att/OTel-Embedding-568M

feature-extraction model by att

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OTel-Embedding-568M is a telecom embedding model full-parameter fine-tuned on OTel telecommunications data. It is part of the OTel Family of Models, an open-source initiative to build reference AI resources for the global telecommunications sector. Across the 10 OTel embedding baselines, OTel fine-tuning improves telecom retrieval quality by +9.2 to +59.7 NDCG@10 percentage points. As of May 3, 2026, the released OTel models had been downloaded over 16 million times, and the Open Telco AI project had received 157+ pieces of media coverage worldwide. BAAI/bge-m3 -> OTel-Embedding full-parameter post-training -> farbodtavakkoli/OTel-Embedding-568M Standard errors are computed with bootstrap resampling (n=1000) over the held-out OTel evaluation partition. NDCG@10 measures whether relevant telecom passages are ranked near the top of the retrieved list. The model was trained on telecom-focused data curated by 100+ domain experts. The raw corpus contained roughly 1.1M training points and was filtered to 326,767 higher-confidence examples. Released datasets: OTel-LLM, OTel-Embedding,…

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Inside the original model card — Document outline
  1. OTel-Embedding-568M
  2. Community Use
  3. Model Details
  4. Model Lineage
  5. OTel vs. Base Model
  6. Evaluation Caveats
  7. Training Data
  8. Representative Training Row
  9. Intended Use
  10. Training Recipe
  11. Usage
  12. Limitations and Responsible Use

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Model overview

att/OTel-Embedding-568M is a feature-extraction model repository published by att on Hugging Face. A library was not reported.

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Model facts

Task
feature-extraction
Library
Not reported
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
567,754,752
Architecture
XLMRobertaModel
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-10-23T06:14:09.000Z
Last modified
2026-10-23T06:14:15.000Z

Compatibility and lineage

Reported languages: en

Reported base models: BAAI/bge-m3

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Source: Hugging Face Hub. Metadata observed 2026-10-23T06:46:00.441Z. Daily imports are snapshots, not real-time monitoring.

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