upload_model.py

# pip install --upgrade huggingface_hub
from huggingface_hub import login, create_repo, upload_folder, HfApi

# === EDIT THESE IF NEEDED ===
REPO_ID = "mdg-nlp/updated-time-ner-bert-base-cased"  # target HF repo
LOCAL_OUTPUTS = r"C:\Users\ASUS_GAMER\Desktop\time-ner\updated-time-ner-bert-base-cased\outputs"  # local folder to upload
USE_DATASETS_SECTION = False  # set True if this repo should be under "Datasets"
# ============================

repo_type = "dataset" if USE_DATASETS_SECTION else "model"

# 1) Auth (or use: login(token="hf_..."))
login()

# 2) Ensure repo exists
create_repo(repo_id=REPO_ID, repo_type=repo_type, exist_ok=True)

# 3) Force re-upload LOCAL_OUTPUTS -> repo path "outputs"
#    delete=True removes remote /outputs first so it exactly mirrors your local folder.
print("Uploading. This may take a while for large .safetensors/.bin files...")
upload_folder(
    folder_path=LOCAL_OUTPUTS,
    repo_id=REPO_ID,
    repo_type=repo_type,
    path_in_repo="outputs",
    commit_message="Force re-upload of outputs (weights + tokenizer)",
    delete=True,
    # If you want to limit what gets uploaded, uncomment:
    # allow_patterns=["*.safetensors", "*.bin", "*.json", "*.txt", "*.model", "*.vocab", "*.merges"],
    # ignore_patterns=["checkpoint*/", "runs/", "logs/", "*.tmp"],
)

print("✅ Upload complete.")

# 4) Verify by listing files now on the repo under /outputs
api = HfApi()
files = api.list_repo_files(REPO_ID, repo_type=repo_type, revision="main")
outputs_files = [f for f in files if f.startswith("outputs/")]

print("\nRemote files under /outputs:")
for f in outputs_files:
    print(" -", f)

if not outputs_files:
    print("⚠️ No files found under /outputs on the remote. Double-check LOCAL_OUTPUTS path and permissions.")