reranker-22m

CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2

This is a Cross Encoder model finetuned from cross-encoder/ms-marco-MiniLM-L6-v2 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: cross-encoder/ms-marco-MiniLM-L6-v2 <!-- at revision c5ee24cb16019beea0893ab7796b1df96625c6b8 -->
• Maximum Sequence Length: 512 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("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
    ['what is the average payment volume per transaction for american express?', '(table): company the american express of payments volume ( billions ) is 637 ; the american express of total volume ( billions ) is 647 ; the american express of total transactions ( billions ) is 5.0 ; the american express of cards ( millions ) is 86 ;'],
    ['what is the average payment volume per transaction for american express?', '(text): largest operators of open-loop and closed-loop retail electronic payments networks the largest operators of open-loop and closed-loop retail electronic payments networks are visa , mastercard , american express , discover , jcb and diners club .'],
    ['what is the average payment volume per transaction for american express?', '(text): with the exception of discover , which primarily operates in the united states , all of the other network operators can be considered multi- national or global providers of payments network services .'],
    ['what is the average payment volume per transaction for american express?', '(text): based on payments volume , total volume , number of transactions and number of cards in circulation , visa is the largest retail electronic payments network in the world .'],
    ['what is the average payment volume per transaction for american express?', '(text): the following chart compares our network with those of our major competitors for calendar year 2007 : company payments volume volume transactions cards ( billions ) ( billions ) ( billions ) ( millions ) visa inc. ( 1 ) .'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'what is the average payment volume per transaction for american express?',
    [
        '(table): company the american express of payments volume ( billions ) is 637 ; the american express of total volume ( billions ) is 647 ; the american express of total transactions ( billions ) is 5.0 ; the american express of cards ( millions ) is 86 ;',
        '(text): largest operators of open-loop and closed-loop retail electronic payments networks the largest operators of open-loop and closed-loop retail electronic payments networks are visa , mastercard , american express , discover , jcb and diners club .',
        '(text): with the exception of discover , which primarily operates in the united states , all of the other network operators can be considered multi- national or global providers of payments network services .',
        '(text): based on payments volume , total volume , number of transactions and number of cards in circulation , visa is the largest retail electronic payments network in the world .',
        '(text): the following chart compares our network with those of our major competitors for calendar year 2007 : company payments volume volume transactions cards ( billions ) ( billions ) ( billions ) ( millions ) visa inc. ( 1 ) .',
    ]
)
# [{'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.* -->

Evaluation

Metrics

Cross Encoder Reranking

• Dataset: reranker
• Evaluated with <code>CrossEncoderRerankingEvaluator</code> with these parameters:

  {
      "at_k": 10
  }
  

<!-- ## 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: 175,555 training samples
• Columns: <code>query</code>, <code>passage</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	query	passage	label
  type	string	string	float
  details	<ul><li>min: 41 characters</li><li>mean: 89.26 characters</li><li>max: 186 characters</li></ul>	<ul><li>min: 11 characters</li><li>mean: 182.61 characters</li><li>max: 1853 characters</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.07</li><li>max: 1.0</li></ul>

• Samples:

  query	passage	label
  <code>what is the the interest expense in 2009?</code>	<code>(text): if libor changes by 100 basis points , our annual interest expense would change by $ 3.8 million .</code>	<code>1.0</code>
  <code>what is the the interest expense in 2009?</code>	<code>(text): interest rate to a variable interest rate based on the three-month libor plus 2.05% ( 2.05 % ) ( 2.34% ( 2.34 % ) as of october 31 , 2009 ) .</code>	<code>0.0</code>
  <code>what is the the interest expense in 2009?</code>	<code>(text): foreign currency exposure as more fully described in note 2i .</code>	<code>0.0</code>

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

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

Evaluation Dataset

Unnamed Dataset

• Size: 25,007 evaluation samples
• Columns: <code>query</code>, <code>passage</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	query	passage	label
  type	string	string	float
  details	<ul><li>min: 52 characters</li><li>mean: 86.04 characters</li><li>max: 137 characters</li></ul>	<ul><li>min: 11 characters</li><li>mean: 166.61 characters</li><li>max: 717 characters</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.06</li><li>max: 1.0</li></ul>

• Samples:

  query	passage	label
  <code>what is the average payment volume per transaction for american express?</code>	<code>(table): company the american express of payments volume ( billions ) is 637 ; the american express of total volume ( billions ) is 647 ; the american express of total transactions ( billions ) is 5.0 ; the american express of cards ( millions ) is 86 ;</code>	<code>1.0</code>
  <code>what is the average payment volume per transaction for american express?</code>	<code>(text): largest operators of open-loop and closed-loop retail electronic payments networks the largest operators of open-loop and closed-loop retail electronic payments networks are visa , mastercard , american express , discover , jcb and diners club .</code>	<code>0.0</code>
  <code>what is the average payment volume per transaction for american express?</code>	<code>(text): with the exception of discover , which primarily operates in the united states , all of the other network operators can be considered multi- national or global providers of payments network services .</code>	<code>0.0</code>

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

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 0.0001
• weight_decay: 0.01
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: True
• load_best_model_at_end: True

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• 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: 0.0001
• weight_decay: 0.01
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: True
• 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: 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_fused
• 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: {}

</details>

Training Logs

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

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.1.2
• Transformers: 4.57.3
• PyTorch: 2.9.0+cu126
• Accelerate: 1.12.0
• Datasets: 4.4.1
• 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",
}

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