import gradio as gr import pandas as pd from css_html_js import custom_css TITLE = """

🇲🇾 Malaysian Embedding Leaderboard

""" INTRODUCTION_TEXT = """ 📐 The 🇲🇾 Malaysian Embedding Leaderboard aims to track, rank and evaluate Top-k retrieval using embedding models. All notebooks at https://github.com/mesolitica/embedding-benchmarks, feel free to submit your own score at https://huggingface.co/spaces/mesolitica/Malaysian-Embedding-Leaderboard/discussions with link to the notebook. ## Dataset 📈 We evaluate models based on 2 datasets, 1. Research paper keyword `melayu` using Crossref, https://huggingface.co/datasets/mesolitica/malaysian-ultrachat/resolve/main/ultrachat-crossref-melayu-malay.jsonl 2. Epenerbitan, https://huggingface.co/datasets/mesolitica/malaysian-ultrachat/resolve/main/ultrachat-epenerbitan-malay.jsonl """ close_source = [ { 'model': 'OpenAI ADA-002', 'Crossref Melayu top-1': 0.3155939351340496, 'Crossref Melayu top-3': 0.5120996083944171, 'Crossref Melayu top-5': 0.5878100210864544, 'Crossref Melayu top-10': 0.6721558389396526, 'lom.agc.gov.my top-1': 0.19168533731640527, 'lom.agc.gov.my top-3': 0.2827981080408265, 'lom.agc.gov.my top-5': 0.322504356484939, 'lom.agc.gov.my top-10': 0.36855862584017923, } ] open_source = [ { 'model': '[llama2-embedding-600m-8k](https://huggingface.co/mesolitica/llama2-embedding-600m-8k)', 'Crossref Melayu top-1': 0.09549151521237072, 'Crossref Melayu top-3': 0.1834521538307059, 'Crossref Melayu top-5': 0.23375840947886334, 'Crossref Melayu top-10': 0.3098704689225826, 'lom.agc.gov.my top-1': 0.05215334826985312, 'lom.agc.gov.my top-3': 0.09932785660941, 'lom.agc.gov.my top-5': 0.12969878018421707, 'lom.agc.gov.my top-10': 0.1797361214836943, }, { 'model': '[llama2-embedding-1b-8k](https://huggingface.co/mesolitica/llama2-embedding-1b-8k)', 'Crossref Melayu top-1': 0.06777788934631991, 'Crossref Melayu top-3': 0.142584596847073, 'Crossref Melayu top-5': 0.18817150316296816, 'Crossref Melayu top-10': 0.25715433276433375, 'lom.agc.gov.my top-1': 0.06870799103808813, 'lom.agc.gov.my top-3': 0.1343042071197411, 'lom.agc.gov.my top-5': 0.1717699775952203, 'lom.agc.gov.my top-10': 0.23089370176748816, }, { 'model': '[llama2-embedding-600m-8k-contrastive](https://huggingface.co/mesolitica/llama2-embedding-600m-8k)', 'Crossref Melayu top-1': 0.11015162164875991, 'Crossref Melayu top-3': 0.23707199518023897, 'Crossref Melayu top-5': 0.30916758710713926, 'Crossref Melayu top-10': 0.4196204438196606, 'lom.agc.gov.my top-1': 0.05414488424197162, 'lom.agc.gov.my top-3': 0.11600697037590242, 'lom.agc.gov.my top-5': 0.16143888473985563, 'lom.agc.gov.my top-10': 0.23823749066467514, }, { 'model': '[llama2-embedding-1b-8k-contrastive](https://huggingface.co/mesolitica/llama2-embedding-1b-8k)', 'Crossref Melayu top-1': 0.16306858118284967, 'Crossref Melayu top-3': 0.32824580781202933, 'Crossref Melayu top-5': 0.41409780098403454, 'Crossref Melayu top-10': 0.5312782407872276, 'lom.agc.gov.my top-1': 0.08824993776450087, 'lom.agc.gov.my top-3': 0.17836694050286284, 'lom.agc.gov.my top-5': 0.2399800846402788, 'lom.agc.gov.my top-10': 0.3343291013193926, }, ] 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()