| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:852480 |
| - loss:MultipleNegativesRankingLoss |
| base_model: sentence-transformers/all-mpnet-base-v2 |
| widget: |
| - source_sentence: anal ectopia |
| sentences: |
| - Acute Chagas disease without heart involvement |
| - Ectopic anus |
| - chronic inactive rheumatic heart disease |
| - source_sentence: lactose intolerance |
| sentences: |
| - infectious granulomas of the central nervous system, site unspecified |
| - lm - [lactose malabsorption] |
| - monoblastic leukaemia |
| - source_sentence: syndromes with eye anomalies as a major feature |
| sentences: |
| - Syndromes with eye anomalies as a major feature |
| - abnormality of nail surface |
| - dermatomyositis |
| - source_sentence: dibothriocephalus latus |
| sentences: |
| - dibothriocephaliasis |
| - postprocedural pelvic peritoneal adhesions |
| - saccadic pulse-step mismatch |
| - source_sentence: indeterminate cell histiocytosis |
| sentences: |
| - other specified progressive focal atrophies |
| - fetal nephrocalcinosis |
| - Indeterminate cell histiocytosis |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision e8c3b32edf5434bc2275fc9bab85f82640a19130 --> |
| - **Maximum Sequence Length:** 32 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'MPNetModel'}) |
| (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True}) |
| (2): Normalize({}) |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("seanfarrell/icd11_synonym_model") |
| # Run inference |
| sentences = [ |
| 'indeterminate cell histiocytosis', |
| 'Indeterminate cell histiocytosis', |
| 'fetal nephrocalcinosis', |
| ] |
| 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, 1.0000, 0.0856], |
| # [1.0000, 1.0000, 0.0856], |
| # [0.0856, 0.0856, 1.0000]]) |
| ``` |
| <!-- |
| ### 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.* |
| --> |
|
|
| <!-- |
| ## 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: 852,480 training samples |
| * Columns: <code>anchor</code> and <code>positive</code> |
| * Approximate statistics based on the first 100 samples: |
| | | anchor | positive | |
| |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | |
| | modality | text | text | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 12.02 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.19 tokens</li><li>max: 29 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | |
| |:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------| |
| | <code>fracture, dislocation or subluxation of spine due to birth injury</code> | <code>Fracture, dislocation or subluxation of spine due to birth injury</code> | |
| | <code>malignant neoplasms of bladder</code> | <code>Malignant neoplasms of bladder</code> | |
| | <code>Toxic shock syndrome due to other specified infectious agent</code> | <code>toxic shock syndrome due to other specified infectious agent</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 512 |
| - `num_train_epochs`: 1 |
| - `learning_rate`: 2e-05 |
| - `lr_scheduler_type`: cosine |
| - `warmup_steps`: 166 |
| - `weight_decay`: 0.01 |
| - `bf16`: True |
| - `per_device_eval_batch_size`: 256 |
| - `dataloader_drop_last`: True |
| - `dataloader_num_workers`: 4 |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `per_device_train_batch_size`: 512 |
| - `num_train_epochs`: 1 |
| - `max_steps`: -1 |
| - `learning_rate`: 2e-05 |
| - `lr_scheduler_type`: cosine |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 166 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `weight_decay`: 0.01 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 1 |
| - `average_tokens_across_devices`: True |
| - `max_grad_norm`: 1.0 |
| - `label_smoothing_factor`: 0.0 |
| - `bf16`: True |
| - `fp16`: False |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `use_cache`: False |
| - `neftune_noise_alpha`: None |
| - `torch_empty_cache_steps`: None |
| - `auto_find_batch_size`: False |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `include_num_input_tokens_seen`: no |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `disable_tqdm`: False |
| - `project`: huggingface |
| - `trackio_space_id`: None |
| - `trackio_bucket_id`: None |
| - `trackio_static_space_id`: None |
| - `per_device_eval_batch_size`: 256 |
| - `prediction_loss_only`: True |
| - `eval_on_start`: False |
| - `eval_do_concat_batches`: True |
| - `eval_use_gather_object`: False |
| - `eval_accumulation_steps`: None |
| - `include_for_metrics`: [] |
| - `batch_eval_metrics`: False |
| - `save_only_model`: False |
| - `save_on_each_node`: False |
| - `enable_jit_checkpoint`: False |
| - `push_to_hub`: False |
| - `hub_private_repo`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `full_determinism`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `use_cpu`: False |
| - `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 |
| - `dataloader_drop_last`: True |
| - `dataloader_num_workers`: 4 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `dataloader_multiprocessing_context`: None |
| - `dataloader_in_order`: True |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `train_sampling_strategy`: random |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `ddp_static_graph`: None |
| - `ddp_backend`: None |
| - `ddp_timeout`: 1800 |
| - `fsdp`: None |
| - `fsdp_config`: None |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| - `warmup_ratio`: None |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | |
| |:------:|:----:|:-------------:| |
| | 0.0300 | 50 | 0.8889 | |
| | 0.0601 | 100 | 0.7854 | |
| | 0.0901 | 150 | 0.7348 | |
| | 0.1201 | 200 | 0.6554 | |
| | 0.1502 | 250 | 0.6254 | |
| | 0.1802 | 300 | 0.5845 | |
| | 0.2102 | 350 | 0.5857 | |
| | 0.2402 | 400 | 0.5496 | |
| | 0.2703 | 450 | 0.5358 | |
| | 0.3003 | 500 | 0.5186 | |
| | 0.3303 | 550 | 0.5068 | |
| | 0.3604 | 600 | 0.4737 | |
| | 0.3904 | 650 | 0.4635 | |
| | 0.4204 | 700 | 0.4621 | |
| | 0.4505 | 750 | 0.4498 | |
| | 0.4805 | 800 | 0.4508 | |
| | 0.5105 | 850 | 0.4135 | |
| | 0.5405 | 900 | 0.4298 | |
| | 0.5706 | 950 | 0.4096 | |
| | 0.6006 | 1000 | 0.3973 | |
| | 0.6306 | 1050 | 0.4013 | |
| | 0.6607 | 1100 | 0.3847 | |
| | 0.6907 | 1150 | 0.3742 | |
| | 0.7207 | 1200 | 0.3726 | |
| | 0.7508 | 1250 | 0.3738 | |
| | 0.7808 | 1300 | 0.3742 | |
| | 0.8108 | 1350 | 0.3786 | |
| | 0.8408 | 1400 | 0.3710 | |
| | 0.8709 | 1450 | 0.3806 | |
| | 0.9009 | 1500 | 0.3719 | |
| | 0.9309 | 1550 | 0.3721 | |
| | 0.9610 | 1600 | 0.3482 | |
| | 0.9910 | 1650 | 0.3703 | |
|
|
|
|
| ### Training Time |
| - **Training**: 3.2 minutes |
|
|
| ### Framework Versions |
| - Python: 3.13.15 |
| - Sentence Transformers: 5.7.0 |
| - Transformers: 5.18.0 |
| - PyTorch: 2.11.0+cu130 |
| - Accelerate: 1.15.0 |
| - Datasets: 4.8.5 |
| - Tokenizers: 0.23.2 |
|
|
| ## Additional Resources |
|
|
| - [Training and Finetuning Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sentence-transformers): the end-to-end guide for training or finetuning Sentence Transformer models. |
| - [Introduction to Matryoshka Embedding Models](https://huggingface.co/blog/matryoshka): variable-size embeddings that can be truncated with minimal quality loss. |
| - [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization): post-training compression of embedding vectors. |
| - [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video models through the same API. |
| - [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): train multimodal embedding models, with a Visual Document Retrieval walkthrough. |
|
|
| ## Citation |
|
|
| ### BibTeX |
|
|
| #### Sentence Transformers |
| ```bibtex |
| @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", |
| } |
| ``` |
|
|
| #### MultipleNegativesRankingLoss |
| ```bibtex |
| @misc{oord2019representationlearningcontrastivepredictive, |
| title={Representation Learning with Contrastive Predictive Coding}, |
| author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, |
| year={2019}, |
| eprint={1807.03748}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/1807.03748}, |
| } |
| ``` |
|
|
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| ## Glossary |
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| *Clearly define terms in order to be accessible across audiences.* |
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| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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