Model Overview

Description:

FlagEmbedding maps text to a low-dimensional dense vector, which can be used for tasks like retrieval, classification, clustering, or semantic search. It also can be used in vector databases for LLMs.

This model has been fine-tuned on domain specific data and is for demonstration purposes and not for production usage.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to the HuggingFace age model.

License/Terms of Use:

https://huggingface.co/BAAI/bge-large-en#license

Reference(s):

For more details please refer to the Github: FlagEmbedding repo.

Model Architecture:

Architecture Type: FlagEmbedding
Network Architecture: bge-large-en-v1.5

Input:

Input Type(s): Text
Input Format(s): String
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: No Pre-Processing

Output:

Output Type(s): Vector embeddings
Output Format: Dimensions: 1024 · Max Tokens: 512
Output Parameters: (2D)
Other Properties Related to Output: No Post-Processing

Software Integration

Runtime Engine(s):\nHoloscan SDK

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Jetson
NVIDIA Turing
NVIDIA Volta

[Preferred/Supported] Operating System(s):

Linux
Linux 4 Tegra

Model Version(s):

v 0.1

Training, Testing, and Evaluation Datasets:

Training Dataset:

Link: [https://github.com/synthetichealth/synthea]
\nData Collection Method by dataset: Synthetic
\nLabeling Method by dataset: Synthetic
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): 2500 synthetic electronic healthcare record (EHR) document and question pairs
\nDataset License(s): [https://github.com/synthetichealth/synthea/blob/master/LICENSE]

Testing Dataset:

Link: internal dataset generated by LLM
\nData Collection Method by dataset: Automated
\nLabeling Method by dataset: Automated
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): 2500 Q/A pairs from synthea dataset
\nDataset License: [https://github.com/synthetichealth/synthea/blob/master/LICENSE]

Evaluation Dataset:

Link: synthetic dataset generated by LLM
\nData Collection Method by dataset: Automated
\nLabeling Method by dataset: Automated
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): 15 Q/A pairs
\nDataset License(s): [https://github.com/synthetichealth/synthea/blob/master/LICENSE]

Inference:

Engine: LLAMA cpp
Test Hardware: IGX platform

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n**Data Collection Method by dataset:** Synthetic
\n**Labeling Method by dataset:** Synthetic
\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** 2500 synthetic electronic healthcare record (EHR) document and question pairs
\n**Dataset License(s):** [https://github.com/synthetichealth/synthea/blob/master/LICENSE]
\n\n### Testing Dataset:\n\n**Link:** internal dataset generated by LLM
\n**Data Collection Method by dataset:** Automated
\n**Labeling Method by dataset:** Automated
\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** 2500 Q/A pairs from synthea dataset
\n**Dataset License:** [https://github.com/synthetichealth/synthea/blob/master/LICENSE]
\n\n### Evaluation Dataset:\n\n**Link:** synthetic dataset generated by LLM
\n**Data Collection Method by dataset:** Automated
\n**Labeling Method by dataset:** Automated
\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** 15 Q/A pairs
\n**Dataset License(s):** [https://github.com/synthetichealth/synthea/blob/master/LICENSE]
\n\n### Inference:\n\n- **Engine:** LLAMA cpp\n- **Test Hardware:** IGX platform\n\n### Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName",["D",1730767598143],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/clara-holoscan/models/bge-large-ehr-finetune","rawImageUrl","https://avatars.githubusercontent.com/u/119712212?s=280&v=4","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[234,235,236,237,238],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_202":259,"_240":260,"_242":261,"_244":245},{"_202":256,"_240":256,"_242":257,"_244":245},{"_202":252,"_240":253,"_242":254,"_244":245},{"_202":248,"_240":249,"_242":250,"_244":245},{"_202":239,"_240":241,"_242":243,"_244":245},"Natural Language Understanding","value","uscs_natural_language_understanding","filter",{"_246":247,"_240":241},"category","label","field","application","Holoscan","pltfm_clara_holoscan",{"_246":251,"_240":249},"platform","Healthcare","indus_healthcare",{"_246":255,"_240":253},"industry","English",{"_246":258,"_240":256},"language","AI","soln_ai",{"_246":262,"_240":260},"technology",{"_104":1439,"_106":1440},"ngcApps",[1392],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1373":1374,"_1375":1376,"_1377":1378,"_1379":1380,"_1381":274,"_135":298,"_1382":1383,"_1384":55,"_1385":83,"_1386":83,"_1387":1388,"_1389":83,"_1390":1391},"amplitude",{"_1370":1371,"_972":-7,"_1372":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1364":1365,"_1366":1367,"_1368":1369},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1247":219,"_1248":1249,"_1250":1251,"_1252":1253},"announcements",[],"consentState",{"_337":1237,"_1238":83,"_1239":30,"_1240":30,"_1241":1242},"consentConfig",{"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338},"showContactAdmin",1784343382814,[],[],[312,313,314,315,316,317],{"_44":58,"_46":68,"_48":99,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":66,"_48":99,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":65,"_48":99,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":99,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":99,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":318,"_54":55},7,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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