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import json
import os
from dotenv import load_dotenv
from huggingface_hub import HfApi
# Load environment variables
load_dotenv()
# Configuration
HF_TOKEN = os.getenv("HF_TOKEN")
RESULTS_REPO = "hamzabouajila/results"
# Read the original results file
def read_results_file(file_path):
with open(file_path, 'r') as f:
return json.load(f)
# Fix the results format
def fix_results_format(results):
# Fix null accuracy
if results['results'].get('accuracy') is None:
results['results']['accuracy'] = 0.0 # Replace with actual accuracy if known
# Fix model_type format
results['model_type'] = results['model_type'].replace('\ud83d\udfe2 : ', '').strip()
# Convert params to integer if needed
if isinstance(results.get('params'), float):
results['params'] = int(results['params'] * 1000000) # Convert to millions
return results
# Upload to Hugging Face
def upload_to_hf(results, file_name):
api = HfApi(token=HF_TOKEN)
try:
api.upload_file(
path_or_fileobj=file_name,
path_in_repo=os.path.basename(file_name),
repo_id=RESULTS_REPO,
repo_type="dataset",
commit_message=f"Add evaluation results for {results['model']}"
)
print(f"Successfully uploaded to Hugging Face")
return True
except Exception as e:
print(f"Error uploading to Hugging Face: {str(e)}")
return False
if __name__ == "__main__":
# Original file path
original_file = "/teamspace/studios/this_studio/TunisianLeaderBoard/eval-results/tunis-ai/TunBERT_eval_request_False_float16_Original.json"
# Read and fix the results
results = read_results_file(original_file)
fixed_results = fix_results_format(results)
# Save the fixed version
fixed_file = "/teamspace/studios/this_studio/TunisianLeaderBoard/eval-results/tunis-ai/TunBERT_eval_request_False_float16_Original_fixed.json"
with open(fixed_file, 'w') as f:
json.dump(fixed_results, f, indent=2)
print(f"Fixed results saved to: {fixed_file}")
# Try to upload to Hugging Face
if HF_TOKEN:
upload_to_hf(fixed_results, fixed_file)
else:
print("No HF_TOKEN found. Skipping Hugging Face upload.")
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