embedding.yaml

# Soup template: Embedding Model Fine-tuning
# Fine-tune a sentence embedding model (BGE, E5, GTE, etc.)
#
# Data format (JSONL) — contrastive pairs:
#   {"anchor": "What is Python?", "positive": "Python is a programming language."}
#
# Data format (JSONL) — triplets:
#   {"anchor": "query", "positive": "relevant doc", "negative": "unrelated doc"}

base: BAAI/bge-base-en-v1.5
task: embedding
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/embedding_train.jsonl
  format: embedding
  val_split: 0.1
  max_length: 512

training:
  epochs: 3
  lr: 2e-5
  batch_size: auto
  gradient_accumulation_steps: 4
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: none
  embedding_loss: contrastive
  embedding_margin: 0.5
  embedding_pooling: mean

output: ./output_embedding