README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:148295
  - loss:SymmetricLoss
base_model: dmis-lab/biobert-v1.1
widget:
  - source_sentence: >-
      Complications of pregnancy; childbirth; and the puerperium → Complications
      during labor → Forceps delivery
    sentences:
      - >-
        Complications of pregnancy; childbirth; and the puerperium →
        Complications during labor
      - >-
        Complications of pregnancy; childbirth; and the puerperium → Other
        complications of birth; puerperium affecting management of mother
      - >-
        Complications of pregnancy; childbirth; and the puerperium → Normal
        pregnancy and/or delivery → Other pregnancy and delivery including
        normal
  - source_sentence: >-
      Complications of pregnancy; childbirth; and the puerperium → Complications
      mainly related to pregnancy → Early or threatened labor
    sentences:
      - >-
        Complications of pregnancy; childbirth; and the puerperium →
        Complications mainly related to pregnancy
      - >-
        Complications of pregnancy; childbirth; and the puerperium →
        Abortion-related disorders → Postabortion complications
      - >-
        Complications of pregnancy; childbirth; and the puerperium → Indications
        for care in pregnancy; labor; and delivery
  - source_sentence: >-
      Diseases of the respiratory system → Respiratory infections → Acute
      bronchitis
    sentences:
      - Diseases of the respiratory system → Asthma → Asthma
      - Diseases of the respiratory system → Lung disease due to external agents
      - Diseases of the respiratory system → Respiratory infections
  - source_sentence: >-
      Diseases of the circulatory system → Diseases of the heart → Cardiac
      arrest and ventricular fibrillation
    sentences:
      - >-
        Diseases of the circulatory system → Hypertension → Essential
        hypertension
      - Diseases of the circulatory system → Cerebrovascular disease
      - Diseases of the circulatory system → Diseases of the heart
  - source_sentence: Infectious and parasitic diseases → Mycoses
    sentences:
      - >-
        Diseases of the skin and subcutaneous tissue → Skin and subcutaneous
        tissue infections
      - Mental illness
      - Infectious and parasitic diseases
pipeline_tag: sentence-similarity
library_name: sentence-transformers

HierarchyTransformer based on dmis-lab/biobert-v1.1

This is a sentence-transformers model finetuned from dmis-lab/biobert-v1.1. It maps sentences & paragraphs to a 768-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
• Base model: dmis-lab/biobert-v1.1
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 768 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

HierarchyTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Infectious and parasitic diseases → Mycoses',
    'Infectious and parasitic diseases',
    'Mental illness',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6610, 0.3361],
#         [0.6610, 1.0000, 0.2730],
#         [0.3361, 0.2730, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

• Size: 148,295 training samples
• Columns: child, parent, parent_negative, and child_negative
• Approximate statistics based on the first 1000 samples:

  	child	parent	parent_negative	child_negative
  type	string	string	string	string
  details	min: 8 tokens mean: 25.19 tokens max: 65 tokens	min: 4 tokens mean: 16.22 tokens max: 41 tokens	min: 4 tokens mean: 16.94 tokens max: 34 tokens	min: 11 tokens mean: 23.48 tokens max: 65 tokens

• Samples:

  child	parent	parent_negative	child_negative
  Infectious and parasitic diseases → Bacterial infection	Infectious and parasitic diseases	Mental illness	Diseases of the nervous system and sense organs → Central nervous system infection
  Infectious and parasitic diseases → Bacterial infection	Infectious and parasitic diseases	Mental illness	Diseases of the digestive system → Intestinal infection
  Infectious and parasitic diseases → Bacterial infection	Infectious and parasitic diseases	Mental illness	Diseases of the skin and subcutaneous tissue → Skin and subcutaneous tissue infections

• Loss: hierarchy_transformers.losses.symmetric_loss.SymmetricLoss with these parameters:

  {
      "distance_metric": "PoincareBall(c=0.0013021096820011735).dist and dist0",
      "HyperbolicChildTriplet": {
          "weight": 1.0,
          "distance_metric": "PoincareBall(c=0.0013021096820011735).dist",
          "margin": 3.0
      },
      "HyperbolicParentTriplet": {
          "weight": 1.0,
          "distance_metric": "PoincareBall(c=0.0013021096820011735).dist",
          "margin": 3.0
      }
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 512
• learning_rate: 1e-05
• num_train_epochs: 10
• warmup_steps: 500
• load_best_model_at_end: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 512
• 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: 1e-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: 10
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 500
• 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
• 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: 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}
• parallelism_config: 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
• project: huggingface
• trackio_space_id: trackio
• 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: 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: no
• 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: True
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0863	100	2.1613
0.1726	200	0.5936
0.2588	300	0.1998
0.3451	400	0.1107
0.4314	500	0.0567
0.5177	600	0.0452
0.6040	700	0.032
0.6903	800	0.0279
0.7765	900	0.0218
0.8628	1000	0.0235
0.9491	1100	0.018
1.0	1159	-
1.0354	1200	0.0192
1.1217	1300	0.0176
1.2079	1400	0.0137
1.2942	1500	0.0119
1.3805	1600	0.0139
1.4668	1700	0.0138
1.5531	1800	0.0123
1.6393	1900	0.0104
1.7256	2000	0.0117
1.8119	2100	0.0097
1.8982	2200	0.0133
1.9845	2300	0.01
2.0	2318	-
2.0708	2400	0.0109
2.1570	2500	0.0074
2.2433	2600	0.0072
2.3296	2700	0.015
2.4159	2800	0.0069
2.5022	2900	0.0107
2.5884	3000	0.0094
2.6747	3100	0.0105
2.7610	3200	0.0095
2.8473	3300	0.0072
2.9336	3400	0.0084
3.0	3477	-
3.0198	3500	0.0104
3.1061	3600	0.0078
3.1924	3700	0.008
3.2787	3800	0.0086
3.3650	3900	0.0085
3.4513	4000	0.0081
3.5375	4100	0.0093
3.6238	4200	0.0107
3.7101	4300	0.008
3.7964	4400	0.0099
3.8827	4500	0.0058
3.9689	4600	0.0084
4.0	4636	-
4.0552	4700	0.01
4.1415	4800	0.0053
4.2278	4900	0.0075
4.3141	5000	0.0077
4.4003	5100	0.0065
4.4866	5200	0.0089
4.5729	5300	0.0082
4.6592	5400	0.0093
4.7455	5500	0.0076
4.8318	5600	0.0095
4.9180	5700	0.0078
5.0	5795	-
5.0043	5800	0.0055
5.0906	5900	0.0061
5.1769	6000	0.005
5.2632	6100	0.0075
5.3494	6200	0.0079
5.4357	6300	0.006
5.5220	6400	0.0095
5.6083	6500	0.0099
5.6946	6600	0.0084
5.7808	6700	0.008
5.8671	6800	0.0064
5.9534	6900	0.0097
6.0	6954	-
6.0397	7000	0.0063
6.1260	7100	0.0069
6.2123	7200	0.0095
6.2985	7300	0.0067
6.3848	7400	0.0056
6.4711	7500	0.0074
6.5574	7600	0.0086
6.6437	7700	0.0072
6.7299	7800	0.0065
6.8162	7900	0.0052
6.9025	8000	0.0101
6.9888	8100	0.0086
7.0	8113	-
7.0751	8200	0.0065
7.1613	8300	0.0106
7.2476	8400	0.0049
7.3339	8500	0.0074
7.4202	8600	0.0065
7.5065	8700	0.004
7.5928	8800	0.0075
7.6790	8900	0.009
7.7653	9000	0.0059
7.8516	9100	0.0063
7.9379	9200	0.0095
8.0	9272	-
8.0242	9300	0.0082
8.1104	9400	0.0067
8.1967	9500	0.0063
8.2830	9600	0.0071
8.3693	9700	0.0064
8.4556	9800	0.0072
8.5418	9900	0.0059
8.6281	10000	0.0085
8.7144	10100	0.0083
8.8007	10200	0.0046
8.8870	10300	0.0055
8.9733	10400	0.008
9.0	10431	-
9.0595	10500	0.0066
9.1458	10600	0.0068
9.2321	10700	0.0093
9.3184	10800	0.0067
9.4047	10900	0.0054
9.4909	11000	0.0079
9.5772	11100	0.0052
9.6635	11200	0.0073
9.7498	11300	0.0088
9.8361	11400	0.005
9.9223	11500	0.0069
10.0	11590	-

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.13
• Sentence Transformers: 5.1.2
• Transformers: 4.57.1
• PyTorch: 2.9.0+cu128
• Accelerate: 1.11.0
• Datasets: 4.3.0
• Tokenizers: 0.22.1

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",
}

SymmetricLoss

@article{he2024language,
  title={Language models as hierarchy encoders},
  author={He, Yuan and Yuan, Zhangdie and Chen, Jiaoyan and Horrocks, Ian},
  journal={arXiv preprint arXiv:2401.11374},
  year={2024}
}