📊
Idt5 Qa Qg dataset by muchad
⭐ 25.6
📊
Dataset

Idt5 Qa Qg

by muchad hf-model--muchad--idt5-qa-qg
Nexus Index
25.6 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 100
Q: Quality 23
Tech Context
Vital Performance
0 DL / 30D
0.0%
Data Integrity 25.6 FNI Score
- Size
- Rows
Parquet Format
- Tokens
Dataset Information Summary
Entity Passport
Registry ID hf-model--muchad--idt5-qa-qg
Provider huggingface
📜

Cite this dataset

Academic & Research Attribution

BibTeX 
@misc{hf_model__muchad__idt5_qa_qg,
  author = {muchad},
  title = {Idt5 Qa Qg Dataset},
  year = {2026},
  howpublished = {\url{https://free2aitools.com/dataset/hf-model--muchad--idt5-qa-qg}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
muchad. (2026). Idt5 Qa Qg [Dataset]. Free2AITools. https://free2aitools.com/dataset/hf-model--muchad--idt5-qa-qg

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
25.6
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 100
Quality (Q) 23

đŸ’Ŧ Index Insight

FNI V2.0 for Idt5 Qa Qg: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:100), Quality (Q:23).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live

👁ī¸ Data Preview

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Row-level preview not available for this dataset.

Schema structure is shown in the Field Logic panel when available.

đŸ§Ŧ Field Logic

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Schema not yet indexed for this dataset.

Dataset Specification

idT5 for Indonesian Question Generation and Question Answering

idT5 (Indonesian version of mT5) is fine-tuned on 30% of translated SQuAD v2.0 for Question Generation and Question Answering tasks.

Live Demo

Requirements

text 
!pip install transformers==4.4.2
!pip install sentencepiece==0.1.95
!git clone https://github.com/muchad/qaqg.git
%cd qaqg/

Usage 🚀

Question Generation

Open In Colab

text 
from pipeline_qg import pipeline #pipeline_qg.py script in the cloned repo
qg = pipeline()

#sample
qg("Raja Purnawarman mulai memerintah Kerajaan Tarumanegara pada tahun 395 M.")

#output
=> [{'answer': 'Raja Purnawarman','question': 'Siapa yang memerintah Kerajaan Tarumanegara?'}, {'answer': '395 M','question': 'Kapan Raja Purnawarman memerintah Kerajaan Tarumanegara?'}]

Question Answering

Open In Colab

text 
from pipeline_qa import pipeline #pipeline_qa.py script in the cloned repo
qa = pipeline()

#sample
qa({"context":"Raja Purnawarman mulai memerintah Kerajaan Tarumanegara pada tahun 395 M.","question":"Siapa pemimpin Kerajaan Tarumanegara?"})

#output
=> Raja Purnawarman

Citation

Paper: idT5: Indonesian Version of Multilingual T5 Transformer

text 
@misc{https://doi.org/10.48550/arxiv.2302.00856,
  doi = {10.48550/ARXIV.2302.00856},  
  url = {https://arxiv.org/abs/2302.00856},  
  author = {Fuadi, Mukhlish and Wibawa, Adhi Dharma and Sumpeno, Surya},  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7},  
  title = {idT5: Indonesian Version of Multilingual T5 Transformer},  
  publisher = {arXiv},  
  year = {2023}
}
🤗 Data Source: Hugging Face ↗
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AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Dataset Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--muchad--idt5-qa-qg
slug
muchad--idt5-qa-qg
source
huggingface
author
muchad
license
tags

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

📊 Engagement & Metrics

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