Download src/envs.py from silma-ai/Arabic-LLM-Leaderboard: direct link, hf CLI and curl.
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https://huggingface.co/spaces/silma-ai/Arabic-LLM-Leaderboard/resolve/454de08c5312f3435199e94d4657dd45da6c4399/src/envs.py
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hf download hf://spaces/silma-ai/Arabic-LLM-Leaderboard@454de08c5312f3435199e94d4657dd45da6c4399/src/envs.py
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curl -L -o envs.py https://huggingface.co/spaces/silma-ai/Arabic-LLM-Leaderboard/resolve/454de08c5312f3435199e94d4657dd45da6c4399/src/envs.py
889 Bytes
| import os | |
| from huggingface_hub import HfApi | |
| # Info to change for your repository | |
| # ---------------------------------- | |
| TOKEN = os.environ.get("HF_TOKEN") # A read/write token for your org | |
| OWNER = "silma-ai" # Change to your org - don't forget to create a results and request dataset, with the correct format! | |
| # ---------------------------------- | |
| REPO_ID = f"{OWNER}/Arabic-Broad-Leaderboard" | |
| QUEUE_REPO = f"{OWNER}/Arabic-Broad-Leaderboard" | |
| RESULTS_REPO = f"{OWNER}/Arabic-Broad-Leaderboard" | |
| # If you setup a cache later, just change HF_HOME | |
| CACHE_PATH=os.getenv("HF_HOME", ".") | |
| # Local caches | |
| EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue") | |
| EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results") | |
| EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk") | |
| EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk") | |
| API = HfApi(token=TOKEN) | |