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6784fa4 | 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 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | #!/usr/bin/env python3
"""
Layer 2: Baseline Evaluation, GPU Fine-Tuning & Dynamics Optimization for ModernBERT-base
Master Golden Dataset: /opt/vox/sandbox/datasets/memory_scope_golden_v1.json (22,006 samples)
"""
import os
import sys
import json
import time
import torch
import numpy as np
import pandas as pd
from datasets import Dataset
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
Trainer,
TrainingArguments,
DataCollatorWithPadding,
)
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, classification_report
from sklearn.model_selection import train_test_split
GOLDEN_DATASET_PATH = "/opt/vox/sandbox/datasets/memory_scope_golden_v1.json"
BASE_MODEL_NAME = "answerdotai/ModernBERT-base"
OUTPUT_DIR = "/opt/vox/sandbox/artifacts/modernbert_scope_final"
RESULTS_DIR = "/opt/vox/sandbox/results"
os.makedirs(OUTPUT_DIR, exist_ok=True)
os.makedirs(RESULTS_DIR, exist_ok=True)
SCOPE_MAP = {"ChitChat": 0, "User": 1, "Domain": 2, "Temporal": 3}
ID_TO_SCOPE = {0: "ChitChat", 1: "User", 2: "Domain", 3: "Temporal"}
def compute_metrics(eval_pred):
logits, labels = eval_pred
preds = np.argmax(logits, axis=1)
precision, recall, f1, _ = precision_recall_fscore_support(
labels, preds, average="macro", zero_division=0
)
acc = accuracy_score(labels, preds)
_, class_recall, _, _ = precision_recall_fscore_support(
labels, preds, average=None, labels=[0, 1, 2, 3], zero_division=0
)
return {
"accuracy": acc,
"macro_f1": f1,
"macro_precision": precision,
"macro_recall": recall,
"recall_chitchat": class_recall[0],
"recall_user": class_recall[1],
"recall_domain": class_recall[2],
"recall_temporal": class_recall[3],
}
def main():
print("=== Layer 2: Baseline Evaluation & GPU Fine-Tuning Pipeline (ModernBERT-base) ===", flush=True)
# 1. Load Master Golden Dataset
if not os.path.exists(GOLDEN_DATASET_PATH):
print(f"Error: {GOLDEN_DATASET_PATH} missing!", flush=True)
sys.exit(1)
with open(GOLDEN_DATASET_PATH, "r", encoding="utf-8") as f:
data_payload = json.load(f)
samples = data_payload["samples"]
print(f"Loaded {len(samples)} total samples from Master Golden Dataset.", flush=True)
formatted_data = [
{
"id": s["id"],
"text": s["text"],
"label": SCOPE_MAP[s["scope"]],
"language": s.get("language", "en"),
"strat_key": f"{s['scope']}_{s.get('language', 'en')}"
}
for s in samples
]
df = pd.DataFrame(formatted_data)
# 80% Train (17,604), 10% Val (2,201), 10% Test (2,201)
train_df, temp_df = train_test_split(df, test_size=0.20, random_state=42, stratify=df["strat_key"])
val_df, test_df = train_test_split(temp_df, test_size=0.50, random_state=42, stratify=temp_df["strat_key"])
print(f"Dataset Split: Train={len(train_df)}, Val={len(val_df)}, Test={len(test_df)}", flush=True)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_NAME)
def tokenize_df(df_input):
ds = Dataset.from_pandas(df_input)
ds_mapped = ds.map(
lambda x: tokenizer(x["text"], truncation=True, max_length=64, padding=False),
batched=True,
)
cols_to_keep = ["input_ids", "attention_mask", "label"]
cols_to_remove = [c for c in ds_mapped.column_names if c not in cols_to_keep]
return ds_mapped.remove_columns(cols_to_remove)
train_ds = tokenize_df(train_df)
val_ds = tokenize_df(val_df)
test_ds = tokenize_df(test_df)
# 2. Phase 2.1: Pretrained Zero-Shot Baseline Evaluation
print("\n--- Phase 2.1: Zero-Shot Baseline Evaluation of Pretrained ModernBERT-base ---", flush=True)
baseline_model = AutoModelForSequenceClassification.from_pretrained(
BASE_MODEL_NAME,
num_labels=4,
id2label=ID_TO_SCOPE,
label2id=SCOPE_MAP,
)
trainer_baseline = Trainer(
model=baseline_model,
processing_class=tokenizer,
data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
compute_metrics=compute_metrics,
)
baseline_eval = trainer_baseline.evaluate(test_ds)
print("Baseline Zero-Shot Test Evaluation Results:")
for k, v in baseline_eval.items():
print(f" - {k}: {v}", flush=True)
with open(os.path.join(RESULTS_DIR, "baseline_zero_shot_eval.json"), "w") as f:
json.dump(baseline_eval, f, indent=2)
# 3. Phase 2.2: GPU Fine-Tuning Execution on RTX 5070 Ti
print("\n--- Phase 2.2: GPU Fine-Tuning Execution on RTX 5070 Ti ---", flush=True)
model = AutoModelForSequenceClassification.from_pretrained(
BASE_MODEL_NAME,
num_labels=4,
id2label=ID_TO_SCOPE,
label2id=SCOPE_MAP,
)
training_args = TrainingArguments(
output_dir=OUTPUT_DIR,
eval_strategy="epoch",
save_strategy="no",
learning_rate=3e-5,
per_device_train_batch_size=32,
per_device_eval_batch_size=64,
num_train_epochs=3,
weight_decay=0.01,
warmup_ratio=0.10,
logging_steps=50,
bf16=True,
report_to="none",
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_ds,
eval_dataset=val_ds,
processing_class=tokenizer,
data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
compute_metrics=compute_metrics,
)
print("Starting fine-tuning training loop...", flush=True)
trainer.train()
final_model_path = os.path.join(OUTPUT_DIR, "final_pytorch_model")
trainer.save_model(final_model_path)
tokenizer.save_pretrained(final_model_path)
print(f"Fine-tuned PyTorch model saved to {final_model_path}", flush=True)
# 4. Phase 2.3: Holdout Test Set Evaluation & Gate Audit
print("\n--- Phase 2.3: Fine-Tuned Holdout Test Evaluation & Gate 2 Audit ---", flush=True)
final_eval = trainer.evaluate(test_ds)
print("\nFinal Fine-Tuned Test Metrics:")
for k, v in final_eval.items():
print(f" - {k}: {v}", flush=True)
with open(os.path.join(RESULTS_DIR, "finetuned_test_eval.json"), "w") as f:
json.dump(final_eval, f, indent=2)
test_acc = final_eval.get("eval_accuracy", 0.0)
test_f1 = final_eval.get("eval_macro_f1", 0.0)
print("\n==================================================================", flush=True)
print(f"🎯 LAYER 2 MILESTONE VERDICT: {'✅ PASSED' if (test_acc >= 0.88 and test_f1 >= 0.88) else '❌ FAILED'}", flush=True)
print(f" - Holdout Test Accuracy: {test_acc*100:.2f}% (Target: ≥88.0%)", flush=True)
print(f" - Holdout Macro F1: {test_f1:.4f} (Target: ≥0.8800)", flush=True)
print(f" - Baseline Net Gain: Accuracy +{(test_acc - baseline_eval.get('eval_accuracy', 0.0))*100:.2f}%", flush=True)
print("==================================================================", flush=True)
if __name__ == "__main__":
main()
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