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M725/cortexa-write-scorer (the worker-side TF-IDF/lexical stub).
Takes MiniLM text features (384-d) + the 4 Write pillar scores and
emits a creator-vernacular phrase chain about the draft:"first line hooks | ending sticks"
"tight middle | shareable"
"wall of text | no reason to read"
"drags | no payoff"| file | purpose |
|---|---|
student_int8.onnx | TinyTransformer decoder, 4 layers / 256-dim / 4 heads, INT8 dynamic-quantized. 6.8 MB. |
tokenizer.json | Whole-phrase tokenizer (vocab ~120; specials <pad>, <bos>, <eos>, <sep>). |
config.json | Encoder dim (384), pillar names, vocab size, special-token ids. |
inputs:
encoder_feats (1, 384) float32 # sentence-transformers/all-MiniLM-L6-v2 mean-pooled, L2-normalized
scores (1, 4) float32 # [read_likelihood, hold, structure, score] in [0,1]
scores_present (1,) float32 # 1.0 anchored, 0.0 fast-mode
input_ids (1, T) int64
outputs:
logits (1, T, V) float32research/distill_students/train_write.py in the app repo. Teacher
is score_write_for_rules() — the Python port of the cortexa-proxy
worker's deterministic TF-IDF write scorer.