Datasets:

Rickaym
/

Myanmar-G12L-Benchmark

license: apache-2.0
language:
  - my
pretty_name: Myanmar G12L Benchmar
size_categories:
  - n<1K
dataset_info:
  features:
    - name: title
      dtype: string
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: type
      dtype: string
    - name: option_a
      dtype: string
    - name: option_b
      dtype: string
    - name: option_c
      dtype: string
  splits:
    - name: test
      num_bytes: 1643102
      num_examples: 962
  download_size: 469838
  dataset_size: 1643102
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*

Myanmar G12L Benchmark

Burmese language matriculation examination questions to benchmark formal knowledge in literature.

Dataset Details

Our Burmese language matriculation examination resource is a comprehensive tool for evaluating and strengthening formal literary knowledge. It features the following question formats: Short Answer, True or False, Metaphor Analysis, Fill-in-the-Blank, Multiple Choice, Long-Form Response, and Meaning Interpretation. Each question has been extracted from past examination papers and authoritative exam guides

Curated by: Pyae Sone Myo, Min Thein Kyaw, May Myat Noe Aung, Arkar Zaw
Language(s) (NLP): Burmese
License: Apache 2.0

Evaluation

To evaluate models on this benchmark, you can use the ayamytk (Aya Myanmar Toolkit) that was originally developed for running this benchmark.

Install the toolkit directly.

!pip install git+https://github.com/Rickaym/aya-my-tk

Run the ExamEval

from ayamytk.test.bench import evals
from ayamytk.test.bench.sampler.custom_sampler import CustomSampler

def chat(messages):
  # Add your inference code here
  return ...

evals.run(samplers={"your-model": CustomSampler(chat=chat)}, evals="mg12l")

Dataset Structure

Schema overview This dataset captures individual exam items with seven core fields:

title: a brief non-unique identifier for the question
question: the full prompt or stem
answer: the correct response
type: one of MCQ, TOF, FIB, SHORT_QNA, LONG_QNA, MEANING_QNA, or METAPHOR_QNA
option_a, option_b, option_c: the three distractors for multiple-choice items (populated only when type = MCQ; otherwise left blank)

Each row corresponds to a single question, and non-MCQ entries simply omit the option fields.

Source Data

Data Collection and Processing

Steps:

Google Document OCR
Manual Extraction and Correction

Bias, Risks, and Limitations

This dataset only captures the literature subject of the matriculation exam.