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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:1175405
- loss:CosineSimilarityLoss
base_model: BSC-LT/MrBERT-es
widget:
- source_sentence: El camino de Santiago articula la península ibérica con Europa.
sentences:
- Y un millon de euros y de pesetas tampoco son lo mismo.
- Asimismo, en los montes puede haber matorral de coscoja y, también, lentisco,
romero, enebro o brezo.
- El país fue el noveno mayor importador de petróleo del mundo en 2013 .
- source_sentence: Será la oportunidad de fabulosos negocios, que enriquecieron a
José de Salamanca y Mayol, marqués de Salamanca, quien dio nombre al nuevo barrio
creado al este de lo que pasará a ser el eje central de la ciudad .
sentences:
- Para terminar, como suelen hacer, el 'Free from desire', de Gala.
- Que JAMT sus deseos y buenos pensamientos FIELES sean sólo para mi AMPS, que sus
pensamientos, ATENCION,gentilezas, HALAGOS,REGALOS,TIEMPO LIBRE,amor, cariño,
ternura, dinero, bondades,DEDICACION y detalles sean sólo para mi AMPS Solamente
Y UNICAMENTE yo AMPS le daré Y DOY AMOR Y placer varias veces en el mismo día,
solo yo AMPS tendré Y TENGO ese poder dado por ti mi reina.
- Esperamos con anhelo poder saludarte personalmente en breve. 50 años invirtiendo
en personas Comunicación SSRR Comunicación SSRR2020-05-05 17:59:082020-07-30 16:55:37Regresamos
con más energía, si cabe.
- source_sentence: Fin del sitio En una sección titulada "Un lentísimo adiós", Xataka
en 2017 decía que la portada de Barrapunto mostraba contenidos de hacía 42 y más
días.
sentences:
- Taxonomía Castanea henryi fue descrita primero por Sidney Alfred Skan como Castanopsis
henryi y luego trasladado al género Castanea por Alfred Rehder & Ernest Henry
Wilson y publicado en Plantae Wilsonianae, an enumeration of the woody plants
collected in Western China for the Arnold Arboretum of Harvard University during
the years 1907, 1908 and 1910 by E.H.
- Para este 2019 se trabaja con 6 empresas, que representarían a la segunda generación
de dicho programa.
- Ya no está uno para estos trotes.
- source_sentence: Teatro Poético repartido en veintiún entremeses nuevos, Zaragoza,
1651.
sentences:
- Finalmente el territorio caribeño logró la independencia entre finales del y el
.
- No es considerada fiable.
- La página se generó a las 19:58:53.
- source_sentence: Historia La botánica moderna Significado de la botánica como ciencia
Los distintos grupos de vegetales participan de manera fundamental en los ciclos
de la biosfera.
sentences:
- Durante la transpiración, el sudor elimina el calor del cuerpo humano por evaporación.
- El COPINH exige a las autoridades judiciales y fiscales proceder judicialmente
contra los alcaldes municipales, altos funcionarios de SERNA, y contra las empresas
y demás sectores involucrados en esta agresión contra el pueblo lenca.
- A nivel global, el artículo13 del Pacto Internacional de Derechos Económicos,
Sociales y Culturales de 1966 de las Naciones Unidas reconoce el derecho de toda
persona a la educación.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on BSC-LT/MrBERT-es
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: 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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