SignalMod / configs /expert_training.yaml
Mirae Kang
feat: implement new models and improve UI, #23
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# Phase 5: Expert Aggressive — Toxic-BERT (head-only) + bottleneck LR + tuned threshold
# Goals: F1-toxic > 0.75, |Train F1 - Test F1| < 0.05
pipeline:
random_state: 42
test_size: 0.2
val_size: 0.15
cv_folds: 5
max_train_test_gap: 0.05
data:
raw_path: data/raw/youtoxic_english_1000.csv
target_binary: IsToxic
text_column: Text
augmentation:
enabled: true
strategy: back_translation
source_lang: en
pivot_lang: de # higher diversity vs Spanish pivot
min_words: 3
max_words: 60
rate_limit_every: 50
rate_limit_sleep_sec: 1.0
dedup:
enabled: true
cosine_threshold: 0.95
embedding_model: sentence-transformers/all-MiniLM-L6-v2
transformer:
model_id: unitary/toxic-bert
max_length: 128
freeze_mode: head_only # entire backbone frozen; classifier only
learning_rate: 2.0e-5
weight_decay: 0.01
max_epochs: 10
batch_size: 8
warmup_ratio: 0.1
head_dropout: 0.3
label_smoothing: 0.05
early_stopping:
patience: 3
metric: f1_toxic
gap_stop_enabled: false
max_train_val_gap: 0.05
gap_check_min_epoch: 2
metric_for_best: f1_toxic
threshold_tuning:
enabled: true
metric: f1_toxic
min_threshold: 0.05
max_threshold: 0.95
step: 0.01
logistic_regression:
C: 0.05
max_iter: 2000
class_weight: balanced
solver: lbfgs
gap_search:
enabled: true
max_gap: 0.05
use_original_train_for_gap: true
param_grid:
- {C: 0.05, max_features: 250, min_df: 3}
- {C: 0.03, max_features: 250, min_df: 5}
- {C: 0.02, max_features: 250, min_df: 5}
- {C: 0.01, max_features: 250, min_df: 8}
- {C: 0.005, max_features: 250, min_df: 10}
tfidf:
max_features: 250
ngram_range: [1, 2]
sublinear_tf: true
min_df: 3
ensemble:
method: soft_vote
bert_weight: 0.7
lr_weight: 0.3
threshold_tuning:
enabled: true
metric: f1_toxic
output:
transformer_dir: models/expert_toxic_bert
lr_path: models/expert_lr_tfidf.joblib
ensemble_meta_path: models/expert_ensemble_meta.json
reports_dir: reports/expert