| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:1175405 |
| - loss:CosineSimilarityLoss |
| base_model: BSC-LT/MrBERT-es |
| widget: |
| - source_sentence: El camino de Santiago articula la península ibérica con Europa. |
| sentences: |
| - Y un millon de euros y de pesetas tampoco son lo mismo. |
| - Asimismo, en los montes puede haber matorral de coscoja y, también, lentisco, |
| romero, enebro o brezo. |
| - El país fue el noveno mayor importador de petróleo del mundo en 2013 . |
| - source_sentence: Será la oportunidad de fabulosos negocios, que enriquecieron a |
| José de Salamanca y Mayol, marqués de Salamanca, quien dio nombre al nuevo barrio |
| creado al este de lo que pasará a ser el eje central de la ciudad . |
| sentences: |
| - Para terminar, como suelen hacer, el 'Free from desire', de Gala. |
| - Que JAMT sus deseos y buenos pensamientos FIELES sean sólo para mi AMPS, que sus |
| pensamientos, ATENCION,gentilezas, HALAGOS,REGALOS,TIEMPO LIBRE,amor, cariño, |
| ternura, dinero, bondades,DEDICACION y detalles sean sólo para mi AMPS Solamente |
| Y UNICAMENTE yo AMPS le daré Y DOY AMOR Y placer varias veces en el mismo día, |
| solo yo AMPS tendré Y TENGO ese poder dado por ti mi reina. |
| - Esperamos con anhelo poder saludarte personalmente en breve. 50 años invirtiendo |
| en personas Comunicación SSRR Comunicación SSRR2020-05-05 17:59:082020-07-30 16:55:37Regresamos |
| con más energía, si cabe. |
| - source_sentence: Fin del sitio En una sección titulada "Un lentísimo adiós", Xataka |
| en 2017 decía que la portada de Barrapunto mostraba contenidos de hacía 42 y más |
| días. |
| sentences: |
| - Taxonomía Castanea henryi fue descrita primero por Sidney Alfred Skan como Castanopsis |
| henryi y luego trasladado al género Castanea por Alfred Rehder & Ernest Henry |
| Wilson y publicado en Plantae Wilsonianae, an enumeration of the woody plants |
| collected in Western China for the Arnold Arboretum of Harvard University during |
| the years 1907, 1908 and 1910 by E.H. |
| - Para este 2019 se trabaja con 6 empresas, que representarían a la segunda generación |
| de dicho programa. |
| - Ya no está uno para estos trotes. |
| - source_sentence: Teatro Poético repartido en veintiún entremeses nuevos, Zaragoza, |
| 1651. |
| sentences: |
| - Finalmente el territorio caribeño logró la independencia entre finales del y el |
| . |
| - No es considerada fiable. |
| - La página se generó a las 19:58:53. |
| - source_sentence: Historia La botánica moderna Significado de la botánica como ciencia |
| Los distintos grupos de vegetales participan de manera fundamental en los ciclos |
| de la biosfera. |
| sentences: |
| - Durante la transpiración, el sudor elimina el calor del cuerpo humano por evaporación. |
| - El COPINH exige a las autoridades judiciales y fiscales proceder judicialmente |
| contra los alcaldes municipales, altos funcionarios de SERNA, y contra las empresas |
| y demás sectores involucrados en esta agresión contra el pueblo lenca. |
| - A nivel global, el artículo13 del Pacto Internacional de Derechos Económicos, |
| Sociales y Culturales de 1966 de las Naciones Unidas reconoce el derecho de toda |
| persona a la educación. |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| model-index: |
| - name: SentenceTransformer based on BSC-LT/MrBERT-es |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: STSES |
| type: stses |
| metrics: |
| - type: pearson_cosine |
| value: 0.752738 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.716634 |
| 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. |
|
|
| ## About This Project |
|
|
| This model was trained using the **[Transformer Encoder Frankenstein](https://github.com/your-username/transformer-encoder-frankestein)** framework - a config-driven training library and CLI for end-to-end NLP workflows. |
|
|
| The Frankenstein Transformer provides: |
| - **Schema-driven configuration**: Strict YAML schema validation for reproducible training |
| - **Thermal stability controls**: GPU temperature management for safe long-term training |
| - **Advanced optimizer support**: Multiple optimizer implementations (AdamW, AdaFactor, GaLore, Lion, Muon, Sophia, and more) |
| - **SBERT workflows**: Specialized sentence-embedding fine-tuning and inference tools |
| - **Deployment artifact generation**: Model quantization and deployment utilities |
| - **Inference modes**: Single text, batch, and benchmark inference capabilities |
|
|
| Visit the [Transformer Encoder Frankenstein repository](https://github.com/your-username/transformer-encoder-frankestein) for more information, documentation, and usage examples. |
|
|
| ## Evaluation Results (STSES Dataset) |
|
|
| This model achieves strong performance on the Spanish Semantic Textual Similarity Evaluation Set (STSES): |
|
|
| | Metric | Score | |
| |--------|-------| |
| | **Pearson Cosine Similarity** | 0.7527 | |
| | **Spearman Cosine Similarity** | 0.7166 | |
| | **Manhattan Pearson** | 0.7514 | |
| | **Manhattan Spearman** | 0.7162 | |
| | **Euclidean Pearson** | 0.7499 | |
| | **Euclidean Spearman** | 0.7166 | |
| | **Main Score (Spearman Cosine)** | **0.7166** | |
| | **Evaluation Time** | 1.15 seconds | |
| | **Languages** | Spanish (spa-Latn) | |
| | **MTEB Version** | 1.39.7 | |
|
|
| ## Training Configuration |
|
|
| This model was trained using the following Frankenstein Transformer YAML configuration: |
|
|
| ```yaml |
| base_model: BSC-LT/MrBERT-es |
| training: |
| task: sbert |
| switch_on_thermal: true |
| gpu_temp_guard_enabled: true |
| gpu_temp_resume_threshold_c: 75 |
| gpu_temp_pause_threshold_c: 85 |
| gpu_temp_critical_threshold_c: 88 |
| gpu_temp_poll_interval_seconds: 30 |
| telemetry_log_interval: 1 |
| sbert: |
| dataset_name: "erickfmm/agentlans__multilingual-sentences__paired_10_sts" |
| dataset_type: paired_similarity |
| columns: |
| sentence1: sentence1 |
| sentence2: sentence2 |
| similarity: similarity |
| output_dir: "./output/sbert_modernbert" |
| batch_size: 512 |
| gradient_accumulation_steps: 1 |
| max_grad_norm: 2.0 |
| epochs: 10 |
| warmup_steps: 250 |
| evaluation_steps: 5000 |
| checkpoint_save_steps: 1000 |
| resume_from_checkpoint: true |
| learning_rate: 1.6e-6 |
| max_train_samples: null |
| max_eval_samples: 20000 |
| max_seq_length: 8192 |
| pooling_mode: mean |
| use_amp: false |
| resample_balanced: false |
| resample_std: 0.3 |
| standardize_scores: true |
| ``` |
|
|
| ### Configuration Details |
|
|
| - **Base Model**: BSC-LT/MrBERT-es - Spanish BERT variant |
| - **Task**: Sentence-BERT (SBERT) fine-tuning for semantic similarity |
| - **Thermal Management**: Enabled with safeguards (pause at 85°C, resume at 75°C, critical at 88°C) |
| - **Dataset**: Multilingual sentence pairs with similarity scores |
| - **Batch Size**: 512 samples per batch |
| - **Training Duration**: 10 epochs |
| - **Sequence Length**: Up to 8,192 tokens (extended from standard 512) |
| - **Learning Rate**: 1.6e-6 (very low for stable fine-tuning) |
| - **Pooling**: Mean pooling over token embeddings |
| - **Output Dimensionality**: 768 dimensions |
|
|
| ## 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 |
| - **Dataset Size:** 1,175,405 sentence pairs |
| - **Loss Function:** Cosine Similarity Loss |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Spanish --> |
| <!-- - **License:** Apache 2.0 --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'ModernBertModel'}) |
| (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("sentence_transformers_model_id") |
| # Run inference |
| sentences = [ |
| 'Historia La botánica moderna Significado de la botánica como ciencia Los distintos grupos de vegetales participan de manera fundamental en los ciclos de la biosfera.', |
| 'El COPINH exige a las autoridades judiciales y fiscales proceder judicialmente contra los alcaldes municipales, altos funcionarios de SERNA, y contra las empresas y demás sectores involucrados en esta agresión contra el pueblo lenca.', |
| 'Durante la transpiración, el sudor elimina el calor del cuerpo humano por evaporación.', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 768] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.2126, 0.2099], |
| # [0.2126, 1.0000, 0.0278], |
| # [0.2099, 0.0278, 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 | 0.4611 | |
| | **spearman_cosine** | **0.2749** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### Unnamed Dataset |
| |
| * Size: 1,175,405 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence_0 | sentence_1 | label | |
| |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 37.17 tokens</li><li>max: 290 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 38.26 tokens</li><li>max: 375 tokens</li></ul> | <ul><li>min: -0.75</li><li>mean: 0.17</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| |
| | <code>Los ahorros de la jubilación podrán usarse para este fin.</code> | <code>Sony Ericsson W8 además de todo eso presenta una pantalla táctil de tipo HVGA de 320 x 480 píxeles y la pantalla posee 16.777.216 colores.</code> | <code>0.2533760964870453</code> | |
| | <code>Programas de desarrollo en el cerebelo La transición célula progenitora a neurona madura, implica una serie de cambios morfológicos y moleculares altamente regulada espacial y temporalmente.</code> | <code>Dos ejemplos en los que el principio de exclusión relaciona la materia con la ocupación del espacio son las estrellas enanas blancas y las estrellas de neutrones, que se analizan más adelante.</code> | <code>0.1902337223291397</code> | |
| | <code>Bolsa inmobiliaria online en Distrito Federal df, inmuebles en venta y renta, casas, departamentos, locales, terrenos, inmobiliarias, desarrollos, anunciar inmuebles.</code> | <code>Otros prefieren hablar de "régimen" o "sistema feudal", para diferenciarlo sutilmente del feudalismo estricto, o de síntesis feudal, para marcar el hecho de que sobreviven en ella rasgos de la antigüedad clásica mezclados con contribuciones germánicas, implicando tanto a instituciones como a elementos productivos, y significó la especificidad del feudalismo europeo occidental como formación económico social frente a otras también feudales, con consecuencias trascendentales en el futuro devenir histórico.</code> | <code>0.21721388399600983</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
| ```json |
| { |
| "loss_fct": "torch.nn.modules.loss.MSELoss" |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `eval_strategy`: steps |
| - `max_grad_norm`: 2.0 |
| - `num_train_epochs`: 10 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: steps |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 8 |
| - `per_device_eval_batch_size`: 8 |
| - `per_gpu_train_batch_size`: None |
| - `per_gpu_eval_batch_size`: None |
| - `gradient_accumulation_steps`: 1 |
| - `eval_accumulation_steps`: None |
| - `torch_empty_cache_steps`: None |
| - `learning_rate`: 5e-05 |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 2.0 |
| - `num_train_epochs`: 10 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_ratio`: 0.0 |
| - `warmup_steps`: 0 |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `save_safetensors`: True |
| - `save_on_each_node`: False |
| - `save_only_model`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `no_cuda`: False |
| - `use_cpu`: False |
| - `use_mps_device`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `jit_mode_eval`: False |
| - `bf16`: False |
| - `fp16`: False |
| - `fp16_opt_level`: O1 |
| - `half_precision_backend`: auto |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `local_rank`: 0 |
| - `ddp_backend`: None |
| - `tpu_num_cores`: None |
| - `tpu_metrics_debug`: False |
| - `debug`: [] |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_prefetch_factor`: None |
| - `past_index`: -1 |
| - `disable_tqdm`: False |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `fsdp`: [] |
| - `fsdp_min_num_params`: 0 |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `fsdp_transformer_layer_cls_to_wrap`: None |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch |
| - `optim_args`: None |
| - `adafactor`: False |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `skip_memory_metrics`: True |
| - `use_legacy_prediction_loop`: False |
| - `push_to_hub`: False |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_private_repo`: None |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_inputs_for_metrics`: False |
| - `include_for_metrics`: [] |
| - `eval_do_concat_batches`: True |
| - `fp16_backend`: auto |
| - `push_to_hub_model_id`: None |
| - `push_to_hub_organization`: None |
| - `mp_parameters`: |
| - `auto_find_batch_size`: False |
| - `full_determinism`: False |
| - `torchdynamo`: None |
| - `ray_scope`: last |
| - `ddp_timeout`: 1800 |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `include_tokens_per_second`: False |
| - `include_num_input_tokens_seen`: no |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: True |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| |
| </details> |
| |
| ### Training Logs |
| <details><summary>Click to expand</summary> |
| |
| | Epoch | Step | Training Loss | sts_eval_spearman_cosine | |
| |:------:|:------:|:-------------:|:------------------------:| |
| | 3.9714 | 583500 | 0.0253 | 0.2725 | |
| | 3.9748 | 584000 | 0.0274 | 0.2733 | |
| | 3.9782 | 584500 | 0.0279 | 0.2711 | |
| | 3.9816 | 585000 | 0.0248 | 0.2708 | |
| | 3.9850 | 585500 | 0.0264 | 0.2676 | |
| | 3.9884 | 586000 | 0.0267 | 0.2713 | |
| | 3.9918 | 586500 | 0.0276 | 0.2703 | |
| | 3.9952 | 587000 | 0.0273 | 0.2674 | |
| | 3.9986 | 587500 | 0.0278 | 0.2688 | |
| | 4.0 | 587704 | - | 0.2672 | |
| | 4.0020 | 588000 | 0.0259 | 0.2675 | |
| | 4.0054 | 588500 | 0.0257 | 0.2697 | |
| | 4.0088 | 589000 | 0.0268 | 0.2694 | |
| | 4.0122 | 589500 | 0.0256 | 0.2706 | |
| | 4.0156 | 590000 | 0.0254 | 0.2706 | |
| | 4.0190 | 590500 | 0.0263 | 0.2695 | |
| | 4.0224 | 591000 | 0.0274 | 0.2691 | |
| | 4.0258 | 591500 | 0.0255 | 0.2712 | |
| | 4.0292 | 592000 | 0.0253 | 0.2696 | |
| | 4.0326 | 592500 | 0.025 | 0.2692 | |
| | 4.0360 | 593000 | 0.0263 | 0.2679 | |
| | 4.0394 | 593500 | 0.028 | 0.2689 | |
| | 4.0429 | 594000 | 0.0275 | 0.2696 | |
| | 4.0463 | 594500 | 0.0268 | 0.2699 | |
| | 4.0497 | 595000 | 0.025 | 0.2686 | |
| | 4.0531 | 595500 | 0.0277 | 0.2683 | |
| | 4.0565 | 596000 | 0.0276 | 0.2690 | |
| | 4.0599 | 596500 | 0.0242 | 0.2686 | |
| | 4.0633 | 597000 | 0.0264 | 0.2691 | |
| | 4.0667 | 597500 | 0.0273 | 0.2681 | |
| | 4.0701 | 598000 | 0.0269 | 0.2693 | |
| | 4.0735 | 598500 | 0.0274 | 0.2698 | |
| | 4.0769 | 599000 | 0.0252 | 0.2704 | |
| | 4.0803 | 599500 | 0.0268 | 0.2708 | |
| | 4.0837 | 600000 | 0.0259 | 0.2696 | |
| | 4.0871 | 600500 | 0.0277 | 0.2689 | |
| | 4.0905 | 601000 | 0.0262 | 0.2663 | |
| | 4.0939 | 601500 | 0.0266 | 0.2697 | |
| | 4.0973 | 602000 | 0.0269 | 0.2700 | |
| | 4.1007 | 602500 | 0.0253 | 0.2673 | |
| | 4.1041 | 603000 | 0.0281 | 0.2684 | |
| | 4.1075 | 603500 | 0.0263 | 0.2687 | |
| | 4.1109 | 604000 | 0.028 | 0.2677 | |
| | 4.1143 | 604500 | 0.0277 | 0.2701 | |
| | 4.1177 | 605000 | 0.0273 | 0.2686 | |
| | 4.1211 | 605500 | 0.0253 | 0.2681 | |
| | 4.1245 | 606000 | 0.0264 | 0.2694 | |
| | 4.1279 | 606500 | 0.0281 | 0.2706 | |
| | 4.1313 | 607000 | 0.0262 | 0.2714 | |
| | 4.1347 | 607500 | 0.0265 | 0.2673 | |
| | 4.1381 | 608000 | 0.0254 | 0.2685 | |
| | 4.1415 | 608500 | 0.0279 | 0.2674 | |
| | 4.1449 | 609000 | 0.0284 | 0.2692 | |
| | 4.1483 | 609500 | 0.0283 | 0.2680 | |
| | 4.1517 | 610000 | 0.0277 | 0.2673 | |
| | 4.1552 | 610500 | 0.0264 | 0.2692 | |
| | 4.1586 | 611000 | 0.0261 | 0.2687 | |
| | 4.1620 | 611500 | 0.0273 | 0.2697 | |
| | 4.1654 | 612000 | 0.027 | 0.2697 | |
| | 4.1688 | 612500 | 0.0274 | 0.2696 | |
| | 4.1722 | 613000 | 0.0273 | 0.2698 | |
| | 4.1756 | 613500 | 0.0255 | 0.2659 | |
| | 4.1790 | 614000 | 0.0274 | 0.2660 | |
| | 4.1824 | 614500 | 0.0284 | 0.2666 | |
| | 4.1858 | 615000 | 0.0268 | 0.2680 | |
| | 4.1892 | 615500 | 0.0278 | 0.2674 | |
| | 4.1926 | 616000 | 0.0276 | 0.2684 | |
| | 4.1960 | 616500 | 0.026 | 0.2700 | |
| | 4.1994 | 617000 | 0.0266 | 0.2686 | |
| | 4.2028 | 617500 | 0.0266 | 0.2680 | |
| | 4.2062 | 618000 | 0.0277 | 0.2678 | |
| | 4.2096 | 618500 | 0.0291 | 0.2649 | |
| | 4.2130 | 619000 | 0.0281 | 0.2635 | |
| | 4.2164 | 619500 | 0.0291 | 0.2659 | |
| | 4.2198 | 620000 | 0.0281 | 0.2672 | |
| | 4.2232 | 620500 | 0.0282 | 0.2655 | |
| | 4.2266 | 621000 | 0.0287 | 0.2648 | |
| | 4.2300 | 621500 | 0.0285 | 0.2640 | |
| | 4.2334 | 622000 | 0.0282 | 0.2645 | |
| | 4.2368 | 622500 | 0.027 | 0.2674 | |
| | 4.2402 | 623000 | 0.0268 | 0.2669 | |
| | 4.2436 | 623500 | 0.0291 | 0.2663 | |
| | 4.2470 | 624000 | 0.0291 | 0.2645 | |
| | 4.2504 | 624500 | 0.0277 | 0.2677 | |
| | 4.2538 | 625000 | 0.0273 | 0.2631 | |
| | 4.2572 | 625500 | 0.0265 | 0.2653 | |
| | 4.2606 | 626000 | 0.0276 | 0.2665 | |
| | 4.2641 | 626500 | 0.027 | 0.2654 | |
| | 4.2675 | 627000 | 0.0271 | 0.2659 | |
| | 4.2709 | 627500 | 0.0279 | 0.2659 | |
| | 4.2743 | 628000 | 0.0274 | 0.2648 | |
| | 4.2777 | 628500 | 0.0263 | 0.2659 | |
| | 4.2811 | 629000 | 0.0279 | 0.2665 | |
| | 4.2845 | 629500 | 0.028 | 0.2677 | |
| | 4.2879 | 630000 | 0.0299 | 0.2701 | |
| | 4.2913 | 630500 | 0.0284 | 0.2688 | |
| | 4.2947 | 631000 | 0.0269 | 0.2683 | |
| | 4.2981 | 631500 | 0.0271 | 0.2689 | |
| | 4.3015 | 632000 | 0.0288 | 0.2680 | |
| | 4.3049 | 632500 | 0.0274 | 0.2674 | |
| | 4.3083 | 633000 | 0.0277 | 0.2675 | |
| | 4.3117 | 633500 | 0.0282 | 0.2671 | |
| | 4.3151 | 634000 | 0.0266 | 0.2658 | |
| | 4.3185 | 634500 | 0.0284 | 0.2648 | |
| | 4.3219 | 635000 | 0.0283 | 0.2637 | |
| | 4.3253 | 635500 | 0.0283 | 0.2647 | |
| | 4.3287 | 636000 | 0.0281 | 0.2641 | |
| | 4.3321 | 636500 | 0.0275 | 0.2620 | |
| | 4.3355 | 637000 | 0.0272 | 0.2630 | |
| | 4.3389 | 637500 | 0.0282 | 0.2642 | |
| | 4.3423 | 638000 | 0.0294 | 0.2664 | |
| | 4.3457 | 638500 | 0.0283 | 0.2639 | |
| | 4.3491 | 639000 | 0.0262 | 0.2663 | |
| | 4.3525 | 639500 | 0.0275 | 0.2671 | |
| | 4.3559 | 640000 | 0.0298 | 0.2669 | |
| | 4.3593 | 640500 | 0.0292 | 0.2693 | |
| | 4.3627 | 641000 | 0.0283 | 0.2673 | |
| | 4.3661 | 641500 | 0.027 | 0.2687 | |
| | 4.3695 | 642000 | 0.0278 | 0.2663 | |
| | 4.3729 | 642500 | 0.0301 | 0.2652 | |
| | 4.3764 | 643000 | 0.0275 | 0.2676 | |
| | 4.3798 | 643500 | 0.0292 | 0.2680 | |
| | 4.3832 | 644000 | 0.0266 | 0.2680 | |
| | 4.3866 | 644500 | 0.0283 | 0.2668 | |
| | 4.3900 | 645000 | 0.0303 | 0.2677 | |
| | 4.3934 | 645500 | 0.0299 | 0.2701 | |
| | 4.3968 | 646000 | 0.0284 | 0.2680 | |
| | 4.4002 | 646500 | 0.0272 | 0.2664 | |
| | 4.4036 | 647000 | 0.0297 | 0.2662 | |
| | 4.4070 | 647500 | 0.029 | 0.2661 | |
| | 4.4104 | 648000 | 0.0281 | 0.2678 | |
| | 4.4138 | 648500 | 0.0282 | 0.2683 | |
| | 4.4172 | 649000 | 0.0278 | 0.2699 | |
| | 4.4206 | 649500 | 0.0309 | 0.2684 | |
| | 4.4240 | 650000 | 0.0288 | 0.2693 | |
| | 4.4274 | 650500 | 0.0307 | 0.2697 | |
| | 4.4308 | 651000 | 0.0272 | 0.2722 | |
| | 4.4342 | 651500 | 0.0289 | 0.2726 | |
| | 4.4376 | 652000 | 0.0288 | 0.2716 | |
| | 4.4410 | 652500 | 0.0289 | 0.2729 | |
| | 4.4444 | 653000 | 0.0297 | 0.2699 | |
| | 4.4478 | 653500 | 0.0286 | 0.2724 | |
| | 4.4512 | 654000 | 0.0298 | 0.2702 | |
| | 4.4546 | 654500 | 0.0302 | 0.2738 | |
| | 4.4580 | 655000 | 0.0292 | 0.2713 | |
| | 4.4614 | 655500 | 0.0297 | 0.2712 | |
| | 4.4648 | 656000 | 0.0286 | 0.2705 | |
| | 4.4682 | 656500 | 0.0285 | 0.2735 | |
| | 4.4716 | 657000 | 0.0294 | 0.2733 | |
| | 4.4750 | 657500 | 0.0291 | 0.2722 | |
| | 4.4784 | 658000 | 0.0283 | 0.2708 | |
| | 4.4818 | 658500 | 0.028 | 0.2714 | |
| | 4.4853 | 659000 | 0.0298 | 0.2716 | |
| | 4.4887 | 659500 | 0.0275 | 0.2721 | |
| | 4.4921 | 660000 | 0.0314 | 0.2731 | |
| | 4.4955 | 660500 | 0.0292 | 0.2730 | |
| | 4.4989 | 661000 | 0.029 | 0.2749 | |
| |
| </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", |
| } |
| ``` |
| |
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