app.py

import gradio as gr
import pandas as pd
from css_html_js import custom_css

TITLE = """<h1 align="center" id="space-title">🇲🇾 Malay LLM Leaderboard</h1>"""

INTRODUCTION_TEXT = """
📐 The 🇲🇾 Malay LLM Leaderboard aims to track, rank and evaluate open LLMs on Malay tasks. All notebooks at https://github.com/mesolitica/llm-benchmarks, feel free to submit your own score at https://huggingface.co/spaces/mesolitica/malay-llm-leaderboard/discussions with link to the notebook.

## Dataset

📈 We evaluate models based on 3 datasets,

1. BM-PT3 Paper 1, contains 54 questions, https://github.com/mesolitica/malaysian-dataset/tree/master/llm-benchmark/BM-pt3
- This test is for 15 years old Malaysia student, it is about reading comprehension and general knowledge for malay language.
2. Tatabahasa, contains 349 questions, https://github.com/mesolitica/malaysian-dataset/tree/master/llm-benchmark/tatabahasabm.tripod.com
- This test is general test for malay grammar.
3. Translated IndoNLI to Malay, tested on `test_expert` dataset, https://huggingface.co/datasets/mesolitica/translated-indonli
- This test is general test to language reasoning.
4. HumanEval, https://github.com/openai/human-eval
- This test is for programming language understanding.
"""

close_source = [
    {
        'model': 'gpt-4-1106-preview',
        'BM-PT3 0-shot': 51.85185185185185,
        'BM-PT3 1-shot': 66.66666666666666,
        'BM-PT3 3-shots': 55.55555555555556,
        'Tatabahasa 0-shot': 75.64469914040114,
        'Tatabahasa 1-shot': 73.63896848137536,
        'Tatabahasa 3-shots': 75.64469914040114,
    },
    {
        'model': 'gpt-3.5-turbo-0613',
        'BM-PT3 0-shot': 36.53846153846153,
        'BM-PT3 1-shot': 28.846153846153843,
        'BM-PT3 3-shots': 24.528301886792452,
        'Tatabahasa 0-shot': 59.530791788856305,
        'Tatabahasa 1-shot': 60.80691642651297,
        'Tatabahasa 3-shots': 63.03724928366762,
    },
    {
        'model': 'Antrophic Claude 2',
        'Tatabahasa 0-shot': 61,
        'Tatabahasa 3-shots': 57.8,
    },
    {
        'model': 'Antrophic Claude 1',
        'Tatabahasa 3-shots': 67,
    },
]

open_source = [
    {
        'model': '[llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf)',
        'Tatabahasa 0-shot': 24.355300859598856,
        'Tatabahasa 1-shot': 28.08022922636103,
        'Tatabahasa 3-shots': 24.641833810888254,
    },
    {
        'model': '[malaysian-llama2-7b-32k](https://huggingface.co/mesolitica/llama-7b-hf-32768-fpf)',
        'BM-PT3 0-shot': 20.37037037037037,
        'BM-PT3 1-shot': 20.37037037037037,
        'BM-PT3 3-shots': 29.629629629629626,
        'Tatabahasa 0-shot': 17.765042979942695,
        'Tatabahasa 1-shot': 24.068767908309454,
        'Tatabahasa 3-shots': 27.507163323782237,
    },
    {
        'model': '[malaysian-llama2-7b-32k-instructions](https://huggingface.co/mesolitica/malaysian-llama2-7b-32k-instructions)',
        'BM-PT3 0-shot': 35.294117647058826,
        'BM-PT3 1-shot': 21.153846153846153,
        'BM-PT3 3-shots': 28.30188679245283,
    },
    {
        'model': '[malaysian-llama2-13b-32k](https://huggingface.co/mesolitica/llama-13b-hf-32768-fpf)',
        'BM-PT3 0-shot': 33.33333333333333,
        'BM-PT3 1-shot': 20.37037037037037,
        'BM-PT3 3-shots': 31.48148148148148,
        'Tatabahasa 0-shot': 26.07449856733524,
        'Tatabahasa 1-shot': 25.214899713467048,
        'Tatabahasa 3-shots': 24.355300859598856,
    },
    {
        'model': '[malaysian-llama2-13b-32k-instructions](https://huggingface.co/mesolitica/malaysian-llama2-13b-32k-instructions)',
        'BM-PT3 0-shot': 28.57142857142857,
        'BM-PT3 1-shot': 12.244897959183673,
        'BM-PT3 3-shots': 17.307692307692307,
    },
    {
        'model': '[mistral-7b](https://huggingface.co/mistralai/Mistral-7B-v0.1)',
        'Tatabahasa 0-shot': 28.939828080229223,
        'Tatabahasa 1-shot': 34.38395415472779,
        'Tatabahasa 3-shots': 32.95128939828081,
    },
    {
        'model': '[malaysian-mistral-7b-4k](https://huggingface.co/mesolitica/mistral-7b-4096-fpf)',
        'BM-PT3 0-shot': 20.37037037037037,
        'BM-PT3 1-shot': 22.22222222222222,
        'BM-PT3 3-shots': 33.33333333333333,
        'Tatabahasa 0-shot': 21.48997134670487,
        'Tatabahasa 1-shot': 28.939828080229223,
        'Tatabahasa 3-shots': 24.641833810888254,
    },
    {
        'model': '[malaysian-mistral-7b-32k](https://huggingface.co/mesolitica/mistral-7b-32768-fpf)',
        'BM-PT3 0-shot': 16.666666666666664,
        'BM-PT3 1-shot': 16.666666666666664,
        'BM-PT3 3-shots': 25.925925925925924,
        'Tatabahasa 0-shot': 18.624641833810887,
        'Tatabahasa 1-shot': 24.355300859598856,
        'Tatabahasa 3-shots': 28.653295128939828,
    },
    {
        'model': '[malaysian-mistral-7b-32k-instructions](https://huggingface.co/mesolitica/malaysian-mistral-7b-32k-instructions)',
        'BM-PT3 0-shot': 35.18518518518518,
        'BM-PT3 1-shot': 33.33333333333333,
        'BM-PT3 3-shots': 37.03703703703704,
        'Tatabahasa 0-shot': 55.014326647564474,
        'Tatabahasa 1-shot': 42.693409742120345,
        'Tatabahasa 3-shots': 33.33333333333333,
    },
    {
        'model': '[aisingapore/sealion3b](https://huggingface.co/aisingapore/sealion3b)',
        'BM-PT3 0-shot': 20.37037037037037,
        'BM-PT3 1-shot': 25.925925925925924,
        'BM-PT3 3-shots': 31.48148148148148,
        'Tatabahasa 0-shot': 21.776504297994272,
        'Tatabahasa 1-shot': 21.776504297994272,
        'Tatabahasa 3-shots': 24.641833810888254,
    },
    {
        'model': '[aisingapore/sealion7b](https://huggingface.co/aisingapore/sealion7b)',
        'BM-PT3 0-shot': 20.37037037037037,
        'BM-PT3 1-shot': 24.074074074074073,
        'BM-PT3 3-shots': 33.33333333333333,
        'Tatabahasa 0-shot': 25.787965616045845,
        'Tatabahasa 1-shot': 27.507163323782237,
        'Tatabahasa 3-shots': 26.07449856733524,
    }
]

data = pd.DataFrame(close_source + open_source)

demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
    gr.DataFrame(data, datatype = 'markdown')

demo.launch()