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989d4ef | 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 | # 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]
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