by farbodtavakkoli
Open source · 790k downloads · 0 likes
OTel Embedding 22M is an embedding model specifically designed for the telecommunications sector, optimized to understand and process technical data related to industry standards and norms. It excels in information retrieval and question-answering applications, particularly for leveraging technical documents such as 3GPP specifications, RFCs, or GSMA documents. Trained on data validated by over 200 industry experts, it ensures enhanced accuracy and relevance for telecom professionals. The model stands out for its ability to generate vector representations tailored to the specific needs of industry stakeholders, simplifying integration into RAG systems or semantic analysis tools. It is part of a broader collection of telecom-focused models, providing a consistent and high-performance solution for the ecosystem.
OTel-Embedding-22M is a telecom-specialized embedding model fine-tuned on telecommunications domain data. It is part of the OTel Family of Models, an open-source initiative to build industry-standard AI models for the global telecommunications sector.
| Attribute | Value |
|---|---|
| Base Model | sentence-transformers/all-MiniLM-L6-v2 |
| Parameters | 22M |
| Training Method | Full parameter fine-tuning |
| Language | English |
| License | Apache 2.0 |
The model was trained on high-quality telecom-focused data curated by 200+ domain experts from organizations including AT&T, RelationalAI, AMD, GSMA, Purdue University, Khalifa University, University of Leeds, Yale University, The University of Texas at Dallas, NetoAI, and MantisNLP.
Data Sources:
This model is optimized for:
@misc{otel2026,
title={OTel: Open Telco AI Models},
author={Tavakkoli, Farbod and Diamos, Gregory and Paulk, Roderic and Terrazas, Jorden},
year={2026},
url={https://huggingface.co/farbodtavakkoli}
}
If you have any technical questions, please feel free to reach out to [email protected] or [email protected]