lucasflins/CE-BERT-Tiny_L-2_H-128_A-2-BCE-20260207-084638

CrossEncoder based on nreimers/BERT-Tiny_L-2_H-128_A-2

This is a Cross Encoder model finetuned from nreimers/BERT-Tiny_L-2_H-128_A-2 using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

• Model Type: Cross Encoder
• Base model: nreimers/BERT-Tiny_L-2_H-128_A-2
• Maximum Sequence Length: 64 tokens
• Number of Output Labels: 1 label

Model Sources

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

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("lucasflins/CE-BERT-Tiny_L-2_H-128_A-2-BCE-20260207-084638")
# Get scores for pairs of texts
pairs = [
    ['aparador off white', 'aparador para sala ambiente classic off white nature - imcal'],
    ['itatiaia renova', 'balcão itatiaia branco renova com 3 portas e 2 gavetas'],
    ['cozinhas moduladas completas 100 mdf', 'cozinha completa 7 peças 100% mdf com portas de vidro americana'],
    ['caixa de som', 'caixa de som torre double 12 2300w bluetooth pulse - ps736'],
    ['escrivaninha 90cm', 'escrivaninha mesa escritório dobrável 90cm industrial steel quadra'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'aparador off white',
    [
        'aparador para sala ambiente classic off white nature - imcal',
        'balcão itatiaia branco renova com 3 portas e 2 gavetas',
        'cozinha completa 7 peças 100% mdf com portas de vidro americana',
        'caixa de som torre double 12 2300w bluetooth pulse - ps736',
        'escrivaninha mesa escritório dobrável 90cm industrial steel quadra',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

Unnamed Dataset

• Size: 3,891,914 training samples
• Columns: query, text, and label
• Approximate statistics based on the first 1000 samples:

  	query	text	label
  type	string	string	float
  details	min: 2 characters mean: 22.67 characters max: 80 characters	min: 20 characters mean: 67.39 characters max: 188 characters	min: 0.0 mean: 0.47 max: 1.0

• Samples:

  query	text	label
  tenis under armour masculino	tênis under armour charged slight 3 se	0.9281755975185405
  jogos de cama casal 100 algodao 4 pecas	lençol casal 400 fios 3 peças toque 100% macio com fronhas	0.5433727088037827
  geladeira para caminhao	geladeira portátil 40l 12v/24v 110v/220v caminhão ônibus van	0.7394712159210983

• Loss: BinaryCrossEntropyLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• Size: 1,323,175 evaluation samples
• Columns: query, text, and label
• Approximate statistics based on the first 1000 samples:

  	query	text	label
  type	string	string	float
  details	min: 3 characters mean: 22.38 characters max: 76 characters	min: 15 characters mean: 66.75 characters max: 150 characters	min: 0.0 mean: 0.46 max: 1.0

• Samples:

  query	text	label
  aparador off white	aparador para sala ambiente classic off white nature - imcal	0.3710301443240389
  itatiaia renova	balcão itatiaia branco renova com 3 portas e 2 gavetas	0.35256627704291904
  cozinhas moduladas completas 100 mdf	cozinha completa 7 peças 100% mdf com portas de vidro americana	0.2038212525937755

• Loss: BinaryCrossEntropyLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 2048
• per_device_eval_batch_size: 32
• num_train_epochs: 10
• warmup_ratio: 0.1
• log_level: info
• tf32: True
• load_best_model_at_end: True
• hub_strategy: end
• hub_private_repo: True
• hub_always_push: True
• eval_on_start: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 2048
• per_device_eval_batch_size: 32
• 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
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 10
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: info
• 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: True
• 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: True
• 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: end
• hub_private_repo: True
• hub_always_push: True
• 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: True
• 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 Logs

Epoch	Step	Training Loss	Validation Loss
0	0	-	0.6922
1.0	1901	0.6888	0.6872
2.0	3802	0.6802	0.6879
3.0	5703	0.675	0.6902
4.0	7604	0.6715	0.6912

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.11
• Sentence Transformers: 5.2.0
• Transformers: 4.57.6
• PyTorch: 2.7.1+cu128
• Accelerate: 1.9.0
• Datasets: 4.1.1
• Tokenizers: 0.22.1

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",
}