rohit-vt/tink-reranker-phase1_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 <!-- at revision c5ee24cb16019beea0893ab7796b1df96625c6b8 -->
• —Maximum Sequence Length: 512 tokens
• —Number of Output Labels: 1 label
• —Supported Modality: Text <!-- - 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

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: work with nursing staff'],
    ['skill: manage health care staff', 'skill: manage chiropractic staff'],
]
scores = model.predict(pairs)
print(scores)
# [ 8.621   5.0407  1.0124 -1.7446  0.0759]

# 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: work with nursing staff',
        'skill: manage chiropractic staff',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 10,246 training samples
• —Columns: <code>sentenceA</code>, <code>sentenceB</code>, and <code>label</code>
• —Approximate statistics based on the first 100 samples: | | sentenceA | sentenceB | label | |:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | modality | text | text | | | details | <ul><li>min: 7 tokens</li><li>mean: 8.14 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 7.94 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.22</li><li>max: 1.0</li></ul> |
• —Samples: | sentenceA | sentenceB | label | |:--------------------------------------------|:--------------------------------------------------------|:-----------------| | <code>skill: manage security systems</code> | <code>skill: manage technical security systems</code> | <code>1.0</code> | | <code>skill: manage security systems</code> | <code>skill: Physical access management software</code> | <code>0.0</code> | | <code>skill: manage security systems</code> | <code>skill: manage theft prevention</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: 1,416 evaluation samples
• —Columns: <code>sentenceA</code>, <code>sentenceB</code>, and <code>label</code>
• —Approximate statistics based on the first 100 samples: | | sentenceA | sentenceB | label | |:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | modality | text | text | | | details | <ul><li>min: 6 tokens</li><li>mean: 9.23 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 8.06 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.21</li><li>max: 1.0</li></ul> |
• —Samples: | sentenceA | sentenceB | label | |:---------------------------------------------|:-----------------------------------------------|:-----------------| | <code>skill: manage health care staff</code> | <code>skill: manage healthcare staff</code> | <code>1.0</code> | | <code>skill: manage health care staff</code> | <code>skill: manage staff</code> | <code>0.0</code> | | <code>skill: manage health care staff</code> | <code>skill: manage physiotherapy staff</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

• —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

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

• —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: adamwtorchfused
• —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: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_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: {}

</details>

Training Logs

• —The bold row denotes the saved checkpoint.

Training Time

• —Training: 48.1 seconds
• —Evaluation: 1.0 seconds
• —Total: 49.1 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",
}

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