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 profile-coherence aligner (demographic profile consistency scoring) 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 = [
    ['Chloé is a 17-year-old female working as a manager. They have master or above level education and poor health. They live as: couple with children. \nScore whether this synthetic agent profile is demographically coherent and plausible. Use 0 for impossible or highly inconsistent profiles and 1 for fully coherent profiles.', 'demographically coherent and valid synthetic agent profile'],
    ['Clément is a 26-year-old male working as a technician or associate professional. They have secondary or less level education and good health. They live as: living with parents. \nScore whether this synthetic agent profile is demographically coherent and plausible. Use 0 for impossible or highly inconsistent profiles and 1 for fully coherent profiles.', 'demographically coherent and valid synthetic agent profile'],
    ['Chloé is a 25-year-old female working as a service or sales worker. They have secondary or less level education and very poor health. They live as: couple without children. \nScore whether this synthetic agent profile is demographically coherent and plausible. Use 0 for impossible or highly inconsistent profiles and 1 for fully coherent profiles.', 'demographically coherent and valid synthetic agent profile'],
    ['Théo is a 42-year-old male working as a service or sales worker. They have bachelor level education and good health. They live as: living alone. \nScore whether this synthetic agent profile is demographically coherent and plausible. Use 0 for impossible or highly inconsistent profiles and 1 for fully coherent profiles.', 'demographically coherent and valid synthetic agent profile'],
    ['Camille is a 16-year-old female working as a manager. They have no diploma level education and good health. They live as: couple with children. \nScore whether this synthetic agent profile is demographically coherent and plausible. Use 0 for impossible or highly inconsistent profiles and 1 for fully coherent profiles.', 'demographically coherent and valid synthetic agent profile'],
]
scores = model.predict(pairs)
print(scores)
# [0.0939 0.7422 0.8488 0.9852 0.1007]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Chloé is a 17-year-old female working as a manager. They have master or above level education and poor health. They live as: couple with children. \nScore whether this synthetic agent profile is demographically coherent and plausible. Use 0 for impossible or highly inconsistent profiles and 1 for fully coherent profiles.',
    [
        'demographically coherent and valid synthetic agent profile',
        'demographically coherent and valid synthetic agent profile',
        'demographically coherent and valid synthetic agent profile',
        'demographically coherent and valid synthetic agent profile',
        'demographically coherent and valid synthetic agent profile',
    ]
)
# [{'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: 62 tokensmean: 66.98 tokensmax: 73 tokens	min: 10 tokensmean: 10.0 tokensmax: 10 tokens	min: 0.0mean: 0.43max: 1.0

• Samples:

  sentence_0	sentence_1	label
  Chloé is a 17-year-old female working as a manager. They have master or above level education and poor health. They live as: couple with children. Score whether this synthetic agent profile is demographically coherent and plausible. Use 0 for impossible or highly inconsistent profiles and 1 for fully coherent profiles.	demographically coherent and valid synthetic agent profile	0.1
  Clément is a 26-year-old male working as a technician or associate professional. They have secondary or less level education and good health. They live as: living with parents. Score whether this synthetic agent profile is demographically coherent and plausible. Use 0 for impossible or highly inconsistent profiles and 1 for fully coherent profiles.	demographically coherent and valid synthetic agent profile	0.8
  Chloé is a 25-year-old female working as a service or sales worker. They have secondary or less level education and very poor health. They live as: couple without children. Score whether this synthetic agent profile is demographically coherent and plausible. Use 0 for impossible or highly inconsistent profiles and 1 for fully coherent profiles.	demographically coherent and valid synthetic agent profile	0.8

• Loss: BinaryCrossEntropyLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• num_train_epochs: 10

All Hyperparameters

• per_device_train_batch_size: 8
• num_train_epochs: 10
• 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: 9.9 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",
}