Edit model card
YAML Metadata Error: "datasets[0]" with value "220M words (IndoWiki, IndoWC, News)" is not valid. If possible, use a dataset id from https://hf.co/datasets.
YAML Metadata Error: "datasets[1]" with value "Squad 2.0 (Indonesian translated)" is not valid. If possible, use a dataset id from https://hf.co/datasets.

Github

This project is part of my research with my friend Muhammad Fajrin Buyang Daffa entitled "Teman Belajar : Asisten Digital Pelajar SMA Negeri 28 Jakarta dalam Membaca" for KOPSI (Kompetisi Penelitian Siswa Indonesia/Indonesian Student Research Competition).

indoBERT Base-Uncased fine-tuned on Translated Squad v2.0

IndoBERT trained by IndoLEM and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.

Model Size (after training): 420mb

Details of indoBERT (from their documentation)

IndoBERT is the Indonesian version of BERT model. We train the model using over 220M words, aggregated from three main sources:

  • Indonesian Wikipedia (74M words)
  • news articles from Kompas, Tempo (Tala et al., 2003), and Liputan6 (55M words in total)
  • an Indonesian Web Corpus (Medved and Suchomel, 2017) (90M words).

We trained the model for 2.4M steps (180 epochs) with the final perplexity over the development set being 3.97 (similar to English BERT-base).

This IndoBERT was used to examine IndoLEM - an Indonesian benchmark that comprises of seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse.[1]

Details of the downstream task (Q&A) - Dataset

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.

Dataset Split # samples
SQuAD2.0 train 130k
SQuAD2.0 eval 12.3k

Model Training

The model was trained on a Tesla T4 GPU and 12GB of RAM.

Results:

Metric # Value
EM 51.61
F1 69.09

Simple Usage

from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="Rifky/Indobert-QA",
    tokenizer="Rifky/Indobert-QA"
)

qa_pipeline({
    'context': """Pangeran Harya Dipanegara (atau biasa dikenal dengan nama Pangeran Diponegoro, lahir di Ngayogyakarta Hadiningrat, 11 November 1785 – meninggal di Makassar, Hindia Belanda, 8 Januari 1855 pada umur 69 tahun) adalah salah seorang pahlawan nasional Republik Indonesia, yang memimpin Perang Diponegoro atau Perang Jawa selama periode tahun 1825 hingga 1830 melawan pemerintah Hindia Belanda. Sejarah mencatat, Perang Diponegoro atau Perang Jawa dikenal sebagai perang yang menelan korban terbanyak dalam sejarah Indonesia, yakni 8.000 korban serdadu Hindia Belanda, 7.000 pribumi, dan 200 ribu orang Jawa serta kerugian materi 25 juta Gulden.""",
    'question': "kapan pangeran diponegoro lahir?"
})

output:

{
  'answer': '11 November 1785',
  'end': 131,
  'score': 0.9272009134292603,
  'start': 115
}

Reference

[1]Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin. 2020. IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP. Proceedings of the 28th COLING.

Downloads last month
1,032
Safetensors
Model size
110M params
Tensor type
I64
Β·
F32
Β·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for Rifky/Indobert-QA

Finetunes
1 model

Space using Rifky/Indobert-QA 1