src/assets/text_content.py

TITLE = """<h1 align="center" id="space-title"> 🏆 Multimodal CLEM Leaderboard</h1>"""

REPO = "https://raw.githubusercontent.com/clembench/clembench-runs/main/"
HF_REPO = "colab-potsdam/clem-leaderboard"

TEXT_NAME = "🥇 CLEM Leaderboard"
MULTIMODAL_NAME = "🥇 Multimodal CLEM Leaderboard"

INTRODUCTION_TEXT = """
<h6 align="center">

The CLEM Leaderboard aims to track, rank and evaluate current cLLMs (chat-optimized Large Language Models) with the suggested pronounciation “clems”. 

The multimodal benchmark is described in [Two Giraffes in a Dirt Field: Using Game Play to Investigate Situation Modelling in Large Multimodal Models](https://arxiv.org/abs/2406.14035)

The original benchmarking approach for text-only models is described in [Clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents](https://aclanthology.org/2023.emnlp-main.689.pdf).

Source code for benchmarking "clems" is available here: [Clembench](https://github.com/clembench/clembench)

All generated files and results from the benchmark runs are available here: [clembench-runs](https://github.com/clembench/clembench-runs) </h6>
"""

CLEMSCORE_TEXT = """
The <i>clemscore</i> combines a score representing the overall ability to just follow the game instructions (separately scored in field <i>Played</i>) and the quality of the play in attempt where instructions were followed (field <i>Quality Scores</i>). For details about the games / interaction settings, and for results on older versions of the benchmark, see the tab <i>Versions and Details</i>.
"""

SHORT_NAMES = {
    "t0.0": "",
    "claude-v1.3": "cl-1.3",
    "claude-2": "cl-2",
    "claude-2.1": "cl-2.1",
    "claude-instant-1.2": "cl-ins-1.2",
    "gpt-3.5-turbo-0613": "3.5-0613",
    "gpt-3.5-turbo-1106": "3.5-1106",
    "gpt-4-0613": "4-0613",
    "gpt-4-1106-preview": "4-1106",
    "gpt-4-0314": "4-0314",
    "gpt-4": "4",
    "text-davinci-003": "3",
    "luminous-supreme": "lm",
    "koala-13b": "k-13b",
    "falcon-40b": "fal-40b",
    "falcon-7b-instruct": "fal-7b",
    "falcon-40b-instruct": "flc-i-40b",
    "oasst-12b": "oas-12b",
    "oasst-sft-4-pythia-12b-epoch-3.5": "ost-12b",
    "vicuna-13b": "vic-13b",
    "vicuna-33b-v1.3": "vic-33b-v1.3",
    "sheep-duck-llama-2-70b-v1.1": "sd-l2-70b-v1.1",
    "sheep-duck-llama-2-13b": "sd-l2-13b",
    "WizardLM-70b-v1.0": "w-70b-v1.0",
    "CodeLlama-34b-Instruct-hf": "cl-34b",
    "command": "com",
    "Mistral-7B-Instruct-v0.1": "m-i-7b-v0.1",
    "Wizard-Vicuna-13B-Uncensored-HF": "vcn-13b",
    "llama-2-13b-chat-hf": "l2-13b",
    "llama-2-70b-chat-hf": "l2-70b",
    "llama-2-7b-chat-hf": "l2-7b",
    "koala-13B-HF": "k-13b",
    "WizardLM-13b-v1.2": "w-13b-v1.2",
    "vicuna-7b-v1.5": "vic-7b-v1.5",
    "vicuna-13b-v1.5": "vic-13b-v1.5",
    "gpt4all-13b-snoozy": "g4a-13b-s",
    "zephyr-7b-alpha": "z-7b-a",
    "zephyr-7b-beta": "z-7b-b"
}