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config.yaml

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+# ============================================================
+# miRNA-target prediction model — default training configuration
+# ============================================================
+# This file controls all hyperparameters for the training pipeline.
+# Modify this file to tune parameters without changing the code.
+#
+# Usage:
+#   python insect_mirna_target/training/train.py --config insect_mirna_target/configs/default.yaml
+#   python insect_mirna_target/training/train.py --config insect_mirna_target/configs/default.yaml --fast-dev-run
+# ============================================================
+
+# ---- Model architecture ----
+model:
+  freeze_backbone: true       # Whether to freeze the RNA-FM backbone (Phase 1 frozen, Phase 2 unfrozen)
+  cross_attn_heads: 8         # Number of Cross-Attention heads (8 heads x 80 dim = 640)
+  cross_attn_layers: 2        # Number of stacked Cross-Attention layers (Mimosa uses 16, our lightweight version uses 2)
+  classifier_hidden:          # MLP classifier head hidden layer dimensions
+    - 256                     # First layer: 640->256
+    - 64                      # Second layer: 256->64, final output 64->1
+  dropout: 0.3                # Dropout rate (0.2-0.5 range, 0.3 is a common default)
+
+# ---- Data loading ----
+data:
+  data_dir: "insect_mirna_target/data/training"  # Training data directory (contains train.csv, val.csv, test.csv)
+  batch_size: 128             # Batch size (128-256 recommended, reduce to 64 if GPU memory is insufficient)
+  num_workers: 8              # DataLoader worker processes (typically set to 1/4 of CPU cores)
+  pin_memory: true            # Pinned memory (recommended true for GPU training, accelerates CPU->GPU data transfer)
+
+# ---- Training hyperparameters ----
+training:
+  lr: 1.0e-4                  # Base learning rate (used for classifier head; backbone is multiplied by 0.01)
+  weight_decay: 1.0e-5        # L2 regularization coefficient (prevents overfitting, 1e-5 to 1e-4 range)
+  scheduler: "cosine"         # Learning rate scheduler: cosine | onecycle | none
+  max_epochs: 30              # Maximum training epochs (typically 20-50, combined with early stopping)
+  gradient_clip_val: 1.0      # Gradient clipping threshold (prevents gradient explosion, 1.0 is standard)
+  accumulate_grad_batches: 1  # Gradient accumulation steps (set to 2-4 to simulate larger batch if GPU memory is insufficient)
+  precision: "16-mixed"       # Mixed precision training (reduces GPU memory, speeds up computation, with negligible precision loss)
+
+# ---- Progressive unfreezing ----
+unfreezing:
+  enabled: false              # Whether to enable progressive unfreezing (Phase 1 set false, Phase 2 set true)
+  unfreeze_at_epoch: 5        # Epoch at which to unfreeze the first layer (typically after classifier head converges)
+  num_layers: 3               # Total number of top RNA-FM layers to unfreeze (3 layers = layers 10, 11, 12)
+  unfreeze_interval: 3        # Unfreeze the next layer every N epochs (epoch 5->L12, 8->L11, 11->L10)
+  warmup_epochs: 1            # Number of backbone lr warmup epochs after each unfreeze (lr linearly ramps up from 1/10)
+
+# ---- Trainer configuration ----
+trainer:
+  accelerator: "gpu"          # Accelerator type: gpu | cpu
+  devices: 2                  # Number of GPUs (this machine has 2x NVIDIA L20)
+  strategy: "ddp"             # Distributed strategy: ddp (data parallel) | fsdp (fully sharded data parallel)
+
+# ---- Checkpoint management ----
+checkpointing:
+  monitor: "val_auroc"        # Metric to monitor (criterion for selecting the best model)
+  mode: "max"                 # max=higher is better (auroc), min=lower is better (loss)
+  save_top_k: 3               # Save top-K best models (disk space limited, keep only 3)
+  save_last: true             # Whether to additionally save the last epoch's checkpoint
+  dirpath: "checkpoints/"     # Checkpoint save directory
+
+# ---- Logging configuration ----
+logging:
+  logger: "tensorboard"       # Logging backend: tensorboard (no login required, view locally)
+  log_dir: "lightning_logs/"  # TensorBoard log directory
+  log_every_n_steps: 50       # Log every N steps (too frequent will slow down training)
+
+# ---- Early stopping ----
+early_stopping:
+  enabled: true               # Whether to enable early stopping (prevents overfitting, recommended)
+  monitor: "val_loss"         # Metric to monitor
+  patience: 10                # Stop after N consecutive epochs without improvement (progressive unfreezing needs more recovery time)
+  mode: "min"                 # min=lower is better
+
+# ---- Random seed ----
+seed: 42                      # Global random seed (ensures experiment reproducibility)