--- 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) - **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 ### 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]]) ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts_eval` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.4611 | | **spearman_cosine** | **0.2749** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,175,405 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| | Los ahorros de la jubilación podrán usarse para este fin. | 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. | 0.2533760964870453 | | 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. | 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. | 0.1902337223291397 | | Bolsa inmobiliaria online en Distrito Federal df, inmuebles en venta y renta, casas, departamentos, locales, terrenos, inmobiliarias, desarrollos, anunciar inmuebles. | 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. | 0.21721388399600983 | * Loss: [CosineSimilarityLoss](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
Click to expand - `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`: {}
### Training Logs
Click to expand | 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 |
### 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", } ```