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Download README.md from TIGER-Lab/MMEB-Leaderboard: direct link, hf CLI and curl.
- Browser
- Download file 1.46 kB
-
https://huggingface.co/spaces/TIGER-Lab/MMEB-Leaderboard/resolve/refs%2Fpr%2F25/README.md
- Command line
-
hf download hf://spaces/TIGER-Lab/MMEB-Leaderboard@refs/pr/25/README.md
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curl -L -o README.md https://huggingface.co/spaces/TIGER-Lab/MMEB-Leaderboard/resolve/refs%2Fpr%2F25/README.md
1.46 kB
metadata
title: MMEB Leaderboard
emoji: 📊
colorFrom: green
colorTo: indigo
sdk: gradio
app_file: app.py
pinned: false
license: apache-2.0
short_description: The massive multimodal embedding benchmark
sdk_version: 5.9.1
Start the configuration
Most of the variables to change for a default leaderboard are in src/env.py (replace the path for your leaderboard) and src/about.py (for tasks).
Results files should have the following format and be stored as json files:
{
"config": {
"model_dtype": "torch.float16", # or torch.bfloat16 or 8bit or 4bit
"model_name": "path of the model on the hub: org/model",
"model_sha": "revision on the hub",
},
"results": {
"task_name": {
"metric_name": score,
},
"task_name2": {
"metric_name": score,
}
}
}
Request files are created automatically by this tool.
If you encounter problem on the space, don't hesitate to restart it to remove the create eval-queue, eval-queue-bk, eval-results and eval-results-bk created folder.
Code logic for more complex edits
You'll find
- the main table' columns names and properties in
src/display/utils.py - the logic to read all results and request files, then convert them in dataframe lines, in
src/leaderboard/read_evals.py, andsrc/populate.py - the logic to allow or filter submissions in
src/submission/submit.pyandsrc/submission/check_validity.py