Model Overview

Description:

The intfloat/e5-large-unsupervised is a text embedding model without supervised fine-tuning. This is used to generate embeddings from input text. This model is to be used with the Nv IGI SDK embed plugin.

This model is ready for commercial/non-commercial use.

Model Developer: Microsoft

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 e5-large-unsupervised Model Card.

License/Terms of Use:

This model is distributed under MIT license e5-large-unsupervised License. Please refer to e5-large-unsupervised Model Card. for further details.

Reference(s):

e5-large-unsupervised Hugging Face

Model Architecture:

Architecture Type: Transformer
Network Architecture: MiniLM

Input:

Input Type(s): Text
Input Format(s): String
Input Parameters: 1D

Output:

Output Type(s): Embedding vectors
Output Format: Vector
Output Parameters: 2D

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ada

Supported Operating System(s):

Windows

Model Version(s):

e5-large-unsupervised GGUF q4_k_s 1.0
Nv IGI SDK Model GUID : {5D458A64-C62E-4A9C-9086-2ADBF6B241C7}

Training, Testing, and Evaluation Datasets:

Training Dataset:

Data Collection Method by dataset: Unknown
Labeling Method by dataset: Unknown
Properties: The model is trained in a contrastive manner with weak supervision signals from Microsoft’s curated large-scale text pair dataset (called CCPairs). Please refer to the e5 training paper for additional information.

Please refer to e5-large-unsupervised Model Card for information on Training, Testing and Evaluation Datasets

Inference:

Engine: GGUF
Test Hardware : RTX 4090

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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This is used to generate embeddings from input text. This model is to be used with the Nv IGI SDK embed plugin.\n\nThis model is ready for commercial/non-commercial use.\n\n## Model Developer: Microsoft\n\n## Third-Party Community Consideration\nThis 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 [e5-large-unsupervised Model Card](https://huggingface.co/intfloat/e5-large-unsupervised).\n\n## License/Terms of Use:\nThis model is distributed under MIT license [e5-large-unsupervised License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md). Please refer to [e5-large-unsupervised Model Card](https://huggingface.co/intfloat/e5-large-unsupervised). for further details.\n\n## Reference(s):\n- e5-large-unsupervised [Hugging Face](https://huggingface.co/intfloat/e5-large-unsupervised)\n\n## Model Architecture:\n- **Architecture Type:** Transformer\n- **Network Architecture:** MiniLM\n\n## Input: \n- **Input Type(s):** Text\n- **Input Format(s):** String\n- **Input Parameters:** 1D\n\n## Output: \n- **Output Type(s):** Embedding vectors\n- **Output Format:** Vector\n- **Output Parameters:** 2D\n\n**Supported Hardware Microarchitecture Compatibility:**\n- NVIDIA Ada\n\n**Supported Operating System(s):**\n- Windows\n\n## Model Version(s):\n\n- e5-large-unsupervised GGUF q4_k_s 1.0\n- Nv IGI SDK Model GUID : {5D458A64-C62E-4A9C-9086-2ADBF6B241C7}\n\n## Training, Testing, and Evaluation Datasets:\n\nTraining Dataset:\n- **Data Collection Method by dataset:** Unknown\n- **Labeling Method by dataset:** Unknown\n- **Properties:** The model is trained in a contrastive manner with weak supervision signals from Microsoft’s curated large-scale text pair dataset (called CCPairs). Please refer to the [e5 training paper](https://arxiv.org/pdf/2212.03533) for additional information. \n\nPlease refer to [e5-large-unsupervised Model Card](https://huggingface.co/intfloat/e5-large-unsupervised) for information on Training, Testing and Evaluation Datasets\n\n## Inference:\n- **Engine:** GGUF\n- **Test Hardware** : RTX 4090\n\n## Ethical Considerations:\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","e5-large-unsupervised GGUF for Nv IGI SDK",["D",1733519407449],"publisher","","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nvigisdk/models/e5-large-unsupervised","rawImageUrl","https://developer.download.nvidia.com/images/products/aim-sdk-1920x1080.jpg","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[255],"displayLabels",[236,237,238,239],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_204":251,"_241":252,"_242":253,"_244":245},{"_204":249,"_241":249,"_242":250,"_244":245},{"_204":247,"_241":247,"_242":248,"_244":245},{"_204":240,"_241":240,"_242":243,"_244":245},"Nv IGI SDK Plugin","value","filter",{"_246":245,"_241":240},"category","label","field","Nv IGI SDK",{"_246":245,"_241":247},"GGUF",{"_246":245,"_241":249},"AI","soln_ai",{"_246":254,"_241":252},"technology",{"_204":256,"_241":257,"_242":258,"_244":259},"Runs on RTX","pltfm_runs_on_rtx",{"_246":260,"_241":257},"feature","platform",{"_106":1239,"_108":1240},"ngcApps",[1192],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1173":1174,"_1175":1176,"_1177":1178,"_1179":1180,"_1181":272,"_137":298,"_1182":1183,"_1184":56,"_1185":85,"_1186":85,"_1187":1188,"_1189":85,"_1190":1191},"amplitude",{"_1170":1171,"_810":-7,"_1172":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1164":1165,"_1166":1167,"_1168":1169},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787647853312,"1.12.0","defaultSearchSuggestions",{"_1050":208,"_1051":1052,"_1053":1054,"_1055":1056},"announcements",[],"consentState",{"_335":1040,"_1041":85,"_1042":31,"_1043":31,"_1044":1045},"consentConfig",{"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336},"showContactAdmin",1787647853313,[],[],[312,313,314,315,316],{"_45":59,"_47":69,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":101,"_51":-5,"_53":-5,"_55":56},"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. 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