by farbodtavakkoli
Open source · 344k downloads · 0 likes
OTel Embedding 335M 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 extracting knowledge from complex documents such as 3GPP specifications or RFCs. Its training on expert-validated industry data ensures accuracy tailored to the needs of telecom professionals. The model stands out for its ability to contextualize technical terms and enhance the performance of retrieval-augmented generation (RAG) systems in this field. It seamlessly integrates with analysis and automation tools used by telecom operators or technology solution providers.
OTel-Embedding-335M 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 | BAAI/bge-large-en-v1.5 |
| Parameters | 335M |
| 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]