# Native-Bird image-text alignment (AfriCaption / Flickr8k)
# All paths are relative to the project root.
seed: 42
data:
  dir: data/processed            # train/val/test parquet from scripts/04_split.py
  image_dir: data/raw/flickr8k/Flicker8k_Dataset
  langs: [eng, ibo, hau, yor]    # captions used for training (each image contributes one pair per kept language)
  max_text_len: 64
  subset: null                   # int -> use only first N images per split (smoke tests)

image_encoder:
  # Any open_clip model or HF CLIP/SigLIP checkpoint. Frozen; features are cached to disk once.
  backend: hf                    # hf | open_clip
  name: google/siglip-base-patch16-224
  cache_dir: data/features       # cached image features: <cache_dir>/<safe_name>/<split>.pt

text_encoder:
  # Pick the arm. Each arm is a sentence-transformers model id.
  arm: native_bird               # native_bird | embeddinggemma2 | embeddinggemma1 | labse | custom
  arms:
    native_bird:     {name: Modularcomputing/Native-Bird, prompt: ""}
    embeddinggemma2: {name: google/embeddinggemma-2, prompt: "task: search result | query: ", text_only: true}
    embeddinggemma1: {name: google/embeddinggemma-300m, prompt: "task: search result | query: "}
    labse:           {name: sentence-transformers/LaBSE, prompt: ""}
    custom:          {name: null, prompt: ""}
  unfreeze_last_n_layers: 0      # 0 = fully frozen (text embeddings are cached); >0 fine-tunes the top-N transformer layers
  cache_dir: data/features

model:
  proj_dim: 512
  proj_hidden: 0                 # 0 = linear projection; >0 = 2-layer MLP with this hidden size
  dropout: 0.1
  init_logit_scale: 2.659        # ln(1/0.07), CLIP default; learnable, clamped at ln(100)

train:
  epochs: 20
  batch_size: 256                # contrastive batch; bigger = more negatives
  lr: 1.0e-3                     # projection heads
  text_lr: 1.0e-5                # unfrozen text layers (only if unfreeze_last_n_layers > 0)
  weight_decay: 0.01
  warmup_steps: 100
  same_image_negatives: mask     # mask: captions of the same image in other languages are not treated as negatives
  device: auto                   # auto | cuda | cpu
  num_workers: 4
  out_dir: runs/${text_encoder.arm}
  eval_every: 1

eval:
  split: test
  ks: [1, 5, 10]
  langs: [eng, ibo, hau, yor]