api call
Datasets
All datasets matching “api call”api-calling-training-pool
API calling training pool
Public API-calling data from five datasets, read at the pinned revisions named below and laid out
twice. Train on either layer or on both.
pool.jsonl
Every source rewritten into one shape, 199186 rows, one JSON object per line, with these fields.
Field
What it holds
id
a row identifier unique within this file
query
the user's request, as its source publishes it
functions
the declarations offered with the request, as a list… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/api-calling-training-pool.healthcare-api-tool-calling
Healthcare API Tool Calling Dataset
Dataset Description
This dataset contains 650+ examples of healthcare API tool calling responses in JSON format. Each example includes:
query: Natural language user request
plan: JSON response following healthcare API specification
tools: List of required tools for the response
Dataset Structure
{
"query": "I want to create an appointment",
"plan": {
"action": "create",
"resource": "appointment",
"params":… See the full description on the dataset page: https://huggingface.co/datasets/aldsouza/healthcare-api-tool-calling.twse-api-call-reasoning-0.1ktpex-api-call-reasoning-1k
TPEx OpenAPI Function-Calling Reasoning (1K)
繁體中文的 function-calling 推理 SFT 資料集,共 1,000 筆。任務情境圍繞**台灣證券櫃檯買賣中心(TPEx,櫃買中心)**的開放資料查詢——上櫃股票行情、櫃買指數、權證、國際債券、ESG/財報揭露、公司基本資料等。
工具取自 TPEx 官方 OpenAPI,且每一筆樣本的工具呼叫都實際對 TPEx API 發出並驗證成功。每筆包含使用者任務(中英雙語)、繁體中文思考鏈(think)、對應的 function call(answer),以及可直接 SFT 的 Hermes 格式 messages。格式對齊 twinkle-ai/tw-function-call-reasoning-10k。
資料來源與生成方式
工具來源:TPEx 證券櫃檯買賣中心官方 OpenAPI(https://www.tpex.org.tw/openapi/swagger.json),涵蓋 120… See the full description on the dataset page: https://huggingface.co/datasets/Simon-Liu/tpex-api-call-reasoning-1k.api_callsAPI_Discovery_Retrieval_Augmented_Calling
🇰🇿 Kazakh API Discovery and Tool Retrieval Dataset
Dataset Summary
Kazakh API Discovery and Tool Retrieval Dataset is a Kazakh-language dataset designed for training and evaluating Large Language Models (LLMs) in agentic AI workflows that require API discovery, tool documentation retrieval, function calling, and multi-step tool execution.
The dataset focuses on scenarios where the assistant must first inspect or retrieve API documentation before calling the… See the full description on the dataset page: https://huggingface.co/datasets/farabi-lab/API_Discovery_Retrieval_Augmented_Calling.