Pre-trained model checkpoints
Each subdirectory holds the minimum artifacts needed to run the matching script in evaluation/, plus metadata for reproducibility. Training-time performance diagnostics (ROC/PR curves, confusion matrices, Optuna trial tables, etc.) are not included — regenerate them with --save-diagnostics when re-training.
nn_one_hot/
| File | Purpose |
|---|---|
model_weights/one_hot_model_full_model.h5 |
Primary Keras checkpoint |
model_weights/one_hot_model_weights.h5 |
Weights-only fallback |
model_weights/one_hot_model_architecture.json |
Architecture fallback (with weights H5) |
one_hot_nn_validation_temperature.csv |
Temperature scaling for inference |
one_hot_nn_validation_validation_diagnostics_summary.csv |
F1-optimal classification threshold |
model_parameters.json |
Training hyperparameters and target_length |
best_hyperparameters.csv |
Optuna best trial (architecture fallback) |
random_seed.txt |
Random seed used during training |
Evaluator: evaluation/evaluate_nn_one_hot.py
rf_one_hot/
| File | Purpose |
|---|---|
one_hot_rf_model.joblib |
sklearn RandomForest + embedded target_length |
one_hot_rf_validation_temperature.csv |
Temperature scaling |
one_hot_rf_validation_validation_diagnostics_summary.csv |
F1-optimal threshold |
model_parameters.json |
Training metadata |
best_hyperparameters.csv |
Optuna best trial |
random_seed.txt |
Random seed |
Evaluator: evaluation/evaluate_rf_one_hot.py
nn_mean_pertoken_esm/
Per head (mean and per_token):
| File pattern | Purpose |
|---|---|
{head}_embeddings_model_weights.h5 |
Keras softmax head |
{head}_embeddings_scaler.npy |
StandardScaler used at training |
{head}_embeddings_pca.npy |
PCA for per-token head only |
{head}_embeddings_validation_temperature.csv |
Temperature scaling |
{head}_embeddings_validation_validation_diagnostics_summary.csv |
F1-optimal threshold |
{head}_embeddings_hyperparameters.csv |
Learning rate, L1, epochs |
Plus model_parameters.json and random_seed.txt.
Evaluator: evaluation/evaluate_nn_mean_pertoken_esm.py — also requires .pt embedding caches (not shipped; see extract_embeddings/).
rf_mean_pertoken_esm/
Per head (mean and per_token):
| File pattern | Purpose |
|---|---|
{head}_embeddings_rf_model.joblib |
sklearn RandomForest |
{head}_embeddings_scaler.joblib |
Fitted scaler |
{head}_embeddings_pca.joblib |
PCA for per-token head only |
{head}_embeddings_validation_temperature.csv |
Temperature scaling |
{head}_embeddings_validation_validation_diagnostics_summary.csv |
F1-optimal threshold |
{head}_embeddings_hyperparameters.csv |
RF hyperparameters from Optuna |
Plus model_parameters.json and random_seed.txt.
Evaluator: evaluation/evaluate_rf_mean_pertoken_esm.py — also requires .pt embedding caches.
Composition logistic regression
No bundle is shipped here. Train with models/lr_sequence_composition_baseline.py, then point evaluation/evaluate_lr_sequence_composition.py at the output directory (needs composition_lr_model.joblib + calibration CSVs).