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 vehicle-ownership aligner (transport mode / car-bike ownership consistency) 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 37-year-old male working as a professional. They have vocational or technical level education and poor health. They live as: living alone. \nCity context: Greater Paris metropolitan region, urban mobility with commuting, errands, leisure, healthcare, studies, and home routines.\nScore how likely this person is to have the listed transport option. Use 0 for very unlikely and 1 for very likely.', 'owns or has reliable access to a private car for everyday travel'],
    ['Barbara is a 40-year-old female working as a manager. They have no diploma level education and good health. They live as: living alone. \nCity context: Greater Paris metropolitan region, urban mobility with commuting, errands, leisure, healthcare, studies, and home routines.\nScore how likely this person is to have the listed transport option. Use 0 for very unlikely and 1 for very likely.', 'owns or has reliable access to a private car for everyday travel'],
    ['Patricia is a 32-year-old female working as a manager. They have no diploma level education and fair health. They live as: couple with children. \nCity context: Greater Paris metropolitan region, urban mobility with commuting, errands, leisure, healthcare, studies, and home routines.\nScore how likely this person is to have the listed transport option. Use 0 for very unlikely and 1 for very likely.', 'owns or has reliable access to a private car for everyday travel'],
    ['Charles is a 41-year-old male working as a machine operator or assembler. They have secondary or less level education and good health. They live as: living with another family member. \nCity context: Greater Paris metropolitan region, urban mobility with commuting, errands, leisure, healthcare, studies, and home routines.\nScore how likely this person is to have the listed transport option. Use 0 for very unlikely and 1 for very likely.', 'owns or has reliable access to a bicycle, e-bike, or equivalent personal cycle'],
    ['Emma is a 26-year-old female working as a professional. They have bachelor level education and poor health. They live as: couple with children. \nCity context: Greater Paris metropolitan region, urban mobility with commuting, errands, leisure, healthcare, studies, and home routines.\nScore how likely this person is to have the listed transport option. Use 0 for very unlikely and 1 for very likely.', 'owns or has reliable access to a private car for everyday travel'],
]
scores = model.predict(pairs)
print(scores)
# [0.4779 0.6005 0.6155 0.6702 0.5633]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Thomas is a 37-year-old male working as a professional. They have vocational or technical level education and poor health. They live as: living alone. \nCity context: Greater Paris metropolitan region, urban mobility with commuting, errands, leisure, healthcare, studies, and home routines.\nScore how likely this person is to have the listed transport option. Use 0 for very unlikely and 1 for very likely.',
    [
        'owns or has reliable access to a private car for everyday travel',
        'owns or has reliable access to a private car for everyday travel',
        'owns or has reliable access to a private car for everyday travel',
        'owns or has reliable access to a bicycle, e-bike, or equivalent personal cycle',
        'owns or has reliable access to a private car for everyday travel',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

Unnamed Dataset

Size: 2,000 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: 86 tokens mean: 89.93 tokens max: 95 tokens	min: 15 tokens mean: 17.71 tokens max: 21 tokens	min: 0.2 mean: 0.58 max: 0.85

Samples:

sentence_0	sentence_1	label
`Thomas is a 37-year-old male working as a professional. They have vocational or technical level education and poor health. They live as: living alone. City context: Greater Paris metropolitan region, urban mobility with commuting, errands, leisure, healthcare, studies, and home routines. Score how likely this person is to have the listed transport option. Use 0 for very unlikely and 1 for very likely.`	`owns or has reliable access to a private car for everyday travel`	`0.6`
`Barbara is a 40-year-old female working as a manager. They have no diploma level education and good health. They live as: living alone. City context: Greater Paris metropolitan region, urban mobility with commuting, errands, leisure, healthcare, studies, and home routines. Score how likely this person is to have the listed transport option. Use 0 for very unlikely and 1 for very likely.`	`owns or has reliable access to a private car for everyday travel`	`0.7`
`Patricia is a 32-year-old female working as a manager. They have no diploma level education and fair health. They live as: couple with children. City context: Greater Paris metropolitan region, urban mobility with commuting, errands, leisure, healthcare, studies, and home routines. Score how likely this person is to have the listed transport option. Use 0 for very unlikely and 1 for very likely.`	`owns or has reliable access to a private car for everyday travel`	`0.7`

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: 3.3 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",
}