medimaven-reranker-bge-cross-encoder

🩺 MediMaven BGE Cross-Encoder Reranker (v1.1)

A domain-adapted BGE reranker fine-tuned on synthetic triples (query, +, –) derived from 500 k medical passage pairs. This is a Cross Encoder model finetuned from BAAI/bge-reranker-base 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: BAAI/bge-reranker-base <!-- at revision 2cfc18c9415c912f9d8155881c133215df768a70 -->
• Maximum Sequence Length: 512 tokens
• Number of Output Labels: 1 label

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.

## 🚀 Usage

from sentence_transformers import CrossEncoder
model = CrossEncoder("medimaven-ai/medimaven-reranker-bge-cross-encoder")
scores = model.predict([("what causes gerd?", "Gastro-oesophageal reflux disease (GERD) occurs...")])
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Recipe

Training Dataset

Medimaven-ai Dataset

• Size: 570,914 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	int
  details	<ul><li>min: 17 characters</li><li>mean: 98.9 characters</li><li>max: 274 characters</li></ul>	<ul><li>min: 117 characters</li><li>mean: 1647.18 characters</li><li>max: 2339 characters</li></ul>	<ul><li>0: ~47.70%</li><li>1: ~52.30%</li></ul>

• Samples:

  sentence_0	sentence_1	label
  <code>what is the life expectancy for babies with trisomy 18</code>	<code>Most babies born with this condition die within the first few days or weeks of life, as they have so many medical complications. Just 5% to 10% make it past their first year. Like trisomy 18, no one knows why some babies get this condition. It's known that the chance increases with the mother's age, though women of any age can have a child with trisomy 13. About 80% of babies with trisomy 18 or 13 are born to mothers under 35. The condition can be diagnosed before birth with the same tests used to identify trisomy 18, or after birth by a physical examination. Trisomy 18 is a condition where you have three copies of each chromosome 18 in your body's cells instead of two. This can lead to serious physical and mental disabilities. There is no cure, though treatment can include surgeries, medicines, breathing tubes, and feeding tubes. Some parents opt just for comfort care. Life expectancy is usually a year or less. How old is the oldest living person with trisomy 18? The oldest people wer...</code>	<code>1</code>
  <code>what are some reasons doctors prescribe benzodiazepines</code>	<code>But they can be habit-forming, especially if you take them regularly or for a long time. If you think you or a loved one may have a problem with benzodiazepine misuse, contact a doctor or a drug hotline. What is benzodiazepine abuse? Doctors define benzodiazepine abuse as using these drugs for non-medical reasons to get high, What is the incidence of benzodiazepine abuse? In a 12-month period between 2014 and 2015, more than 5 million people in the U.S. reported they had misused benzodiazepines. That's out of 30 million adults who used the drugs at all that year.</code>	<code>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: 96
• per_device_eval_batch_size: 96
• num_train_epochs: 1

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 96
• per_device_eval_batch_size: 96
• 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: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• 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
• use_ipex: False
• bf16: False
• 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: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• 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}
• 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
• 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: 3.11.12
• Sentence Transformers: 4.1.0
• Transformers: 4.52.2
• PyTorch: 2.6.0+cu124
• Accelerate: 1.7.0
• Datasets: 2.14.4
• Tokenizers: 0.21.1

📜 Citations

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",
}


@misc{medimaven-reranker-bge-cross-encoder,
  title        = {medimaven-reranker-bge-cross-encoder},
  author       = {Kyei-Mensah, Bernard},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/dranreb1660/medimaven-reranker-bge-cross-encoder}}
}