datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
otel-test-snippet-jsonl
⚠️ TEST DATASET - DO NOT USE FOR PRODUCTION
This is a small test snippet for internal validation purposes only.
This dataset contains a subset of OpenTelemetry traces from various LLM inference benchmarks. It is intended for testing dataset infrastructure and should NOT be used for research, benchmarking, or production purposes.
Dataset Structure
The dataset contains OpenTelemetry traces organized by:
Benchmark: appworld, tau2_telecom
Agent Framework: openai_solo… See the full description on the dataset page: https://huggingface.co/datasets/lenadan/otel-test-snippet-jsonl.codex_swebenchpro_traces_Otel
Codex SWE-Bench Pro — OTel Traces
OpenTelemetry-formatted LLM traces derived from
Inferact/codex_swebenchpro_traces,
a collection of agentic Codex runs on the
SWE-bench Pro software-engineering benchmark.
Overview
Each row in the source dataset is a full multi-turn agent conversation where a
Codex agent resolves a real GitHub issue. This dataset re-represents those
conversations as OpenTelemetry GenAI spans, one span per LLM call, using
cumulative message history… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/codex_swebenchpro_traces_Otel.gsma-sample-data
Telecom Benchmark Suite
This repository contains a lightweight benchmarking framework for evaluating Large Language Models (LLMs) on various telecom-related tasks.
It is organized into two main folders:
data/ — contains the datasets in .json format
scripts/ — contains task-specific scripts for prompting and evaluation
Each dataset in data/ has a corresponding Python script in scripts/ that defines:
A system prompt (guidance text for the LLM)
A prompt-building function for… See the full description on the dataset page: https://huggingface.co/datasets/otellm/gsma-sample-data.otel-demo-telemetry
OpenTelemetry Demo Telemetry
OTLP logs, metrics, and traces emitted by the
OpenTelemetry Demo,
captured to Parquet via duckdb-otlp.
Each OTLP signal is a separate subset (config), because the schemas differ:
Subset
Contents
traces
Spans (otlp_traces)
logs
Log records (otlp_logs)
metrics_gauge
Gauge metric points
metrics_sum
Sum / counter metric points
metrics_histogram
Histogram metric points
from datasets import load_dataset
# pick a subset by name;… See the full description on the dataset page: https://huggingface.co/datasets/smithclay/otel-demo-telemetry.lmcache-agentic-traces_Otel
Agentic LLM Traces – OTel Format
Overview
Real-world agentic LLM sessions converted to OpenTelemetry (OTel) trace format, derived from sammshen/lmcache-agentic-traces. Each session is a multi-turn agent interaction involving tool calls (bash commands, file edits, web search, etc.), spanning 5–50 turns and totalling 24,880 spans.
Traces come from three agentic benchmarks: SWE-bench, GAIA, and WildClaw. They are formatted as OTel spans following gen_ai.* semantic… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/lmcache-agentic-traces_Otel.OTel-LLM
OTel-LLM
Dataset Summary
OTel-LLM is an instruction-tuning dataset for training large language models to operate within end-to-end retrieval-augmented generation (RAG) pipelines in the telecommunications domain. 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.
The dataset pairs retrieved context passages with instruction-style prompts and reference… See the full description on the dataset page: https://huggingface.co/datasets/farbodtavakkoli/OTel-LLM.OTel-Embedding
OTel-Embedding
Dataset Summary
OTel-Embedding is a contrastive learning dataset for training bi-encoder retrieval models using Multiple Negatives Ranking Loss (MNRL). 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 an anchor query, one positive passage, and five hard negative passages. The dataset is designed to train… See the full description on the dataset page: https://huggingface.co/datasets/farbodtavakkoli/OTel-Embedding.OTel-Safety
OTel-Safety
Dataset Summary
OTel-Safety is a specialized dataset for training large language models to abstain from answering when the retrieved context in a RAG pipeline is insufficient or irrelevant. 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.
In deployed RAG systems, a common failure mode is hallucination when the retrieval step returns… See the full description on the dataset page: https://huggingface.co/datasets/farbodtavakkoli/OTel-Safety.OTel-Reranker
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… See the full description on the dataset page: https://huggingface.co/datasets/farbodtavakkoli/OTel-Reranker.pp-otel43183gpp_knowledgeGraph
📚 3GPP Rel-13 Knowledge Graph Dataset
Overview
This dataset provides a Knowledge Graph representation of 3GPP Release 13 documentation, compiled by Khalifa University. It is intended for research and development in telecommunications, semantic networks, and graph-based AI applications.
Source: 3GPP Official Site
Format: GraphML
Nodes: 12,800
Edges: 9,970
📂 Dataset Structure
File Type: .graphml
Graph Details:
Nodes: Entities extracted from 3GPP… See the full description on the dataset page: https://huggingface.co/datasets/otellm/3gpp_knowledgeGraph.telecom-kg-rel19
3GPP Rel-19 Telecom Knowledge Graph (KG + Chunks)
Dataset Summary
This dataset, developed by Khalifa University Research Institute for Digital Future (KU-DF), contains a large-scale telecom-domain knowledge graph built from 3GPP Release 19 specifications, together with the underlying text chunks used for extraction and grounding.
It supports research and development in:
Telecom and networking
Knowledge graphs
LLM-based reasoning over standards
Retrieval-Augmented… See the full description on the dataset page: https://huggingface.co/datasets/otellm/telecom-kg-rel19.