File size: 3,872 Bytes
db6aa40 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | #!/bin/bash
#
# Train NFQA Model with Automatic Data Splitting
#
# This script trains the NFQA classification model using a single combined
# dataset that will be automatically split into train/val/test sets.
#
# Usage:
# bash run_training_auto.sh
#
# Or with custom parameters:
# bash run_training_auto.sh --epochs 15 --batch-size 32
#
set -e # Exit on error
# Default paths
INPUT_FILE="../output/webfaq_nfqa_combined_highquality.jsonl"
OUTPUT_DIR="../output/training/nfqa_model_auto"
# Default training parameters
MODEL_NAME="xlm-roberta-base"
EPOCHS=6
BATCH_SIZE=16
LEARNING_RATE=2e-5
MAX_LENGTH=128
WARMUP_STEPS=500
WEIGHT_DECAY=0.1
DROPOUT=0.2
TEST_SIZE=0.2
VAL_SIZE=0.1
echo "================================================================================"
echo "NFQA Model Training - Automatic Split Mode"
echo "================================================================================"
echo ""
echo "Training Configuration:"
echo " Input file: $INPUT_FILE"
echo " Output directory: $OUTPUT_DIR"
echo " Model: $MODEL_NAME"
echo " Epochs: $EPOCHS"
echo " Batch size: $BATCH_SIZE"
echo " Learning rate: $LEARNING_RATE"
echo " Max length: $MAX_LENGTH"
echo " Weight decay: $WEIGHT_DECAY"
echo " Dropout: $DROPOUT"
echo " Test split: $TEST_SIZE (20%)"
echo " Val split: $VAL_SIZE (10%)"
echo ""
echo "================================================================================"
echo ""
# Check if input file exists
if [ ! -f "$INPUT_FILE" ]; then
echo "❌ Error: Input file not found: $INPUT_FILE"
echo ""
echo "Please ensure the combined dataset exists."
echo "You can create it by running:"
echo " cd ../annotator"
echo " python combine_datasets.py"
exit 1
fi
# Create output directory
mkdir -p "$OUTPUT_DIR"
# Run training
python train_nfqa_model.py \
--input "$INPUT_FILE" \
--output-dir "$OUTPUT_DIR" \
--model-name "$MODEL_NAME" \
--epochs "$EPOCHS" \
--batch-size "$BATCH_SIZE" \
--learning-rate "$LEARNING_RATE" \
--max-length "$MAX_LENGTH" \
--warmup-steps "$WARMUP_STEPS" \
--weight-decay "$WEIGHT_DECAY" \
--dropout "$DROPOUT" \
--test-size "$TEST_SIZE" \
--val-size "$VAL_SIZE" \
"$@" # Pass any additional arguments from command line
# Check if training was successful
if [ $? -eq 0 ]; then
echo ""
echo "================================================================================"
echo "✅ Training completed successfully!"
echo "================================================================================"
echo ""
echo "Model saved to: $OUTPUT_DIR"
echo ""
echo "Generated files:"
echo " - best_model/ (best checkpoint based on validation F1)"
echo " - final_model/ (final epoch checkpoint)"
echo " - training_history.json (training metrics)"
echo " - training_curves.png (loss/accuracy/F1 plots)"
echo " - test_results.json (final test metrics)"
echo " - classification_report.txt (per-category performance)"
echo " - confusion_matrix.png (confusion matrix visualization)"
echo ""
echo "Next steps:"
echo " 1. Review training curves: $OUTPUT_DIR/training_curves.png"
echo " 2. Check test results: $OUTPUT_DIR/test_results.json"
echo " 3. Analyze confusion matrix: $OUTPUT_DIR/confusion_matrix.png"
echo " 4. Deploy model from: $OUTPUT_DIR/best_model/"
echo ""
else
echo ""
echo "================================================================================"
echo "❌ Training failed!"
echo "================================================================================"
echo ""
echo "Please check the error messages above and try again."
exit 1
fi
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