pankajrajdeo/BioForge-bioformer-16L-umls-integration

SentenceTransformer

This is a sentence-transformers model trained. 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: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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("pankajrajdeo/BioForge-bioformer-16L-umls-integration")
# Run inference
sentences = [
    'Congenital fibrinogen abnormality',
    'Congenital disease',
    'An application of magnetic resonance imaging that uses spin refocusing and spin echo generation, resulting in shorter repetition times and faster imaging.',
]
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: umls_sota_eval
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9579
cosine_accuracy@3	0.9792
cosine_accuracy@5	0.9829
cosine_accuracy@10	0.9886
cosine_precision@1	0.9579
cosine_precision@3	0.5356
cosine_precision@5	0.3658
cosine_precision@10	0.206
cosine_recall@1	0.6671
cosine_recall@3	0.8833
cosine_recall@5	0.9236
cosine_recall@10	0.9553
cosine_ndcg@10	0.9525
cosine_mrr@10	0.9692
cosine_map@100	0.9383

Training Details

Training Dataset

Unnamed Dataset

• Size: 2,945,832 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 3 tokens mean: 12.11 tokens max: 62 tokens	min: 3 tokens mean: 39.97 tokens max: 256 tokens

• Samples:

  anchor	positive
  Cranial nerve structure	Cranial neuropathy due to petrous infection
  Phenylalanine racemase (ATP-hydrolysing)	Phenylalanine racemase (adenosine triphosphate-hydrolysing) (substance)
  Denibulin Hydrochloride	The hydrochloride salt of denibulin, a small molecular vascular disrupting agent, with potential antimitotic and antineoplastic activities. Denibulin selectively targets and reversibly binds to the colchicine-binding site on tubulin and inhibits microtubule assembly. This results in the disruption of the cytoskeleton of tumor endothelial cells, ultimately leading to cell cycle arrest, blockage of cell division and apoptosis. This causes inadequate blood flow to the tumor and eventually leads to a decrease in tumor cell proliferation., a small molecule vascular disrupting agent (VDA), with potential antimitotic and antineoplastic activity. Denibulin selectively targets and reversibly binds to the colchicine-binding site on tubulin and inhibits microtubule assembly. This results in the disruption of the cytoskeleton of tumor endothelial cells (EC), ultimately leading to cell cycle arrest, blockage of cell division and apoptosis. This causes inadequate blood flow to the tumor and eventual...

• Loss: main.MultipleNegativesSymmetricMarginLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 512
• gradient_accumulation_steps: 4
• learning_rate: 1.5e-05
• num_train_epochs: 4
• lr_scheduler_type: cosine
• warmup_ratio: 0.05
• bf16: True
• dataloader_num_workers: 16
• load_best_model_at_end: True
• gradient_checkpointing: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 512
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 4
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 1.5e-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: 0.05
• 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: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 16
• 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
• 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
• hub_revision: None
• gradient_checkpointing: True
• 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
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	umls_sota_eval_cosine_ndcg@10
0.0695	100	0.8266	-
0.1390	200	0.5384	-
0.2086	300	0.4742	-
0.2781	400	0.4355	-
0.3295	474	-	0.9295
0.3476	500	0.4137	-
0.4171	600	0.3961	-
0.4866	700	0.3817	-
0.5561	800	0.3739	-
0.6257	900	0.3564	-
0.6590	948	-	0.9384
0.6952	1000	0.3587	-
0.7647	1100	0.3525	-
0.8342	1200	0.3463	-
0.9037	1300	0.3395	-
0.9732	1400	0.3329	-
0.9885	1422	-	0.9434
1.0424	1500	0.3228	-
1.1119	1600	0.318	-
1.1814	1700	0.3141	-
1.2510	1800	0.3101	-
1.3177	1896	-	0.9463
1.3205	1900	0.3134	-
1.3900	2000	0.3097	-
1.4595	2100	0.3006	-
1.5290	2200	0.303	-
1.5985	2300	0.3003	-
1.6472	2370	-	0.9484
1.6681	2400	0.2949	-
1.7376	2500	0.2951	-
1.8071	2600	0.2939	-
1.8766	2700	0.2908	-
1.9461	2800	0.2912	-
1.9767	2844	-	0.9502
2.0153	2900	0.2869	-
2.0848	3000	0.2807	-
2.1543	3100	0.2771	-
2.2238	3200	0.2795	-
2.2934	3300	0.2756	-
2.3059	3318	-	0.9510
2.3629	3400	0.2758	-
2.4324	3500	0.2765	-
2.5019	3600	0.2752	-
2.5714	3700	0.2745	-
2.6354	3792	-	0.9515
2.6409	3800	0.2714	-
2.7105	3900	0.2732	-
2.7800	4000	0.2735	-
2.8495	4100	0.2722	-
2.9190	4200	0.2713	-
2.9649	4266	-	0.9520
2.9885	4300	0.2721	-
3.0577	4400	0.2662	-
3.1272	4500	0.2654	-
3.1967	4600	0.2683	-
3.2662	4700	0.2687	-
3.2941	4740	-	0.9523
3.3358	4800	0.2665	-
3.4053	4900	0.2686	-
3.4748	5000	0.2612	-
3.5443	5100	0.263	-
3.6138	5200	0.264	-
3.6236	5214	-	0.9523
3.6834	5300	0.2672	-
3.7529	5400	0.2674	-
3.8224	5500	0.2631	-
3.8919	5600	0.2631	-
3.9531	5688	-	0.9525
3.9614	5700	0.2642	-

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.53.2
• PyTorch: 2.6.0+cu124
• Accelerate: 1.5.2
• Datasets: 3.2.0
• Tokenizers: 0.21.0

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",
}