DCE / config /train_config.yaml
That guy James Bond :)
Deploy Medical Intent Escalation API
af61b34
Raw
History Blame Contribute Delete
2.12 kB
# DCE Intent BioClinicalBERT Training Configuration
# ============================================
# Version: 2.0.0
# Last Updated: 2026-02-08
# Model Configuration
# Using BioClinicalBERT for medical domain accuracy
# Reduced max_length to 48 to maintain low latency
model:
base_model: "emilyalsentzer/Bio_ClinicalBERT"
max_length: 48 # Reduced from 64 to compensate for larger model
num_labels: null # Auto-detected from dataset
# Training Hyperparameters
training:
learning_rate: 3.0e-5
epochs: 15
batch_size: 16
eval_batch_size: 32
warmup_ratio: 0.1
weight_decay: 0.01
gradient_accumulation_steps: 1
max_grad_norm: 1.0
# Optimizer
optimizer:
type: "adamw"
betas: [0.9, 0.999]
eps: 1.0e-8
# Scheduler
scheduler:
type: "linear" # linear warmup then linear decay
# Data Paths
data:
train_path: "data/examples/train.jsonl"
val_path: "data/examples/val.jsonl"
test_path: "data/examples/test.jsonl"
text_column: "text"
label_column: "label"
# Output Configuration
output:
base_dir: "artifacts"
pytorch_dir: "artifacts/pytorch"
save_steps: 30
eval_steps: 15
logging_steps: 15
save_total_limit: 2
# Reproducibility
seed: 42
deterministic: true
# Logging
logging:
level: "INFO"
log_to_file: true
log_file: "artifacts/logs/training.log"
# Intent Schema (Tier 1 - Required)
intent_schema:
tier1:
- "ESCALATION" # Urgent medical escalation
- "APPOINTMENT" # Schedule/reschedule appointments
- "MEDICATION" # Medication-related queries
- "SYMPTOM_CHECK" # Symptom assessment
- "GENERAL_INQUIRY" # General health questions
- "BILLING" # Billing/insurance questions
- "OTHER" # Fallback category
tier2: # Optional sub-categories
ESCALATION:
- "CHEST_PAIN"
- "BREATHING_DIFFICULTY"
- "SEVERE_BLEEDING"
- "LOSS_OF_CONSCIOUSNESS"
- "OTHER_EMERGENCY"
# Evaluation Metrics
metrics:
primary: "macro_f1"
track:
- "accuracy"
- "macro_f1"
- "per_class_recall"
escalation_label: "ESCALATION" # Label to track recall for