reranker-MiniLM-L6-H384-uncased-gooaq-bce-495000

ModernBERT-base trained on GooAQ

This is a Cross Encoder model finetuned from nreimers/MiniLM-L6-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-L6-H384-uncased <!-- at revision 3276f0fac9d818781d7a1327b3ff818fc4e643c0 -->
• Maximum Sequence Length: 512 tokens
• Number of Output Labels: 1 label

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

• Language: en
• License: apache-2.0

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("ayushexel/reranker-MiniLM-L6-H384-uncased-gooaq-bce-495000")
# Get scores for pairs of texts
pairs = [
    ["in grey's anatomy how does izzie die?", 'After speculation that Izzie would be killed off in the fifth season, the character was diagnosed with Stage 4 metastatic melanoma.'],
    ["in grey's anatomy how does izzie die?", "Izzie later admitted to George that she was in love with him, leaving him speechless. George later admitted he loved Izzie too, despite his strange reaction to her when she confessed her love to him. Their relationship was soon discovered by George's wife, Callie and the two got a divorce."],
    ["in grey's anatomy how does izzie die?", "The episode in which Derek Shepherd (Patrick Dempsey) dies is one that most Grey's Anatomy fans will never forget. The fateful incident occurred in season 11, episode 21, and it was titled, “How To Save a Life.” The attending doctor who failed to save McDreamy's life recently appeared in an episode of Grey's Anatomy."],
    ["in grey's anatomy how does izzie die?", "Richard Webber, Grey's Anatomy fans are nervous he'll die, though nothing is set in stone on the show yet. Warning: Spoilers for Season 16, Episode 19 of Grey's Anatomy follow."],
    ["in grey's anatomy how does izzie die?", "Izzie eventually forgives him, and they begin dating again until Denny enters the picture. After Denny's death they begin dating yet again and following her recovery from cancer they get married, but it doesn't last."],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    "in grey's anatomy how does izzie die?",
    [
        'After speculation that Izzie would be killed off in the fifth season, the character was diagnosed with Stage 4 metastatic melanoma.',
        "Izzie later admitted to George that she was in love with him, leaving him speechless. George later admitted he loved Izzie too, despite his strange reaction to her when she confessed her love to him. Their relationship was soon discovered by George's wife, Callie and the two got a divorce.",
        "The episode in which Derek Shepherd (Patrick Dempsey) dies is one that most Grey's Anatomy fans will never forget. The fateful incident occurred in season 11, episode 21, and it was titled, “How To Save a Life.” The attending doctor who failed to save McDreamy's life recently appeared in an episode of Grey's Anatomy.",
        "Richard Webber, Grey's Anatomy fans are nervous he'll die, though nothing is set in stone on the show yet. Warning: Spoilers for Season 16, Episode 19 of Grey's Anatomy follow.",
        "Izzie eventually forgives him, and they begin dating again until Denny enters the picture. After Denny's death they begin dating yet again and following her recovery from cancer they get married, but it doesn't last.",
    ]
)
# [{'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: gooaq-dev
• Evaluated with <code>CrossEncoderRerankingEvaluator</code> with these parameters:

  {
      "at_k": 10,
      "always_rerank_positives": false
  }
  

Cross Encoder Reranking

• Datasets: NanoMSMARCO_R100, NanoNFCorpus_R100 and NanoNQ_R100
• Evaluated with <code>CrossEncoderRerankingEvaluator</code> with these parameters:

  {
      "at_k": 10,
      "always_rerank_positives": true
  }
  

Cross Encoder Nano BEIR

• Dataset: NanoBEIR_R100_mean
• Evaluated with <code>CrossEncoderNanoBEIREvaluator</code> with these parameters:

  {
      "dataset_names": [
          "msmarco",
          "nfcorpus",
          "nq"
      ],
      "rerank_k": 100,
      "at_k": 10,
      "always_rerank_positives": true
  }
  

<!-- ## 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: 2,749,365 training samples
• Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	question	answer	label
  type	string	string	int
  details	<ul><li>min: 19 characters</li><li>mean: 42.17 characters</li><li>max: 79 characters</li></ul>	<ul><li>min: 54 characters</li><li>mean: 246.01 characters</li><li>max: 399 characters</li></ul>	<ul><li>0: ~81.90%</li><li>1: ~18.10%</li></ul>

• Samples:

  question	answer	label
  <code>in grey's anatomy how does izzie die?</code>	<code>After speculation that Izzie would be killed off in the fifth season, the character was diagnosed with Stage 4 metastatic melanoma.</code>	<code>1</code>
  <code>in grey's anatomy how does izzie die?</code>	<code>Izzie later admitted to George that she was in love with him, leaving him speechless. George later admitted he loved Izzie too, despite his strange reaction to her when she confessed her love to him. Their relationship was soon discovered by George's wife, Callie and the two got a divorce.</code>	<code>0</code>
  <code>in grey's anatomy how does izzie die?</code>	<code>The episode in which Derek Shepherd (Patrick Dempsey) dies is one that most Grey's Anatomy fans will never forget. The fateful incident occurred in season 11, episode 21, and it was titled, “How To Save a Life.” The attending doctor who failed to save McDreamy's life recently appeared in an episode of Grey's Anatomy.</code>	<code>0</code>

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

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": 5
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 256
• per_device_eval_batch_size: 256
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• seed: 12
• bf16: True
• dataloader_num_workers: 12
• 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: 256
• per_device_eval_batch_size: 256
• 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: 2e-05
• weight_decay: 0.0
• 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: 12
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: True
• fp16: False
• 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: 12
• 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}
• tp_size: 0
• 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}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• 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
• 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
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

</details>

Training Logs

Framework Versions

• Python: 3.11.0
• Sentence Transformers: 4.0.1
• Transformers: 4.50.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.5.2
• Datasets: 3.5.0
• Tokenizers: 0.21.1

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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