gefgu/modernbert-schedule-aligner

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 macro-schedule aligner (SW-CRP profile-to-diary matching) 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 = [
    ['Thomas is a 28-year-old male working as a professional. They have vocational or technical level education and good health. They live as: single parent. They own a car and a bike.', 'Stays at home the entire day (24 hours).'],
    ['Pauline is a 33-year-old female working as a professional. They have secondary or less level education and good health. They live as: couple without children. They own a car and a bike.', 'Spends 11 hours at home; work from 07:00 to 08:30, other from 08:30 to 09:00, work from 09:00 to 12:00, other from 13:00 to 14:00, work from 14:00 to 18:00, other from 19:00 to 22:00.'],
    ['Zoé is a 18-year-old female working as a service or sales worker. They have secondary or less level education and good health. They live as: living alone. They own a car and a bike.', 'Spends 19 hours at home; work from 07:00 to 08:30, other from 08:30 to 09:00, other from 09:00 to 09:30, other from 10:00 to 11:00, other from 11:30 to 13:00.'],
    ['Manon is a 37-year-old female working as a manager. They have secondary or less level education and fair health. They live as: living alone. They own a car and a bike.', 'Spends 20 hours at home; other from 08:30 to 12:00.'],
    ['Emma is a 35-year-old female working as a professional. They have secondary or less level education and good health. They live as: living with parents. They own a car and a bike.', 'Spends 15 hours at home; other from 09:00 to 10:30, other from 10:30 to 11:45, other from 11:45 to 13:00, other from 14:00 to 17:00, other from 18:00 to 20:00.'],
]
scores = model.predict(pairs)
print(scores)
# [0.2354 0.8564 0.561  0.3906 0.8158]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Thomas is a 28-year-old male working as a professional. They have vocational or technical level education and good health. They live as: single parent. They own a car and a bike.',
    [
        'Stays at home the entire day (24 hours).',
        'Spends 11 hours at home; work from 07:00 to 08:30, other from 08:30 to 09:00, work from 09:00 to 12:00, other from 13:00 to 14:00, work from 14:00 to 18:00, other from 19:00 to 22:00.',
        'Spends 19 hours at home; work from 07:00 to 08:30, other from 08:30 to 09:00, other from 09:00 to 09:30, other from 10:00 to 11:00, other from 11:30 to 13:00.',
        'Spends 20 hours at home; other from 08:30 to 12:00.',
        'Spends 15 hours at home; other from 09:00 to 10:30, other from 10:30 to 11:45, other from 11:45 to 13:00, other from 14:00 to 17:00, other from 18:00 to 20:00.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,000 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: 38 tokens mean: 43.87 tokens max: 50 tokens	min: 13 tokens mean: 33.4 tokens max: 69 tokens	min: 0.0 mean: 0.58 max: 1.0

• Samples:

  sentence_0	sentence_1	label
  Thomas is a 28-year-old male working as a professional. They have vocational or technical level education and good health. They live as: single parent. They own a car and a bike.	Stays at home the entire day (24 hours).	0.2
  Pauline is a 33-year-old female working as a professional. They have secondary or less level education and good health. They live as: couple without children. They own a car and a bike.	Spends 11 hours at home; work from 07:00 to 08:30, other from 08:30 to 09:00, work from 09:00 to 12:00, other from 13:00 to 14:00, work from 14:00 to 18:00, other from 19:00 to 22:00.	0.85
  Zoé is a 18-year-old female working as a service or sales worker. They have secondary or less level education and good health. They live as: living alone. They own a car and a bike.	Spends 19 hours at home; work from 07:00 to 08:30, other from 08:30 to 09:00, other from 09:00 to 09:30, other from 10:00 to 11:00, other from 11:30 to 13:00.	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: 1

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• 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: 1
• num_train_epochs: 1
• 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: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Time

• Training: 1.6 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.6.0
• Transformers: 4.57.6
• PyTorch: 2.6.0+cu124
• Accelerate: 1.14.0
• Datasets: 5.0.0
• Tokenizers: 0.22.2

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",
}