text,label
"This product is amazing! Best purchase ever.",positive
"Terrible quality. Complete waste of money.",negative
"Great service and fast delivery.",positive
"Broken on arrival. Very disappointed.",negative
"Exceeded my expectations!",positive
"Would not recommend to anyone.",negative

aitraining text-classification \
  --model bert-base-uncased \
  --data-path train.csv \
  --text-column text \
  --target-column label \
  --output-dir ./my-sentiment-model \
  --epochs 3

Loading model bert-base-uncased...
Processing data...
Training started...
Epoch 1/3: loss=0.65, accuracy=0.67
Epoch 2/3: loss=0.42, accuracy=0.83
Epoch 3/3: loss=0.31, accuracy=0.92
Model saved to ./my-sentiment-model

aitraining predict \
  --model-path ./my-sentiment-model \
  --text "This is absolutely fantastic!"

Prediction: positive (confidence: 0.94)

from aitraining import TextClassification
import pandas as pd

# Create training data
data = {
    'text': [
        "This product is amazing! Best purchase ever.",
        "Terrible quality. Complete waste of money.",
        "Great service and fast delivery.",
        "Broken on arrival. Very disappointed.",
        "Exceeded my expectations!",
        "Would not recommend to anyone."
    ],
    'label': [
        'positive', 'negative', 'positive',
        'negative', 'positive', 'negative'
    ]
}

# Save as CSV
df = pd.DataFrame(data)
df.to_csv('train.csv', index=False)

# Configure training
trainer = TextClassification(
    model="bert-base-uncased",
    data_path="train.csv",
    text_column="text",
    target_column="label",
    output_dir="./my-sentiment-model",
    epochs=3,
    batch_size=8
)

# Start training
print("Starting training...")
trainer.train()

# Test the model
test_texts = [
    "This is absolutely fantastic!",
    "Complete waste of time and money."
]

predictions = trainer.predict(test_texts)
for text, pred in zip(test_texts, predictions):
    print(f"Text: {text}")
    print(f"Prediction: {pred['label']} (confidence: {pred['score']:.2f})\n")

python train_sentiment.py

from aitraining import load_model

# Load your trained model
model = load_model("./my-sentiment-model")

# Make predictions
result = model.predict("Best product ever!")
print(result)  # {'label': 'positive', 'score': 0.95}