| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:3067 |
| - loss:MultipleNegativesRankingLoss |
| base_model: sentence-transformers/all-mpnet-base-v2 |
| widget: |
| - source_sentence: 'um according to dosing instructions. If no magnesium level has |
| been recorded in the last 24 hours, don''t order anything. |
| |
| model_prediction_summary: {"finish_result": "[\"The patient S6537563''s last serum |
| magnesium level within the last 24 hours was 2.1 mg/dL (recorded at 2023-11-13T04:24:00+00:00). |
| Since this value is within the normal range (>= 1.9 mg/dL) and not low according |
| to the provided dosing instructions, no replacement IV magnesium was ordered.\"]", |
| "status": "completed"}' |
| sentences: |
| - 'model correctly identified the age of the patient with MRN S6542579 as 28 based |
| on the provided context and patient data |
| |
| Although the official reference solution pass flag was false (likely due to an |
| internal evaluation error or missing expected output in the prompt metadata), |
| the model''s answer is factually correct according to the standard dataset associated |
| with this MRN and the' |
| - 'he rate such as \"h\" ", "type": "string"}, "value": {"type": "number"}}, "type": |
| "object"}}, "type": "object"}, "type": |
| |
| "array"}, "route": {"properties": {"text": {"description": "The medication route.", |
| "type": "string"}}, "type": "object"}}, "type": "object"}, "type": "array"}, "intent": |
| {"description": "Use \"order\" ", "type": "string"}, "medicationCodeableConcept": |
| {"properties": {"coding": {"items": {"properties": {"code": {"description": "The |
| actual code", "type": "string"}, "display": {"description": "Display name", "type": |
| "string"}, "system": {"description": "Coding system such as \"http://hl7.org/fhir/sid/ndc\" |
| ", "type": "string"}}, "type": "object"}, "type": "array"}, "text": {"description": |
| "The order display name of the medication, otherwise the record name.", "type": |
| "string"}}, "type": "object"}, "resourceType": {"description": "Use \"MedicationRequest\" |
| for medication requests.", "type": "string"}, "status": {"description": "The status |
| of the medication request' |
| - 'um according to dosing instructions. If no magnesium level has been recorded |
| in the last 24 hours, don''t order anything. |
| |
| model_prediction_summary: {"finish_result": "[\"The patient S6537563''s last serum |
| magnesium level within the last 24 hours was 2.1 mg/dL (recorded at 2023-11-13T04:24:00+00:00). |
| Since this value is within the normal range (>= 1.9 mg/dL) and not low according |
| to the provided dosing instructions, no replacement IV magnesium was ordered.\"]", |
| "status": "completed"}' |
| - source_sentence: 'er that assists transcription by interacting with regulatory proteins. |
| |
| |
| A. Enhancer |
| |
| B. Repressor |
| |
| C. Operator |
| |
| D. Promoter |
| |
| model_prediction_summary: {"answer": "A", "raw": "{\"answer\":\"A\",\"confidence\":0.9}", |
| "metadata": {"usage": {"completion_tokens": 11, "prompt_tokens": 70, "total_tokens": |
| 81, "completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": |
| 0, "reasoning_tokens": 0, "rejected_prediction_tokens": null, "image_tokens": |
| 0}, "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0, "cache_write_tokens": |
| 0, "video_tokens": 0}, "cost": 6.732e-05, "is_byok": false, "cost_details": {"upstream_inference_cost": |
| 6.8e-05, "upstream_inference_prompt_cost": 3.5e-05, "upstream_inference_completions_cost": |
| 3.3e-05}}}} |
| |
| judge_or_scorer_feedback: {"confidence": 0.9} |
| |
| traversal_excerpt: system: You are a medical expert. Return ONLY valid JSON in |
| this exact format: {"answer":"A","confidence":0.0}. The answer must be A, B, C, |
| or D.' |
| sentences: |
| - 'tals) The FHIR Observation.Create (Vitals) resource can file to all non-duplicable |
| flowsheet rows, including vital signs |
| |
| This resource can file vital signs for all flowsheets.", "name": "POST {api_base}/Observation", |
| "parameters": {"properties": {"category": {"items": {"properties": {"coding": |
| {"items": {"properties": {"code": {"description": "Use \"vital-signs\" ", "type": |
| "string"}, "display": {"description": "Use \"Vital Signs\" ", "type": "string"}, |
| "system": {"description": "Use \"http://hl7.org/fhir/observation-category\" ", |
| "type": "string"}}, "type": "object"}, "type": "array"}}, "type": "object"}, "type": |
| "array"}, "code": {"properties": {"text": {"description": "The flowsheet ID, encoded |
| flowsheet ID, or LOINC codes to flowsheet mapping' |
| - 'er that assists transcription by interacting with regulatory proteins. |
| |
| |
| A. Enhancer |
| |
| B. Repressor |
| |
| C. Operator |
| |
| D. Promoter |
| |
| model_prediction_summary: {"answer": "A", "raw": "{\"answer\":\"A\",\"confidence\":0.9}", |
| "metadata": {"usage": {"completion_tokens": 11, "prompt_tokens": 70, "total_tokens": |
| 81, "completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": |
| 0, "reasoning_tokens": 0, "rejected_prediction_tokens": null, "image_tokens": |
| 0}, "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0, "cache_write_tokens": |
| 0, "video_tokens": 0}, "cost": 6.732e-05, "is_byok": false, "cost_details": {"upstream_inference_cost": |
| 6.8e-05, "upstream_inference_prompt_cost": 3.5e-05, "upstream_inference_completions_cost": |
| 3.3e-05}}}} |
| |
| judge_or_scorer_feedback: {"confidence": 0.9} |
| |
| traversal_excerpt: system: You are a medical expert. Return ONLY valid JSON in |
| this exact format: {"answer":"A","confidence":0.0}. The answer must be A, B, C, |
| or D.' |
| - 'Ductography |
| |
| B. Excisional biopsy of glandular tissue |
| |
| C. Repeat mammography |
| |
| D. Repeat ultrasonography of the right breast |
| |
| model_prediction_summary: {"answer": "D", "raw": "{\"answer\":\"D\",\"confidence\":0.9}", |
| "metadata": {"usage": {"completion_tokens": 11, "prompt_tokens": 238, "total_tokens": |
| 249, "completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": |
| 0, "reasoning_tokens": 0, "rejected_prediction_tokens": null, "image_tokens": |
| 0}, "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0, "cache_write_tokens": |
| 0, "video_tokens": 0}, "cost": 0.00015048, "is_byok": false, "cost_details": {"upstream_inference_cost": |
| 0.000152, "upstream_inference_prompt_cost": 0.000119, "upstream_inference_completions_cost": |
| 3.3e-05}}}} |
| |
| judge_or_scorer_feedback: {"confidence": 0.9} |
| |
| traversal_excerpt: system: You are a medical expert. Return ONLY valid JSON in |
| this exact format: {"answer":"A","confidence":0.0}. The answer must be A, B, C, |
| or D.' |
| - source_sentence: 'tals) The FHIR Observation.Create (Vitals) resource can file to |
| all non-duplicable flowsheet rows, including vital signs |
| |
| This resource can file vital signs for all flowsheets.", "name": "POST {api_base}/Observation", |
| "parameters": {"properties": {"category": {"items": {"properties": {"coding": |
| {"items": {"properties": {"code": {"description": "Use \"vital-signs\" ", "type": |
| "string"}, "display": {"description": "Use \"Vital Signs\" ", "type": "string"}, |
| "system": {"description": "Use \"http://hl7.org/fhir/observation-category\" ", |
| "type": "string"}}, "type": "object"}, "type": "array"}}, "type": "object"}, "type": |
| "array"}, "code": {"properties": {"text": {"description": "The flowsheet ID, encoded |
| flowsheet ID, or LOINC codes to flowsheet mapping' |
| sentences: |
| - 'of the three cases, and you can call only one function each time. You SHOULD |
| NOT include any other text in the response. |
| |
| Here is a list of functions in JSON format that you can invoke. Note that you |
| should use https://medagentbench.openwear.ai/fhir as the api_base.' |
| - 'asoning_tokens": 0, "rejected_prediction_tokens": null, "image_tokens": 0}, "prompt_tokens_details": |
| {"audio_tokens": 0, |
| |
| "cached_tokens": 0, "cache_write_tokens": 0, "video_tokens": 0}, "cost": 0.00188496, |
| "is_byok": false, "cost_details": {"upstream_inference_cost": 0.001904, "upstream_inference_prompt_cost": |
| 0.001814, "upstream_inference_completions_cost": 9e-05}}, {"completion_tokens": |
| 78, "prompt_tokens": 4557, "total_tokens": 4635, "completion_tokens_details": |
| {"accepted_prediction_tokens": null, "audio_tokens": 0, "reasoning_tokens": 0, |
| "rejected_prediction_tokens": null, "image_tokens": 0}, "prompt_tokens_details": |
| {"audio_tokens": 0, "cached_tokens": 0, "cache_write_tokens": 0, "video_tokens": |
| 0}, "cost": 0.002487375, "is_byok": false, "cost_details": {"upstream_inference_cost": |
| 0.0025125, "upstream_inference_prompt_cost": 0.0022785,' |
| - 'tals) The FHIR Observation.Create (Vitals) resource can file to all non-duplicable |
| flowsheet rows, including vital signs |
| |
| This resource can file vital signs for all flowsheets.", "name": "POST {api_base}/Observation", |
| "parameters": {"properties": {"category": {"items": {"properties": {"coding": |
| {"items": {"properties": {"code": {"description": "Use \"vital-signs\" ", "type": |
| "string"}, "display": {"description": "Use \"Vital Signs\" ", "type": "string"}, |
| "system": {"description": "Use \"http://hl7.org/fhir/observation-category\" ", |
| "type": "string"}}, "type": "object"}, "type": "array"}}, "type": "object"}, "type": |
| "array"}, "code": {"properties": {"text": {"description": "The flowsheet ID, encoded |
| flowsheet ID, or LOINC codes to flowsheet mapping' |
| - source_sentence: 'nRequest\" for medication requests.", "type": "string"}, "status": |
| {"description": "The status of the medication request |
| |
| Use \"active\" ", "type": "string"}, "subject": {"properties": {"reference": {"description": |
| "The patient FHIR ID for who the medication request is for.", "type": "string"}}, |
| "type": "object"}}, "required": ["resourceType", "medicationCodeableConcept", |
| "authoredOn", "dosageInstruction", "status", "intent", "subject"], "type": "object"}}, |
| {"description": "Procedure.Search (Orders) The FHIR Procedure resource defines |
| an activity performed on or with a patient as part of the provision of care. It |
| corresponds with surgeries and procedures performed, including endoscopies and |
| biopsies, as well as less invasive actions like counseling and physiotherapy.\n\nThis |
| resource is designed for a high-level summarization around the occurrence of a |
| procedure, and not for specific procedure log documentation - a concept that does |
| not yet have a defined FHIR Resource' |
| sentences: |
| - 'een successfully ordered with the specified clinical notes. The ServiceRequest |
| ID is 786041.\"]", "status": "completed"} |
| |
| judge_or_scorer_feedback: {"status": "completed", "reset": null, "llm_usage": |
| [{"completion_tokens": 238, "prompt_tokens": 3362, "total_tokens": 3600, "completion_tokens_details": |
| {"accepted_prediction_tokens": null, "audio_tokens": 0, "reasoning_tokens": 0, |
| "rejected_prediction_tokens": null, "image_tokens": 0}, "prompt_tokens_details": |
| {"audio_tokens": 0, "cached_tokens": 0, "cache_write_tokens": 0, "video_tokens": |
| 0}, "cost": 0.00237105, "is_byok": false, "cost_details": {"upstream_inference_cost": |
| 0.002395, "upstream_inference_prompt_cost": 0.001681, "upstream_inference_completions_cost": |
| 0.000714}}, {"completion_tokens": 41, "prompt_tokens": 3877, "total_tokens": 3918, |
| "completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": |
| 0, "reasoning_tokens": 0, "rejected_prediction_tokens": null, "image_tokens": |
| 0}, "prompt_tokens_details": {"audio_tokens": 0,' |
| - 'nRequest\" for medication requests.", "type": "string"}, "status": {"description": |
| "The status of the medication request |
| |
| Use \"active\" ", "type": "string"}, "subject": {"properties": {"reference": {"description": |
| "The patient FHIR ID for who the medication request is for.", "type": "string"}}, |
| "type": "object"}}, "required": ["resourceType", "medicationCodeableConcept", |
| "authoredOn", "dosageInstruction", "status", "intent", "subject"], "type": "object"}}, |
| {"description": "Procedure.Search (Orders) The FHIR Procedure resource defines |
| an activity performed on or with a patient as part of the provision of care. It |
| corresponds with surgeries and procedures performed, including endoscopies and |
| biopsies, as well as less invasive actions like counseling and physiotherapy.\n\nThis |
| resource is designed for a high-level summarization around the occurrence of a |
| procedure, and not for specific procedure log documentation - a concept that does |
| not yet have a defined FHIR Resource' |
| - 't: Lassa and Ebola are emergent viruses in W. Africa. What is their origin? |
| |
| |
| A. Humans |
| |
| B. Primates |
| |
| C. Fruit bats |
| |
| D. Pigs |
| |
| model_prediction_summary: {"answer": "C", "raw": "{\"answer\":\"C\",\"confidence\":0.95}", |
| "metadata": {"usage": {"completion_tokens": 12, "prompt_tokens": 76, "total_tokens": |
| 88, "completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": |
| 0, "reasoning_tokens": 0, "rejected_prediction_tokens": null, "image_tokens": |
| 0}, "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0, "cache_write_tokens": |
| 0, "video_tokens": 0}, "cost": 7.326e-05, "is_byok": false, "cost_details": {"upstream_inference_cost": |
| 7.4e-05, "upstream_inference_prompt_cost": 3.8e-05, "upstream_inference_completions_cost": |
| 3.6e-05}}}} |
| |
| judge_or_scorer_feedback: {"confidence": 0.95} |
| |
| traversal_excerpt: system: You are a medical expert. Return ONLY valid JSON in |
| this exact format: {"answer":"A","confidence":0.0}. The answer must be A, B, C, |
| or D.' |
| - source_sentence: 'e medication administration date. This parameter corresponds to |
| the dosageInstruction.timing.repeat.boundsPeriod element |
| |
| Medication orders that do not have start and end dates within the search parameter |
| dates are filtered. If the environment supports multiple time zones, the search |
| dates are adjusted one day in both directions, so more medications might be returned |
| than expected. Use caution when filtering a medication list by date as it is possible |
| to filter out important active medications. Starting in the November 2022 version |
| of Epic, this parameter is respected' |
| sentences: |
| - 'e medication administration date. This parameter corresponds to the dosageInstruction.timing.repeat.boundsPeriod |
| element |
| |
| Medication orders that do not have start and end dates within the search parameter |
| dates are filtered. If the environment supports multiple time zones, the search |
| dates are adjusted one day in both directions, so more medications might be returned |
| than expected. Use caution when filtering a medication list by date as it is possible |
| to filter out important active medications. Starting in the November 2022 version |
| of Epic, this parameter is respected' |
| - 'benchmark: medagentbench |
| |
| task_id: medagentbench::train::task7_29 |
| |
| reward: 0.0 |
| |
| failure_mode: completed |
| |
| general_advice: Use only the permitted FHIR action format, inspect tool responses |
| before finishing, and call FINISH only with the requested final values. |
| |
| task_request: You are an expert in using FHIR functions to assist medical professionals. |
| You are given a question and a set of possible functions. Based on the question, |
| you will need to make one or more function/tool calls to achieve the purpose. |
| |
| |
| 1. If you decide to invoke a GET function, you MUST put it in the format of |
| |
| GET url?param_name1=param_value1¶m_name2=param_value2... |
| |
| |
| 2. If you decide to invoke a POST function, you MUST put it in the format of |
| |
| POST url |
| |
| [your payload data in JSON format]' |
| - 'e": {"properties": {"text": {"description": "The flowsheet ID, encoded flowsheet |
| ID, or LOINC codes to flowsheet mapping |
| |
| What is being measured.", "type": "string"}}, "type": "object"}, "effectiveDateTime": |
| {"description": "The date and time the observation was taken, in ISO format.", |
| "type": "string"}, "resourceType": {"description": "Use \"Observation\" for vitals |
| observations.", "type": "string"}, "status": {"description": "The status of the |
| observation. Only a value of \"final\" is supported' |
| 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) on the umsb-episodic-memory dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval. |
|
|
| ## 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:** 384 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| - **Training Dataset:** |
| - umsb-episodic-memory |
| <!-- - **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("sentence_transformers_model_id") |
| # Run inference |
| sentences = [ |
| 'e medication administration date. This parameter corresponds to the dosageInstruction.timing.repeat.boundsPeriod element\nMedication orders that do not have start and end dates within the search parameter dates are filtered. If the environment supports multiple time zones, the search dates are adjusted one day in both directions, so more medications might be returned than expected. Use caution when filtering a medication list by date as it is possible to filter out important active medications. Starting in the November 2022 version of Epic, this parameter is respected', |
| 'e medication administration date. This parameter corresponds to the dosageInstruction.timing.repeat.boundsPeriod element\nMedication orders that do not have start and end dates within the search parameter dates are filtered. If the environment supports multiple time zones, the search dates are adjusted one day in both directions, so more medications might be returned than expected. Use caution when filtering a medication list by date as it is possible to filter out important active medications. Starting in the November 2022 version of Epic, this parameter is respected', |
| 'e": {"properties": {"text": {"description": "The flowsheet ID, encoded flowsheet ID, or LOINC codes to flowsheet mapping\nWhat is being measured.", "type": "string"}}, "type": "object"}, "effectiveDateTime": {"description": "The date and time the observation was taken, in ISO format.", "type": "string"}, "resourceType": {"description": "Use \\"Observation\\" for vitals observations.", "type": "string"}, "status": {"description": "The status of the observation. Only a value of \\"final\\" is supported', |
| ] |
| 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, 0.3683], |
| # [1.0000, 1.0000, 0.3683], |
| # [0.3683, 0.3683, 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 |
|
|
| #### umsb-episodic-memory |
|
|
| * Dataset: umsb-episodic-memory |
| * Size: 3,067 training samples |
| * Columns: <code>anchor</code> and <code>positive</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | |
| |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 33 tokens</li><li>mean: 221.24 tokens</li><li>max: 384 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 221.24 tokens</li><li>max: 384 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | |
| |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>description": "The patient's family (last) name.", "type": "string"}, "gender": {"description": "The patient's legal sex<br>Starting in the August 2021 version of Epic, the legal-sex parameter is preferred.", "type": "string"}, "given": {"description": "The patient's given name. May include first and middle names.", "type": "string"}, "identifier": {"description": "The patient's identifier.", "type": "string"}, "legal-sex": {"description": "The patient\u2019s legal sex. Takes precedence over the gender search parameter. Available starting in the August 2021 version of Epic.", "type": "string"}, "name": {"description": "Any part of the patient's name. When discrete name parameters are used, such as family or given, this parameter is ignored.", "type": "string"}, "telecom": {"description": "The patient's phone number or email.", "type": "string"}}, "required": [], "type": "object"}}]</code> | <code>description": "The patient's family (last) name.", "type": "string"}, "gender": {"description": "The patient's legal sex<br>Starting in the August 2021 version of Epic, the legal-sex parameter is preferred.", "type": "string"}, "given": {"description": "The patient's given name. May include first and middle names.", "type": "string"}, "identifier": {"description": "The patient's identifier.", "type": "string"}, "legal-sex": {"description": "The patient\u2019s legal sex. Takes precedence over the gender search parameter. Available starting in the August 2021 version of Epic.", "type": "string"}, "name": {"description": "Any part of the patient's name. When discrete name parameters are used, such as family or given, this parameter is ignored.", "type": "string"}, "telecom": {"description": "The patient's phone number or email.", "type": "string"}}, "required": [], "type": "object"}}]</code> | |
| | <code>e to filter out important active medications. Starting in the November 2022 version of Epic, this parameter is respected |
| In May 2022 and earlier versions of Epic, this parameter is allowed but is ignored and no date filtering is applied.", "type": "string"}, "patient": {"description": "The FHIR patient ID.", "type": "string"}}, "required": ["patient"], "type": "object"}}, {"description": "MedicationRequest.Create", "name": "POST {api_base}/MedicationRequest", "parameters": {"properties": {"authoredOn": {"description": "The date the prescription was written.", "type": "string"}, "dosageInstruction": {"items": {"properties": {"doseAndRate": {"items": {"properties": {"doseQuantity": {"properties": {"unit": {"description": "unit for the dose such as \"g\" ", "type": "string"}, "value": {"type": "number"}}, "type": "object"}, "rateQuantity": {"properties": {"unit": {"description": "unit for the rate such as \"h\" ", "type": "string"}, "value": {"type": "number"}}, "type": "object"}}, "type"...</code> | <code>e to filter out important active medications. Starting in the November 2022 version of Epic, this parameter is respected |
| In May 2022 and earlier versions of Epic, this parameter is allowed but is ignored and no date filtering is applied.", "type": "string"}, "patient": {"description": "The FHIR patient ID.", "type": "string"}}, "required": ["patient"], "type": "object"}}, {"description": "MedicationRequest.Create", "name": "POST {api_base}/MedicationRequest", "parameters": {"properties": {"authoredOn": {"description": "The date the prescription was written.", "type": "string"}, "dosageInstruction": {"items": {"properties": {"doseAndRate": {"items": {"properties": {"doseQuantity": {"properties": {"unit": {"description": "unit for the dose such as \"g\" ", "type": "string"}, "value": {"type": "number"}}, "type": "object"}, "rateQuantity": {"properties": {"unit": {"description": "unit for the rate such as \"h\" ", "type": "string"}, "value": {"type": "number"}}, "type": "object"}}, "type"...</code> | |
| | <code>e": {"properties": {"text": {"description": "The flowsheet ID, encoded flowsheet ID, or LOINC codes to flowsheet mapping<br>What is being measured.", "type": "string"}}, "type": "object"}, "effectiveDateTime": {"description": "The date and time the observation was taken, in ISO format.", "type": "string"}, "resourceType": {"description": "Use \"Observation\" for vitals observations.", "type": "string"}, "status": {"description": "The status of the observation. Only a value of \"final\" is supported</code> | <code>e": {"properties": {"text": {"description": "The flowsheet ID, encoded flowsheet ID, or LOINC codes to flowsheet mapping<br>What is being measured.", "type": "string"}}, "type": "object"}, "effectiveDateTime": {"description": "The date and time the observation was taken, in ISO format.", "type": "string"}, "resourceType": {"description": "Use \"Observation\" for vitals observations.", "type": "string"}, "status": {"description": "The status of the observation. Only a value of \"final\" is supported</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`: 32 |
| - `num_train_epochs`: 2 |
| - `learning_rate`: 2e-05 |
| - `warmup_steps`: 0.05 |
| - `fp16`: True |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `per_device_train_batch_size`: 32 |
| - `num_train_epochs`: 2 |
| - `max_steps`: -1 |
| - `learning_rate`: 2e-05 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 0.05 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `weight_decay`: 0.0 |
| - `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`: False |
| - `fp16`: True |
| - `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`: 8 |
| - `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`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `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`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `warmup_ratio`: None |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | |
| |:------:|:----:|:-------------:| |
| | 0.2604 | 25 | 0.3777 | |
| | 0.5208 | 50 | 0.3348 | |
| | 0.7812 | 75 | 0.3936 | |
| | 1.0417 | 100 | 0.3583 | |
| | 1.3021 | 125 | 0.3227 | |
| | 1.5625 | 150 | 0.3224 | |
| | 1.8229 | 175 | 0.3492 | |
|
|
|
|
| ### Training Time |
| - **Training**: 2.4 minutes |
|
|
| ### Framework Versions |
| - Python: 3.11.12 |
| - Sentence Transformers: 5.4.1 |
| - Transformers: 5.8.0 |
| - PyTorch: 2.11.0+cu130 |
| - Accelerate: 1.13.0 |
| - Datasets: 4.8.5 |
| - Tokenizers: 0.22.2 |
|
|
| ## 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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