Upload folder using huggingface_hub

upload_model.py

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+
+# upload_model.py
+
+import tensorflow as tf
+from transformers import TFAutoModelForSequenceClassification, AutoTokenizer
+
+# --- 1. EDIT THESE VARIABLES ---
+# The base model architecture you used, e.g., 'bert-base-uncased', 'distilbert-base-cased'
+MODEL_ARCH = 'bert-base-uncased' 
+# Path to your saved .h5 file
+H5_WEIGHTS_PATH = './my_model.h5' 
+# The name for your new repository on the Hugging Face Hub
+HUB_REPO_ID = "your-hf-username/your-model-name" 
+# The number of labels your model was trained to predict
+NUM_LABELS = 2 # Example for binary classification
+
+# --- 2. LOAD THE TRANSFORMERS MODEL AND TOKENIZER ---
+print("Loading base tokenizer and model architecture...")
+# Load the tokenizer that corresponds to your model architecture
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ARCH)
+
+# Load the model architecture, specifying it's a TensorFlow model and the number of classes
+model = TFAutoModelForSequenceClassification.from_pretrained(MODEL_ARCH, num_labels=NUM_LABELS, from_pt=True)
+
+# --- 3. LOAD WEIGHTS FROM YOUR .H5 FILE ---
+# NOTE: The model must be "built" before loading weights. 
+# A simple way to do this is to pass a dummy input through it.
+dummy_input = tokenizer("This is a dummy sentence.", return_tensors="tf")
+_ = model(dummy_input) # The output of this call is not needed
+
+print(f"Loading weights from {H5_WEIGHTS_PATH}...")
+model.load_weights(H5_WEIGHTS_PATH)
+print("Weights loaded successfully.")
+
+# --- 4. PUSH THE MODEL AND TOKENIZER TO THE HUB ---
+print(f"Uploading model and tokenizer to {HUB_REPO_ID}...")
+# This command will create the repository if it doesn't exist
+model.push_to_hub(HUB_REPO_ID)
+tokenizer.push_to_hub(HUB_REPO_ID)
+
+print("All done! Your model is now on the Hugging Face Hub.")