Upload main.py

main.py

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+import pandas as pd
+from huggingface_hub import HfApi, login
+from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
+from datasets import Dataset
+
+# Log in to Hugging Face
+login()  # Pastikan kamu sudah login ke akun Hugging Face
+
+# Load dataset
+data = {
+    'text': ["I love programming!", "I hate bugs.", "Python is great.", "I dislike syntax errors."],
+    'label': [1, 0, 1, 0]  # 1 untuk sentimen positif, 0 untuk sentimen negatif
+}
+df = pd.DataFrame(data)
+
+# Convert to Hugging Face dataset
+dataset = Dataset.from_pandas(df)
+
+# Split the dataset into training and evaluation sets
+train_test_split = dataset.train_test_split(test_size=0.2)  # 80% train, 20% eval
+train_dataset = train_test_split['train']
+eval_dataset = train_test_split['test']
+
+# Tokenize the text
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+
+def tokenize_function(examples):
+    return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+# Tokenisasi data training dan evaluasi
+tokenized_train = train_dataset.map(tokenize_function, batched=True)
+tokenized_eval = eval_dataset.map(tokenize_function, batched=True)
+
+# Define model
+model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
+
+# Set training arguments
+training_args = TrainingArguments(
+    output_dir='./results',
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=2,
+    num_train_epochs=3,
+    weight_decay=0.01,
+)
+
+# Train the model
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_train,
+    eval_dataset=tokenized_eval,  # Menyediakan dataset evaluasi di sini
+)
+
+trainer.train()
+
+# Save the model
+model.save_pretrained('./mytest-model')
+tokenizer.save_pretrained('./mytest-model')
+
+# Define model ID
+model_id = "aslan-asilon3/mytest-model"
+api = HfApi()
+
+# Create a new repo on Hugging Face if it doesn't already exist
+try:
+    api.create_repo(repo_id=model_id)
+except Exception as e:
+    print(f"Repo mungkin sudah ada: {e}")
+
+# Upload model dan tokenizer
+model.push_to_hub(model_id)
+tokenizer.push_to_hub(model_id)
+
+print(f"Model berhasil diunggah ke Hugging Face: {model_id}")