.3297 | 269000 | 0.0402        | 0.2814                   |
+| 1.3322 | 269500 | 0.0387        | 0.2825                   |
+| 1.3346 | 270000 | 0.0425        | 0.2789                   |
+| 1.3371 | 270500 | 0.038         | 0.2793                   |
+| 1.3396 | 271000 | 0.0377        | 0.2775                   |
+| 1.3421 | 271500 | 0.0414        | 0.2769                   |
+| 1.3445 | 272000 | 0.0389        | 0.2735                   |
+| 1.3470 | 272500 | 0.0386        | 0.2785                   |
+| 1.3495 | 273000 | 0.0401        | 0.2813                   |
+| 1.3519 | 273500 | 0.0383        | 0.2801                   |
+| 1.3544 | 274000 | 0.0396        | 0.2796                   |
+| 1.3569 | 274500 | 0.0396        | 0.2793                   |
+| 1.3594 | 275000 | 0.0424        | 0.2814                   |
+| 1.3618 | 275500 | 0.0418        | 0.2814                   |
+| 1.3643 | 276000 | 0.0383        | 0.2787                   |
+| 1.3668 | 276500 | 0.04          | 0.2797                   |
+| 1.3692 | 277000 | 0.0414        | 0.2810                   |
+| 1.3717 | 277500 | 0.0379        | 0.2848                   |
+| 1.3742 | 278000 | 0.0381        | 0.2846                   |
+| 1.3767 | 278500 | 0.0383        | 0.2814                   |
+| 1.3791 | 279000 | 0.039         | 0.2818                   |
+| 1.3816 | 279500 | 0.0388        | 0.2792                   |
+| 1.3841 | 280000 | 0.0408        | 0.2784                   |
+| 1.3865 | 280500 | 0.0389        | 0.2814                   |
+| 1.3890 | 281000 | 0.0426        | 0.2794                   |
+| 1.3915 | 281500 | 0.0392        | 0.2780                   |
+| 1.3940 | 282000 | 0.0405        | 0.2778                   |
+| 1.3964 | 282500 | 0.0407        | 0.2769                   |
+| 1.3989 | 283000 | 0.0396        | 0.2730                   |
+| 1.4014 | 283500 | 0.0376        | 0.2770                   |
+| 1.4038 | 284000 | 0.0399        | 0.2791                   |
+| 1.4063 | 284500 | 0.0405        | 0.2791                   |
+| 1.4088 | 285000 | 0.0382        | 0.2804                   |
+| 1.4113 | 285500 | 0.0388        | 0.2835                   |
+| 1.4137 | 286000 | 0.0394        | 0.2784                   |
+| 1.4162 | 286500 | 0.0388        | 0.2813                   |
+| 1.4187 | 287000 | 0.0397        | 0.2813                   |
+| 1.4211 | 287500 | 0.0404        | 0.2808                   |
+| 1.4236 | 288000 | 0.0374        | 0.2792                   |
+| 1.4261 | 288500 | 0.041         | 0.2724                   |
+| 1.4286 | 289000 | 0.0409        | 0.2770                   |
+| 1.4310 | 289500 | 0.04          | 0.2789                   |
+| 1.4335 | 290000 | 0.0412        | 0.2754                   |
+| 1.4360 | 290500 | 0.0404        | 0.2780                   |
+| 1.4384 | 291000 | 0.0406        | 0.2794                   |
+| 1.4409 | 291500 | 0.0387        | 0.2776                   |
+| 1.4434 | 292000 | 0.037         | 0.2801                   |
+| 1.4459 | 292500 | 0.0394        | 0.2778                   |
+| 1.4483 | 293000 | 0.0406        | 0.2786                   |
+| 1.4508 | 293500 | 0.0401        | 0.2827                   |
+| 1.4533 | 294000 | 0.0388        | 0.2770                   |
+| 1.4557 | 294500 | 0.0377        | 0.2768                   |
+| 1.4582 | 295000 | 0.0386        | 0.2773                   |
+| 1.4607 | 295500 | 0.04          | 0.2783                   |
+| 1.4632 | 296000 | 0.0402        | 0.2780                   |
+| 1.4656 | 296500 | 0.0401        | 0.2820                   |
+| 1.4681 | 297000 | 0.0393        | 0.2790                   |
+| 1.4706 | 297500 | 0.0394        | 0.2787                   |
+| 1.4730 | 298000 | 0.0392        | 0.2759                   |
+| 1.4755 | 298500 | 0.0396        | 0.2767                   |
+| 1.4780 | 299000 | 0.0379        | 0.2752                   |
+| 1.4805 | 299500 | 0.039         | 0.2742                   |
+| 1.4829 | 300000 | 0.0383        | 0.2750                   |
+| 1.4854 | 300500 | 0.0398        | 0.2741                   |
+| 1.4879 | 301000 | 0.0394        | 0.2749                   |
+| 1.4903 | 301500 | 0.0416        | 0.2728                   |
+| 1.4928 | 302000 | 0.0388        | 0.2751                   |
+| 1.4953 | 302500 | 0.041         | 0.2759                   |
+| 1.4978 | 303000 | 0.0405        | 0.2744                   |
+| 1.5002 | 303500 | 0.0397        | 0.2734                   |
+| 1.5027 | 304000 | 0.0413        | 0.2762                   |
+| 1.5052 | 304500 | 0.0412        | 0.2754                   |
+| 1.5076 | 305000 | 0.0386        | 0.2787                   |
+| 1.5101 | 305500 | 0.0377        | 0.2790                   |
+| 1.5126 | 306000 | 0.0395        | 0.2784                   |
+| 1.5151 | 306500 | 0.0423        | 0.2797                   |
+| 1.5175 | 307000 | 0.0396        | 0.2819                   |
+| 1.5200 | 307500 | 0.0395        | 0.2810                   |
+| 1.5225 | 308000 | 0.04          | 0.2828                   |
+| 1.5249 | 308500 | 0.0373        | 0.2840                   |
+| 1.5274 | 309000 | 0.0385        | 0.2873                   |
+| 1.5299 | 309500 | 0.0401        | 0.2854                   |
+| 1.5324 | 310000 | 0.0404        | 0.2851                   |
+| 1.5348 | 310500 | 0.0404        | 0.2849                   |
+| 1.5373 | 311000 | 0.0407        | 0.2840                   |
+| 1.5398 | 311500 | 0.0389        | 0.2855                   |
+| 1.5422 | 312000 | 0.0403        | 0.2855                   |
+| 1.5447 | 312500 | 0.0395        | 0.2830                   |
+| 1.5472 | 313000 | 0.0419        | 0.2824                   |
+| 1.5497 | 313500 | 0.0389        | 0.2822                   |
+| 1.5521 | 314000 | 0.0382        | 0.2857                   |
+| 1.5546 | 314500 | 0.0383        | 0.2844                   |
+| 1.5571 | 315000 | 0.0415        | 0.2819                   |
+| 1.5595 | 315500 | 0.04          | 0.2820                   |
+| 1.5620 | 316000 | 0.0395        | 0.2849                   |
+| 1.5645 | 316500 | 0.0392        | 0.2841                   |
+| 1.5670 | 317000 | 0.0408        | 0.2834                   |
+| 1.5694 | 317500 | 0.0415        | 0.2816                   |
+| 1.5719 | 318000 | 0.0386        | 0.2832                   |
+| 1.5744 | 318500 | 0.039         | 0.2823                   |
+| 1.5769 | 319000 | 0.0419        | 0.2836                   |
+| 1.5793 | 319500 | 0.0389        | 0.2845                   |
+| 1.5818 | 320000 | 0.0391        | 0.2853                   |
+| 1.5843 | 320500 | 0.0381        | 0.2845                   |
+| 1.5867 | 321000 | 0.0365        | 0.2815                   |
+| 1.5892 | 321500 | 0.0416        | 0.2843                   |
+| 1.5917 | 322000 | 0.039         | 0.2849                   |
+| 1.5942 | 322500 | 0.0419        | 0.2833                   |
+| 1.5966 | 323000 | 0.0393        | 0.2834                   |
+| 1.5991 | 323500 | 0.039         | 0.2857                   |
+| 1.6016 | 324000 | 0.0394        | 0.2835                   |
+| 1.6040 | 324500 | 0.0395        | 0.2820                   |
+| 1.6065 | 325000 | 0.0413        | 0.2827                   |
+| 1.6090 | 325500 | 0.0411        | 0.2839                   |
+| 1.6115 | 326000 | 0.0387        | 0.2844                   |
+| 1.6139 | 326500 | 0.0399        | 0.2873                   |
+| 1.6164 | 327000 | 0.0401        | 0.2871                   |
+| 1.6189 | 327500 | 0.0413        | 0.2840                   |
+| 1.6213 | 328000 | 0.0385        | 0.2846                   |
+| 1.6238 | 328500 | 0.0401        | 0.2855                   |
+| 1.6263 | 329000 | 0.0402        | 0.2836                   |
+| 1.6288 | 329500 | 0.0391        | 0.2845                   |
+| 1.6312 | 330000 | 0.0395        | 0.2850                   |
+| 1.6337 | 330500 | 0.0397        | 0.2847                   |
+| 1.6362 | 331000 | 0.0387        | 0.2890                   |
+| 1.6386 | 331500 | 0.0387        | 0.2838                   |
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+| 1.9278 | 390000 | 0.0407        | 0.2789                   |
+| 1.9303 | 390500 | 0.0386        | 0.2797                   |
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+| 1.9352 | 391500 | 0.0398        | 0.2785                   |
+| 1.9377 | 392000 | 0.0411        | 0.2791                   |
+| 1.9402 | 392500 | 0.0397        | 0.2812                   |
+| 1.9426 | 393000 | 0.0393        | 0.2798                   |
+| 1.9451 | 393500 | 0.0407        | 0.2831                   |
+| 1.9476 | 394000 | 0.0394        | 0.2806                   |
+| 1.9501 | 394500 | 0.0414        | 0.2824                   |
+| 1.9525 | 395000 | 0.0391        | 0.2825                   |
+| 1.9550 | 395500 | 0.0396        | 0.2837                   |
+| 1.9575 | 396000 | 0.0396        | 0.2845                   |
+| 1.9599 | 396500 | 0.0418        | 0.2860                   |
+| 1.9624 | 397000 | 0.0389        | 0.2856                   |
+| 1.9649 | 397500 | 0.0402        | 0.2847                   |
+| 1.9674 | 398000 | 0.0396        | 0.2839                   |
+| 1.9698 | 398500 | 0.0399        | 0.2830                   |
+| 1.9723 | 399000 | 0.0406        | 0.2829                   |
+| 1.9748 | 399500 | 0.0414        | 0.2836                   |
+| 1.9772 | 400000 | 0.0397        | 0.2826                   |
+| 1.9797 | 400500 | 0.0389        | 0.2833                   |
+| 1.9822 | 401000 | 0.0418        | 0.2825                   |
+
+</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-401000/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-401000/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-401000/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-401000/sentence_bert_config.json

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

checkpoints/checkpoint-401000/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/eval/similarity_evaluation_sts_eval_results.csv

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+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 0.0,
+  "train_batch_size": 8,
+  "trial_name": null,
+  "trial_params": null
+}

thermal_emergency/batch_1_temp_0p0c_model_20260305_111245_692375/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
+}

thermal_emergency/batch_1_temp_0p0c_model_20260305_111245_692375/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"
+}

thermal_emergency/batch_1_temp_0p0c_model_20260305_130932_917945/1_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
+}

thermal_emergency/batch_1_temp_0p0c_model_20260305_130932_917945/README.md

@@ -0,0 +1,143 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+base_model: BSC-LT/MrBERT-es
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# 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 = [
+    'The weather is lovely today.',
+    "It's so sunny outside!",
+    'He drove to the stadium.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### 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
+
+<!--
+## 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.*
+-->

thermal_emergency/batch_1_temp_0p0c_model_20260305_130932_917945/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"
+  }
+]

thermal_emergency/batch_1_temp_0p0c_model_20260305_130932_917945/sentence_bert_config.json

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

thermal_emergency/batch_1_temp_0p0c_model_20260305_130932_917945/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
+  }
+}