potsu-potsu/medembed-small-biomedical-matryoshka-bt384

MedEmbed Biomedical MRL

This is a sentence-transformers model trained on the json dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • json
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("potsu-potsu/medembed-small-biomedical-matryoshka-bt348")
# Run inference
sentences = [
    'What are the effects of the deletion of all three Pcdh clusters (tricluster deletion) in mice?',
    'Multicluster Pcdh diversity is required for mouse olfactory neural circuit assembly. The vertebrate clustered protocadherin (Pcdh) cell surface proteins are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ, and Pcdhγ). Although deletion of individual Pcdh clusters had subtle phenotypic consequences, the loss of all three clusters (tricluster deletion) led to a severe axonal arborization defect and loss of self-avoidance.',
    'Investigators proposed that there have been three extended periods in the evolution of gene regulatory elements. Early vertebrate evolution was characterized by regulatory gains near transcription factors and developmental genes, but this trend was replaced by innovations near extracellular signaling genes, and then innovations near posttranslational protein modifiers.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

• Dataset: dim_384
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 384
  }
  

Metric	Value
cosine_accuracy@10	0.9165
cosine_accuracy@15	0.9264
cosine_accuracy@20	0.9293
cosine_accuracy@25	0.9378
cosine_accuracy@30	0.942
cosine_precision@10	0.3917
cosine_precision@15	0.3233
cosine_precision@20	0.2707
cosine_precision@25	0.2343
cosine_precision@30	0.2052
cosine_recall@10	0.5944
cosine_recall@15	0.6621
cosine_recall@20	0.7
cosine_recall@25	0.7301
cosine_recall@30	0.7514
cosine_ndcg@10	0.6811
cosine_ndcg@20	0.6897
cosine_ndcg@30	0.7021
cosine_mrr@10	0.8049
cosine_mrr@20	0.8058
cosine_mrr@30	0.8064
cosine_map@100	0.6034

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 256
  }
  

Metric	Value
cosine_accuracy@10	0.9081
cosine_accuracy@15	0.925
cosine_accuracy@20	0.9307
cosine_accuracy@25	0.9364
cosine_accuracy@30	0.9392
cosine_precision@10	0.3854
cosine_precision@15	0.3187
cosine_precision@20	0.2674
cosine_precision@25	0.2307
cosine_precision@30	0.2014
cosine_recall@10	0.5818
cosine_recall@15	0.6538
cosine_recall@20	0.6925
cosine_recall@25	0.7205
cosine_recall@30	0.737
cosine_ndcg@10	0.6709
cosine_ndcg@20	0.6814
cosine_ndcg@30	0.6916
cosine_mrr@10	0.7984
cosine_mrr@20	0.8001
cosine_mrr@30	0.8004
cosine_map@100	0.5932

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 128
  }
  

Metric	Value
cosine_accuracy@10	0.8953
cosine_accuracy@15	0.9095
cosine_accuracy@20	0.9165
cosine_accuracy@25	0.9208
cosine_accuracy@30	0.9236
cosine_precision@10	0.3653
cosine_precision@15	0.3012
cosine_precision@20	0.2527
cosine_precision@25	0.2165
cosine_precision@30	0.1897
cosine_recall@10	0.5519
cosine_recall@15	0.6176
cosine_recall@20	0.6549
cosine_recall@25	0.6747
cosine_recall@30	0.6924
cosine_ndcg@10	0.6398
cosine_ndcg@20	0.6474
cosine_ndcg@30	0.655
cosine_mrr@10	0.7772
cosine_mrr@20	0.7787
cosine_mrr@30	0.779
cosine_map@100	0.5546

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 64
  }
  

Metric	Value
cosine_accuracy@10	0.843
cosine_accuracy@15	0.8614
cosine_accuracy@20	0.8713
cosine_accuracy@25	0.8769
cosine_accuracy@30	0.8854
cosine_precision@10	0.3263
cosine_precision@15	0.2661
cosine_precision@20	0.2246
cosine_precision@25	0.1919
cosine_precision@30	0.168
cosine_recall@10	0.4805
cosine_recall@15	0.5398
cosine_recall@20	0.5761
cosine_recall@25	0.5975
cosine_recall@30	0.6173
cosine_ndcg@10	0.5706
cosine_ndcg@20	0.5742
cosine_ndcg@30	0.5816
cosine_mrr@10	0.7214
cosine_mrr@20	0.7233
cosine_mrr@30	0.7239
cosine_map@100	0.4781

Training Details

Training Dataset

json

• Dataset: json
• Size: 4,012 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 5 tokens mean: 16.13 tokens max: 49 tokens	min: 3 tokens mean: 63.38 tokens max: 485 tokens

• Samples:

  anchor	positive
  What is the implication of histone lysine methylation in medulloblastoma?	Aberrant patterns of H3K4, H3K9, and H3K27 histone lysine methylation were shown to result in histone code alterations, which induce changes in gene expression, and affect the proliferation rate of cells in medulloblastoma.
  What is the role of STAG1/STAG2 proteins in differentiation?	STAG1/STAG2 proteins are tumour suppressor proteins that suppress cell proliferation and are essential for differentiation.
  What is the association between cell phone use and glioblastoma?	The association between cell phone use and incident glioblastoma remains unclear. Some studies have reported that cell phone use was associated with incident glioblastoma, and with reduced survival of patients diagnosed with glioblastoma. However, other studies have repeatedly replicated to find an association between cell phone use and glioblastoma.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 16
• learning_rate: 2e-05
• num_train_epochs: 4
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• tf32: True
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 16
• 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: 4
• max_steps: -1
• lr_scheduler_type: cosine
• 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
• use_ipex: False
• bf16: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: True
• 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}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• 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: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_384_cosine_ndcg@30	dim_256_cosine_ndcg@30	dim_128_cosine_ndcg@30	dim_64_cosine_ndcg@30
1.0	8	-	0.7108	0.6997	0.6564	0.5563
1.2540	10	30.3997	-	-	-	-
2.0	16	-	0.7042	0.6958	0.6592	0.5772
2.5079	20	14.8119	-	-	-	-
3.0	24	-	0.7022	0.6922	0.6551	0.5810
3.7619	30	12.4056	-	-	-	-
4.0	32	-	0.7021	0.6916	0.6550	0.5816

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.6
• Sentence Transformers: 4.1.0
• Transformers: 4.52.4
• PyTorch: 2.6.0+cu124
• Accelerate: 1.7.0
• Datasets: 3.6.0
• 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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}