rohit-vt/tink-reranker-minilm

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
• Maximum Sequence Length: 512 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': 'BertForSequenceClassification'})
)

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 = [
    ['skill: manage health care staff', 'skill: manage healthcare staff'],
    ['skill: manage health care staff', 'skill: manage staff'],
    ['skill: manage health care staff', 'skill: manage physiotherapy staff'],
    ['skill: manage health care staff', 'skill: manage a multidisciplinary team involved in patient care'],
    ['skill: manage health care staff', 'skill: work with nursing staff'],
]
scores = model.predict(pairs)
print(scores)
# [ 4.398  -1.7689 -3.7867 -3.7799 -4.4436]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'skill: manage health care staff',
    [
        'skill: manage healthcare staff',
        'skill: manage staff',
        'skill: manage physiotherapy staff',
        'skill: manage a multidisciplinary team involved in patient care',
        'skill: work with nursing staff',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

Unnamed Dataset

• Size: 11,010 training samples
• Columns: sentence_A, sentence_B, and label
• Approximate statistics based on the first 100 samples:

  	sentence_A	sentence_B	label
  type	string	string	float
  modality	text	text
  details	min: 7 tokens mean: 8.15 tokens max: 11 tokens	min: 6 tokens mean: 7.49 tokens max: 9 tokens	min: 0.0 mean: 0.2 max: 1.0

• Samples:

  sentence_A	sentence_B	label
  skill: manage security systems	skill: manage technical security systems	1.0
  skill: manage security systems	skill: manage system security	0.0
  skill: manage security systems	skill: manage alarm system	0.0

• Loss: BinaryCrossEntropyLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• Size: 1,505 evaluation samples
• Columns: sentence_A, sentence_B, and label
• Approximate statistics based on the first 100 samples:

  	sentence_A	sentence_B	label
  type	string	string	float
  modality	text	text
  details	min: 6 tokens mean: 9.26 tokens max: 14 tokens	min: 6 tokens mean: 8.34 tokens max: 16 tokens	min: 0.0 mean: 0.2 max: 1.0

• Samples:

  sentence_A	sentence_B	label
  skill: manage health care staff	skill: manage healthcare staff	1.0
  skill: manage health care staff	skill: manage staff	0.0
  skill: manage health care staff	skill: manage physiotherapy staff	0.0

• Loss: BinaryCrossEntropyLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 32
• num_train_epochs: 2
• learning_rate: 2e-05
• warmup_steps: 0.1
• per_device_eval_batch_size: 64
• load_best_model_at_end: True

All Hyperparameters

Click to expand

• per_device_train_batch_size: 32
• num_train_epochs: 2
• max_steps: -1
• learning_rate: 2e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0.1
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 1
• average_tokens_across_devices: True
• max_grad_norm: 1.0
• label_smoothing_factor: 0.0
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: None
• trackio_bucket_id: None
• trackio_static_space_id: None
• per_device_eval_batch_size: 64
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: True
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• 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
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_static_graph: None
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.0580	20	0.9453	-
0.1159	40	0.5269	-
0.1739	60	0.4222	-
0.2319	80	0.3795	-
0.2899	100	0.3090	-
0.3478	120	0.3580	-
0.4058	140	0.3126	-
0.4638	160	0.2220	-
0.5217	180	0.2884	-
0.5797	200	0.2795	-
0.6377	220	0.2328	-
0.6957	240	0.2573	-
0.7536	260	0.2493	-
0.8116	280	0.2100	-
0.8696	300	0.2548	-
0.9275	320	0.2212	-
0.9855	340	0.2308	-
1.0	345	-	0.257
1.0435	360	0.2258	-
1.1014	380	0.1895	-
1.1594	400	0.1689	-
1.2174	420	0.2283	-
1.2754	440	0.2253	-
1.3333	460	0.2040	-
1.3913	480	0.1850	-
1.4493	500	0.1932	-
1.5072	520	0.1920	-
1.5652	540	0.1964	-
1.6232	560	0.1571	-
1.6812	580	0.2132	-
1.7391	600	0.1823	-
1.7971	620	0.1950	-
1.8551	640	0.1787	-
1.9130	660	0.2028	-
1.9710	680	0.1840	-
2.0	690	-	0.2671

• The bold row denotes the saved checkpoint.

Training Time

• Training: 55.0 seconds
• Evaluation: 1.1 seconds
• Total: 56.1 seconds

Framework Versions

• Python: 3.13.13
• Sentence Transformers: 5.5.0
• Transformers: 5.8.1
• PyTorch: 2.12.0
• 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",
}