datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
llm-ner-extraction
Introduction
This dataset is an extraction of NER data from the wikipedia dataset.
This can be used to fine tune llm models for NER extraction.
extraction-wiki-ja
extraction-wiki-ja
This repository provides an instruction-tuning dataset developed by LLM-jp, a collaborative project launched in Japan.
This is a Japanese instruction-tuning dataset tailored for information extraction and structuring from Japanese Wikipedia text.
The dataset consists of instruction–response pairs automatically generated from Japanese Wikipedia articles. Instructions are created by prompting Qwen/Qwen2.5-32B-Instruct with passages from Wikipedia, and the… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/extraction-wiki-ja.contracts-extraction-instruction-llm-experiments
Dataset Card for "contracts-extraction-instruction-llm-experiments"
More Information needed
LLM-Oasis_claim_extraction
Babelscape/LLM-Oasis_claim_extraction
Dataset Description
LLM-Oasis_claim_extraction is part of the LLM-Oasis suite and contains text-claim pairs extracted from Wikipedia pages.
It provides the data used to train the claim extraction system described in Section 3.1 of the LLM-Oasis paper. Please refer to our GitHub repository for more information on the overall data generation pipeline of LLM-Oasis.
Features
title: The title of the Wikipedia page.
text: A… See the full description on the dataset page: https://huggingface.co/datasets/Babelscape/LLM-Oasis_claim_extraction.deepseek-ai__deepseek-llm-7b-chatsci-m-wang__deepseek-llm-7b-chat-sa-v0.1deepseek-ai__deepseek-llm-67b-chatllm_extractions_literal_OLDllm-output-extraction-cases
llm-output-extraction-cases
20 messy real-world LLM outputs paired with the JSON you're trying to extract. A go-to fixture set for testing tolerant JSON extractors (e.g. agentcast) — covers fenced/unfenced blocks, prose-wrapped JSON, refusals, malformed payloads, JSON5, JavaScript-mistaken-for-JSON, unicode, and edge primitives.
Schema
{
"id": "string",
"raw": "string", // exactly what the LLM produced
"expected": "object | array | null", // the… See the full description on the dataset page: https://huggingface.co/datasets/mukunda1729/llm-output-extraction-cases.llm_extractions_literal