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4a0f6a5 | 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 | """Run archived BERT and RoBERTa classifiers against a dataset folder."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import pandas as pd
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModel, AutoTokenizer, DataCollatorWithPadding
REPO_ROOT = Path(__file__).resolve()
while REPO_ROOT != REPO_ROOT.parent and not (REPO_ROOT / "src").exists():
REPO_ROOT = REPO_ROOT.parent
MODELS_ROOT = REPO_ROOT / "models"
DATASET_ROOT = REPO_ROOT / "data" / "dataset"
OUTPUT_ROOT = REPO_ROOT / "outputs" / "plm"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
MODEL_SPECS = {
"bert": {
"model_dir": MODELS_ROOT / "bert-final",
"hidden_size": 768,
"intermediate": 512,
"dropout": 0.5,
},
"roberta": {
"model_dir": MODELS_ROOT / "roberta-final",
"hidden_size": 1024,
"intermediate": 512,
"dropout": 0.3,
},
}
def load_jsonl(path: Path) -> list[dict]:
rows: list[dict] = []
with path.open("r", encoding="utf-8") as handle:
for line in handle:
line = line.strip()
if line:
rows.append(json.loads(line))
return rows
class TokenDataset(torch.utils.data.Dataset):
def __init__(self, encoded: dict[str, list[int]], labels: list[int]):
self.encoded = encoded
self.labels = labels
def __len__(self) -> int:
return len(self.encoded["input_ids"])
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
item = {key: torch.tensor(value[idx]) for key, value in self.encoded.items()}
item["labels"] = torch.tensor(self.labels[idx], dtype=torch.long)
return item
class TransformerClassifier(nn.Module):
def __init__(self, base_model, hidden_size: int, intermediate: int, dropout: float, num_labels: int = 2):
super().__init__()
self.base = base_model
self.dropout = nn.Dropout(dropout)
self.intermediate = nn.Linear(hidden_size, intermediate)
self.activation = nn.ReLU()
self.classifier = nn.Linear(intermediate, num_labels)
def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
outputs = self.base(input_ids=input_ids, attention_mask=attention_mask, **kwargs)
cls = outputs.last_hidden_state[:, 0, :]
x = self.dropout(cls)
x = self.intermediate(x)
x = self.activation(x)
logits = self.classifier(x)
return type("Output", (object,), {"logits": logits})()
def build_model(model_name: str):
spec = MODEL_SPECS[model_name]
model_dir = spec["model_dir"]
config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
base_model = AutoModel.from_config(config, trust_remote_code=True)
meta_path = model_dir / "model_meta.json"
meta = {}
if meta_path.exists():
meta = json.loads(meta_path.read_text(encoding="utf-8"))
classifier = TransformerClassifier(
base_model=base_model,
hidden_size=int(meta.get("hidden_size", spec["hidden_size"])),
intermediate=int(meta.get("intermediate", spec["intermediate"])),
dropout=float(meta.get("dropout", spec["dropout"])),
)
state_dict = torch.load(model_dir / "classifier_full_model.bin", map_location="cpu")
missing, unexpected = classifier.load_state_dict(state_dict, strict=False)
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
classifier.to(DEVICE).eval()
return classifier, tokenizer, missing, unexpected
def predict_records(model, tokenizer, records: list[dict], batch_size: int, max_length: int) -> list[float]:
texts = [record["text"] for record in records]
labels = [int(record["label"]) for record in records]
encoded = tokenizer(texts, truncation=True, padding=False, max_length=max_length)
dataset = TokenDataset(encoded, labels)
loader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
collate_fn=DataCollatorWithPadding(tokenizer),
)
all_probs: list[float] = []
with torch.no_grad():
for batch in loader:
batch = {key: value.to(DEVICE) if isinstance(value, torch.Tensor) else value for key, value in batch.items()}
outputs = model(**batch)
probs = torch.softmax(outputs.logits, dim=-1)[:, 1].cpu().numpy()
all_probs.extend(float(x) for x in probs)
return all_probs
def main() -> None:
parser = argparse.ArgumentParser(description="Run archived BERT and RoBERTa checkpoints.")
parser.add_argument("--dataset", required=True, help="Dataset name under data/dataset/")
parser.add_argument("--dataset-root", default=str(DATASET_ROOT))
parser.add_argument("--output-root", default=str(OUTPUT_ROOT))
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--max-length", type=int, default=512)
parser.add_argument("--include-train", action="store_true")
args = parser.parse_args()
dataset_dir = Path(args.dataset_root) / args.dataset
output_dir = Path(args.output_root) / args.dataset
output_dir.mkdir(parents=True, exist_ok=True)
splits = ["train", "dev", "test"] if args.include_train else ["dev", "test"]
for model_name in ("bert", "roberta"):
model, tokenizer, missing, unexpected = build_model(model_name)
print(f"[{model_name}] missing={len(missing)} unexpected={len(unexpected)}")
for split in splits:
split_path = dataset_dir / f"{split}.jsonl"
if not split_path.exists():
continue
records = load_jsonl(split_path)
if not records:
continue
probs = predict_records(model, tokenizer, records, args.batch_size, args.max_length)
frame = pd.DataFrame(
{
"text": [record["text"] for record in records],
"label": [int(record["label"]) for record in records],
"length": [len(str(record["text"])) for record in records],
"pred_prob": probs,
"pred_label_05": [int(prob >= 0.5) for prob in probs],
}
)
output_path = output_dir / f"{model_name}_{split}_predictions.csv"
frame.to_csv(output_path, index=False, encoding="utf-8")
print(f"saved {output_path}")
del model
if torch.cuda.is_available():
torch.cuda.empty_cache()
if __name__ == "__main__":
main()
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