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import os | |
from huggingface_hub import HfApi | |
# replace this with our token | |
TOKEN = os.environ.get("HF_TOKEN", None) | |
OWNER = "vectara" | |
REPO_ID = f"{OWNER}/leaderboard" | |
QUEUE_REPO = f"{OWNER}/requests" | |
RESULTS_REPO = f"{OWNER}/results" | |
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") | |
DEVICE = "cpu" | |
API = HfApi(token=TOKEN) | |
DATASET_PATH = "src/datasets/leaderboard_dataset.csv" | |
SAMPLE_DATASET_PATH = "src/datasets/sample_dataset.csv" | |
HEM_PATH = 'vectara/hallucination_evaluation_model' | |
SYSTEM_PROMPT = "You are a chat bot answering questions using data. You must stick to the answers provided solely by the text in the passage provided." | |
USER_PROMPT = "You are asked the question 'Provide a concise summary of the following passage, covering the core pieces of information described': " | |