CrossEncoder based on nomic-ai/modernbert-embed-base

This is a Cross Encoder model finetuned from nomic-ai/modernbert-embed-base using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

CityBehavEx

This checkpoint is the POI-type aligner (semantic POI/location-type selection) in CityBehavEx, a scalable, empirically validated LLM-assisted urban mobility simulation platform. It is served alongside CityBehavEx's other CrossEncoder aligners by scripts/serve_aligners.py and referenced directly by repo id in scenario configs (e.g. schedule.alignment_model, activities.alignment_model, profiles.coherence_alignment_model, profiles.ownership_alignment_model, activities.poi_type_alignment_model).

If you use this model, please cite CityBehavEx:

@misc{santos2026citybehavex,
  title     = {CityBehavEx: A Scalable and Empirically Validated LLM-Assisted Urban Simulation Platform},
  author    = {Santos, Gustavo H. and Viana, Aline and Silva, Thiago H.},
  year      = {2026},
  eprint    = {2607.12086},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url       = {https://arxiv.org/abs/2607.12086}
}

Model Details

Model Description

• Model Type: Cross Encoder
• Base model: nomic-ai/modernbert-embed-base
• Maximum Sequence Length: 8192 tokens
• Number of Output Labels: 1 label
• Supported Modality: Text

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'ModernBertForSequenceClassification'})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("cross_encoder_model_id")
# Get scores for pairs of inputs
pairs = [
    ['Charlotte is a 33-year-old female working as a clerical support worker. They have bachelor level education and good health. They live as: couple without children. They own a car and a bike.\nSchedule block: diary routine-017, block 1, OTHER from 12:30 to 15:00.\nScore which kind of public place best fits this person and schedule block.', 'retail_shopping: public place type with example Overture categories academic_bookstore, adult_store, antique_store, appliance_store, aquatic_pet_store, archery_shop, audio_visual_equipment_store, auto_body_shop, avionics_shop, baby_gear_and_furniture, bagel_shop, beverage_store'],
    ['Charles is a 37-year-old male working as a elementary worker. They have vocational or technical level education and good health. They live as: living alone. They own a car.\nSchedule block: diary routine-021, block 4, OTHER from 14:00 to 18:00.\nScore which kind of public place best fits this person and schedule block.', 'work_industry: public place type with example Overture categories advertising_agency, agriculture, aircraft_manufacturer, appliance_manufacturer, auto_company, auto_manufacturers_and_distributors, bags_luggage_company, biotechnology_company, bottled_water_company, building_contractor, business_office_supplies_and_stationery, central_government_office'],
    ['Pauline is a 34-year-old female working as a craft or trades worker. They have secondary or less level education and good health. They live as: couple with children. They own a car.\nSchedule block: diary routine-016, block 2, OTHER from 08:30 to 12:00.\nScore which kind of public place best fits this person and schedule block.', 'personal_services: public place type with example Overture categories abuse_and_addiction_treatment, accountant, agricultural_service, aircraft_repair, alcohol_and_drug_treatment_centers, ambulance_and_ems_services, animal_rescue_service, appliance_repair_service, appraisal_services, archaeological_services, art_restoration_service, atms'],
    ['Marie is a 21-year-old female working as a craft or trades worker. They have secondary or less level education and poor health. They live as: couple with children. They rely on public transport or walking.\nSchedule block: diary routine-026, block 1, OTHER from 09:00 to 10:30.\nScore which kind of public place best fits this person and schedule block.', 'education: public place type with example Overture categories adult_education, art_school, boxing_class, circus_school, college_university, cooking_school, cosmetology_school, cycling_classes, dance_school, day_care_preschool, driving_school, education'],
    ['Mary is a 23-year-old female working as a manager. They have secondary or less level education and very good health. They live as: couple without children. They own a car and a bike.\nSchedule block: diary routine-029, block 5, OTHER from 11:00 to 12:00.\nScore which kind of public place best fits this person and schedule block.', 'health_care: public place type with example Overture categories acupuncture, aromatherapy, cannabis_clinic, childrens_hospital, chiropractor, clinical_laboratories, cosmetic_dentist, counseling_and_mental_health, dentist, dialysis_clinic, doctor, eye_care_clinic'],
]
scores = model.predict(pairs)
print(scores)
# [0.3402 0.1099 0.3431 0.1385 0.0929]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Charlotte is a 33-year-old female working as a clerical support worker. They have bachelor level education and good health. They live as: couple without children. They own a car and a bike.\nSchedule block: diary routine-017, block 1, OTHER from 12:30 to 15:00.\nScore which kind of public place best fits this person and schedule block.',
    [
        'retail_shopping: public place type with example Overture categories academic_bookstore, adult_store, antique_store, appliance_store, aquatic_pet_store, archery_shop, audio_visual_equipment_store, auto_body_shop, avionics_shop, baby_gear_and_furniture, bagel_shop, beverage_store',
        'work_industry: public place type with example Overture categories advertising_agency, agriculture, aircraft_manufacturer, appliance_manufacturer, auto_company, auto_manufacturers_and_distributors, bags_luggage_company, biotechnology_company, bottled_water_company, building_contractor, business_office_supplies_and_stationery, central_government_office',
        'personal_services: public place type with example Overture categories abuse_and_addiction_treatment, accountant, agricultural_service, aircraft_repair, alcohol_and_drug_treatment_centers, ambulance_and_ems_services, animal_rescue_service, appliance_repair_service, appraisal_services, archaeological_services, art_restoration_service, atms',
        'education: public place type with example Overture categories adult_education, art_school, boxing_class, circus_school, college_university, cooking_school, cosmetology_school, cycling_classes, dance_school, day_care_preschool, driving_school, education',
        'health_care: public place type with example Overture categories acupuncture, aromatherapy, cannabis_clinic, childrens_hospital, chiropractor, clinical_laboratories, cosmetic_dentist, counseling_and_mental_health, dentist, dialysis_clinic, doctor, eye_care_clinic',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

Unnamed Dataset

• Size: 8,000 training samples
• Columns: sentence_0, sentence_1, and label
• Approximate statistics based on the first 100 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  modality	text	text
  details	min: 76 tokensmean: 80.8 tokensmax: 86 tokens	min: 58 tokensmean: 75.8 tokensmax: 93 tokens	min: 0.1mean: 0.36max: 0.8

• Samples:

  sentence_0	sentence_1	label
  Charlotte is a 33-year-old female working as a clerical support worker. They have bachelor level education and good health. They live as: couple without children. They own a car and a bike.Schedule block: diary routine-017, block 1, OTHER from 12:30 to 15:00.Score which kind of public place best fits this person and schedule block.	retail_shopping: public place type with example Overture categories academic_bookstore, adult_store, antique_store, appliance_store, aquatic_pet_store, archery_shop, audio_visual_equipment_store, auto_body_shop, avionics_shop, baby_gear_and_furniture, bagel_shop, beverage_store	0.3
  Charles is a 37-year-old male working as a elementary worker. They have vocational or technical level education and good health. They live as: living alone. They own a car.Schedule block: diary routine-021, block 4, OTHER from 14:00 to 18:00.Score which kind of public place best fits this person and schedule block.	work_industry: public place type with example Overture categories advertising_agency, agriculture, aircraft_manufacturer, appliance_manufacturer, auto_company, auto_manufacturers_and_distributors, bags_luggage_company, biotechnology_company, bottled_water_company, building_contractor, business_office_supplies_and_stationery, central_government_office	0.1
  Pauline is a 34-year-old female working as a craft or trades worker. They have secondary or less level education and good health. They live as: couple with children. They own a car.Schedule block: diary routine-016, block 2, OTHER from 08:30 to 12:00.Score which kind of public place best fits this person and schedule block.	personal_services: public place type with example Overture categories abuse_and_addiction_treatment, accountant, agricultural_service, aircraft_repair, alcohol_and_drug_treatment_centers, ambulance_and_ems_services, animal_rescue_service, appliance_repair_service, appraisal_services, archaeological_services, art_restoration_service, atms	0.3

• Loss: BinaryCrossEntropyLoss with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• num_train_epochs: 5

All Hyperparameters

• per_device_train_batch_size: 8
• num_train_epochs: 5
• max_steps: -1
• learning_rate: 5e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 1
• average_tokens_across_devices: True
• max_grad_norm: 1
• label_smoothing_factor: 0.0
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: None
• trackio_bucket_id: None
• trackio_static_space_id: None
• per_device_eval_batch_size: 8
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: False
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• 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
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• dataloader_multiprocessing_context: None
• dataloader_in_order: True
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_static_graph: None
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: None
• fsdp_config: None
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}
• warmup_ratio: None

Training Logs

Training Time

• Training: 6.6 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 6.0.1
• Transformers: 5.17.0
• PyTorch: 2.11.0+cu130
• Accelerate: 1.15.0
• Datasets: 5.0.1
• Tokenizers: 0.23.2

Additional Resources

• Training and Finetuning Reranker Models with Sentence Transformers: the end-to-end guide for training or finetuning Cross Encoder (reranker) models.
• Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video reranker models through the same API.
• Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: training multimodal Cross Encoders.

Citation

BibTeX

Sentence Transformers

@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",
}