Add new SentenceTransformer model

1_Pooling/config.json

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+{
+    "embedding_dimension": 768,
+    "pooling_mode": "mean",
+    "include_prompt": true
+}

README.md

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+---
+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},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.18.0",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.11.0+cu130",
+    "sentence_transformers": "5.7.0",
+    "transformers": "5.18.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f2675925d9af7a40c748681dfe906c36bc04aa5b85c9f5c8428006b78367f3e2
+size 437967672

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "is_local": true,
+  "local_files_only": false,
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 32,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}