| --- |
| license: apache-2.0 |
| task_categories: |
| - text-classification |
| language: |
| - en |
| tags: |
| - telecommunications |
| - telecom |
| - 3gpp |
| - oran |
| - gsma |
| - rag |
| - reranker |
| - cross-encoder |
| - retrieval |
| - domain-specific |
| - otel |
| size_categories: |
| - 100K<n<1M |
| pretty_name: OTel-Reranker |
| dataset_info: |
| features: |
| - name: sentence_0 |
| dtype: string |
| - name: sentence_1 |
| dtype: string |
| - name: label |
| dtype: float64 |
| --- |
| |
| # OTel-Reranker |
|
|
| ## Dataset Summary |
|
|
| OTel-Reranker is a pointwise reranking dataset for training cross-encoder models that score the relevance of a passage to a given query. It is part of the Open Telco (OTel) AI project, the largest open-source AI initiative in telecommunications, curated by over 100 domain experts from industry and academia. |
|
|
| Each sample consists of a query-passage pair and a continuous relevance score. The dataset is designed to train cross-encoder reranker models that refine the candidate set returned by a first-stage retriever, forming the reranking component of end-to-end RAG pipelines in telecommunications. |
|
|
| ## Dataset Structure |
|
|
| Each sample contains the following fields: |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `sentence_0` | string | The query or information need. | |
| | `sentence_1` | string | A candidate passage to be scored for relevance to the query. | |
| | `label` | float | A continuous relevance score indicating how well the passage addresses the query. Higher values indicate greater relevance. | |
|
|
| The continuous label formulation supports pointwise reranking objectives, where the model learns to predict a scalar relevance score for each query-passage pair independently. |
|
|
| ## Data Sources and Contributor Attribution |
|
|
| The OTel corpus was curated by 100+ domain experts from industry and academia. Each source class was contributed by a specific institutional partner: |
|
|
| | Source | Contributor | Raw samples | |
| |---|---|---:| |
| | arXiv telecom papers, 3GPP standards, telecom Wikipedia, telecom Common Crawl pages | Yale University | 681,172 | |
| | GSMA Permanent Reference Documents (PRDs), Discover portal, mixed telecom documents | GSMA | 158,006 | |
| | IETF RFC series | NetoAI | 100,751 | |
| | Industry whitepapers | Khalifa University | 62,000 | |
| | O-RAN specifications across working groups 1, 2, 4, 5, 6, 7, 8, 9, 10 | University of Leeds | 58,565 | |
| | O-RAN documents across working groups | The University of Texas at Dallas | 42,000 | |
| | **Total raw samples** | | **~1,102,494** | |
|
|
| After the four-stage cleaning pipeline (described below), the corpus was reduced to **326,767** higher-confidence examples across the OTel dataset family. |
|
|
| ## Data Collection and Processing |
|
|
| The raw corpus comprised approximately 1.1 million samples. A four-stage cleaning pipeline reduced this to 326K samples across the OTel dataset family: |
|
|
| 1. **Heuristic filtering**: Rule-based removal of malformed, duplicated, or low-quality entries. |
| 2. **Reranking filtering**: Relevance scoring with a cross-encoder reranker to discard weakly aligned query-passage pairs. |
| 3. **Embedding filtering**: Semantic quality assessment with a strong embedding model to remove outliers and near-duplicates in embedding space. |
| 4. **Deduplication**: Final deduplication pass to ensure sample uniqueness. |
|
|
| Relevance labels were derived from the data generation and filtering pipeline, producing continuous scores that capture graded relevance rather than binary judgments. |
|
|
| ## Intended Use |
|
|
| OTel-Reranker is intended for training cross-encoder reranker models for telecom-domain retrieval. Target applications include: |
|
|
| - Fine-tuning cross-encoder models for second-stage reranking in telecom RAG pipelines. |
| - Improving retrieval precision by rescoring candidate passages returned by a bi-encoder first-stage retriever (e.g., a model trained on OTel-Embedding). |
| - Research on domain-specific reranking and its effect on downstream generation quality in RAG systems. |
|
|
| ## Limitations |
|
|
| - **Domain scope**: The dataset covers telecommunications standards and specifications. Cross-encoder models trained on this data are optimized for telecom relevance scoring and may not generalize to other domains. |
| - **Language**: English only. |
| - **Label granularity**: Relevance scores are derived from the automated pipeline and may not perfectly correspond to expert human judgments in all cases. |
| - **Temporal coverage**: The source documents reflect standards and specifications available at the time of collection. Newer revisions may not be represented. |
|
|
| ## Resources |
|
|
| - [Project Page](https://huggingface.co/att) |
| - [OTel code](https://github.com/farbodtavakkoli/OTel) |
| - [OTel Selected Coverage](https://github.com/farbodtavakkoli/OTel/blob/main/docs/OTel-selected-coverage.md) |
| - [OTel Paper](https://arxiv.org/pdf/2610.07766) |
| - [OTel 1.0 Coverage](https://github.com/farbodtavakkoli/OTel/blob/main/docs/OTel-1.0-coverage.md) |
| - [OTel 2.0 Coverage](https://github.com/farbodtavakkoli/OTel/blob/main/docs/OTel-2.0-coverage.md) |
| - [OTel Docker](https://hub.docker.com/u/farbodatdocker) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{tavakkoli2026otel, |
| title = {OTel: Open Telco AI Datasets, Benchmarks, and Models}, |
| author = {Tavakkoli, Farbod and others}, |
| booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track}, |
| year = {2026}, |
| url = {https://github.com/farbodtavakkoli/OTel} |
| } |
| ``` |
