CE-MiniLM-L3-H384-uncased-RankNet-20260213-175339

CrossEncoder based on nreimers/MiniLM-L3-H384-uncased

This is a Cross Encoder model finetuned from nreimers/MiniLM-L3-H384-uncased 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/MiniLM-L3-H384-uncased <!-- at revision 1857068a44253cf9ab23aefdf562335601cffcda -->
• Maximum Sequence Length: 256 tokens
• Number of Output Labels: 1 label

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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-MiniLM-L3-H384-uncased-RankNet-20260213-175339")
# Get scores for pairs of texts
pairs = [
    ['maquina de lavar roupa panasonic', 'máquina de lavar 17kg f170b7wa branca 110v panasonic'],
    ['maquina de lavar roupa panasonic', 'máquina de lavar panasonic na-f180p7t 18kg'],
    ['maquina de lavar roupa panasonic', 'capa maquina lavar roupa impermeavel protetora panasonic 16kg naf160b6w zíper transparente preta'],
    ['maquina de lavar roupa panasonic', 'lavadora panasonic na-f180p7ta 18kg com tecnologia antibacteria cinza'],
    ['maquina de lavar roupa panasonic', 'lavadora de roupas panasonic f180p7t antibacteria ag 18kg'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'maquina de lavar roupa panasonic',
    [
        'máquina de lavar 17kg f170b7wa branca 110v panasonic',
        'máquina de lavar panasonic na-f180p7t 18kg',
        'capa maquina lavar roupa impermeavel protetora panasonic 16kg naf160b6w zíper transparente preta',
        'lavadora panasonic na-f180p7ta 18kg com tecnologia antibacteria cinza',
        'lavadora de roupas panasonic f180p7t antibacteria ag 18kg',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 58,401 training samples
• Columns: <code>query</code>, <code>text</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	query	text	label
  type	string	list	list
  details	<ul><li>min: 2 characters</li><li>mean: 23.86 characters</li><li>max: 84 characters</li></ul>	<ul><li>min: 3 elements</li><li>mean: 113.68 elements</li><li>max: 647 elements</li></ul>	<ul><li>min: 3 elements</li><li>mean: 113.68 elements</li><li>max: 647 elements</li></ul>

• Samples:

  query	text	label
  <code>mini berco e colchao</code>	<code>['moises mini berço malu para quarto do bebê com rodizios para meninos e meninas moderno com colchão multifuncional kids cor branco', 'mini berço multifuncional evolutivo 4 em 1 sem colchão bb040 completa móveis', 'mini berço moisés multifuncional mdf e rodízios com colchão', 'mini berço 2 gavetas com colchão moisés - branco', 'mini berço 100% mdf com colchão yescasa', ...]</code>	<code>[4.5160912172672525, 4.301410246007121, 4.149199637266272, 4.102236270603044, 4.0407219899682385, ...]</code>
  <code>chinelo havaianas adulto original masculino</code>	<code>['chinelo havaianas masculino dual tiras larga ultra conforto', 'chinelo havaianas brasil adulto - branco', 'chinelo havaianas brasil branco adulto as legitimas original', 'chinelo de dedo adulto havaianas top basic fc', 'chinelo de dedo havaianas adulto dual masculino duas cores', ...]</code>	<code>[5.0, 1.3071868869897774, 1.2851885677229884, 1.2841688461219265, 1.262460870634281, ...]</code>
  <code>armario emilly</code>	<code>['armário de cozinha completa 229cm rustic/cinza emilly madesa 017', 'armário de cozinha completa 229cm branco emilly madesa 17', 'armário de cozinha compacta 105cm com pia emilly madesa 04', 'armário de cozinha completa madesa emilly 293001 com balcão e paneleiro - rustic/preto', 'armário de cozinha completa com armário e balcão 229cm emilly madesa', ...]</code>	<code>[5.0, 4.8185494408696865, 4.732788739215357, 4.663854171188608, 4.617143051987073, ...]</code>

• Loss: <code>RankNetLoss</code> with these parameters:

  {
      "k": null,
      "sigma": 1.0,
      "eps": 1e-10,
      "reduction_log": "binary",
      "activation_fn": "torch.nn.modules.linear.Identity",
      "mini_batch_size": null
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 19,453 evaluation samples
• Columns: <code>query</code>, <code>text</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	query	text	label
  type	string	list	list
  details	<ul><li>min: 3 characters</li><li>mean: 23.46 characters</li><li>max: 88 characters</li></ul>	<ul><li>min: 3 elements</li><li>mean: 105.95 elements</li><li>max: 374 elements</li></ul>	<ul><li>min: 3 elements</li><li>mean: 105.95 elements</li><li>max: 374 elements</li></ul>

• Samples:

  query	text	label
  <code>maquina de lavar roupa panasonic</code>	<code>['máquina de lavar 17kg f170b7wa branca 110v panasonic', 'máquina de lavar panasonic na-f180p7t 18kg', 'capa maquina lavar roupa impermeavel protetora panasonic 16kg naf160b6w zíper transparente preta', 'lavadora panasonic na-f180p7ta 18kg com tecnologia antibacteria cinza', 'lavadora de roupas panasonic f180p7t antibacteria ag 18kg', ...]</code>	<code>[5.0, 0.7308349126503895, 0.6959629150629015, 0.6477526610672186, 0.5892796273761252, ...]</code>
  <code>gyarda roupa</code>	<code>['guarda-roupa casal com espelho 3 portas de correr', 'guarda-roupa casal madesa reno 3 portas de correr com espelho', 'guarda roupa mongi 2 portas 2 gavetas branco caemmun', 'guarda-roupa casal com espelho 4 gavetas 2 portas de correr araplac benfica', 'guarda-roupa casal com espelho 6 portas', ...]</code>	<code>[5.0, 3.3783316133271213, 3.3299655932869037, 3.317338034957803, 3.1937724871830113, ...]</code>
  <code>purificador de agua colormaq premium</code>	<code>['purificador de agua compressor premium narural e gelada colormaq', 'purificador de agua colormaq compressor branco 220v cpuhfba1', 'purificador de agua colormaq premium compressor cpuhfba1', 'purificador de água colormaq premium', 'purificador de água colormaq refrigerado por', ...]</code>	<code>[5.0, 2.7724961811106326, 2.3496201386993913, 1.9642276637675304, 1.154319875829175, ...]</code>

• Loss: <code>RankNetLoss</code> with these parameters:

  {
      "k": null,
      "sigma": 1.0,
      "eps": 1e-10,
      "reduction_log": "binary",
      "activation_fn": "torch.nn.modules.linear.Identity",
      "mini_batch_size": null
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 16
• 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

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 16
• 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: {}

</details>

Training Logs

• 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",
}

RankNetLoss

@inproceedings{burges2005learning,
  title={Learning to Rank using Gradient Descent},
  author={Burges, Chris and Shaked, Tal and Renshaw, Erin and Lazier, Ari and Deeds, Matt and Hamilton, Nicole and Hullender, Greg},
  booktitle={Proceedings of the 22nd international conference on Machine learning},
  pages={89--96},
  year={2005}
}

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