---
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]])
```
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## 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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