File size: 5,953 Bytes
7e9cfd1 | 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 | """Finetune a transformer for stance detection. Picks the checkpoint off dev
Favg2, not loss or a fixed epoch count. Loss fn is configurable -- plain CE,
inverse-frequency weighted, or focal -- to deal with the class imbalance.
python -m src.train --config configs/track1.yaml
"""
import argparse
import json
import os
import random
import numpy as np
import torch
import torch.nn.functional as F
import yaml
from torch.utils.data import DataLoader
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
get_linear_schedule_with_warmup,
)
from src.data import ID2LABEL, LABEL2ID, StanceDataset, load_split
from src.scorer import score
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def focal_loss(logits, targets, gamma, weight=None):
ce = F.cross_entropy(logits, targets, weight=weight, reduction="none")
pt = torch.exp(-ce)
return ((1 - pt) ** gamma * ce).mean()
def class_weights(df, device):
counts = np.array(
[(df["label"] == i).sum() for i in range(3)], dtype=np.float64
)
counts = np.clip(counts, 1, None)
w = counts.sum() / (3.0 * counts)
return torch.tensor(w, dtype=torch.float, device=device)
@torch.no_grad()
def predict_logits(model, loader, device):
model.eval()
out = []
for batch in loader:
batch = {
k: v.to(device) for k, v in batch.items() if k != "labels"
}
out.append(model(**batch).logits.float().cpu().numpy())
return np.concatenate(out, axis=0)
def logits_to_labels(logits, none_bias=0.0):
"""A negative none_bias lowers the None logit before argmax."""
adj = logits.copy()
adj[:, LABEL2ID["None"]] += none_bias
return [ID2LABEL[i] for i in adj.argmax(axis=1)]
def build_config():
ap = argparse.ArgumentParser()
ap.add_argument("--config", required=True)
ap.add_argument("--overrides", default="", help="k=v,k=v pairs")
args = ap.parse_args()
cfg = yaml.safe_load(open(args.config))
for kv in [x for x in args.overrides.split(",") if x]:
k, v = kv.split("=", 1)
cfg[k] = yaml.safe_load(v)
return cfg
def main():
cfg = build_config()
print("[config]", json.dumps(cfg, ensure_ascii=False))
set_seed(cfg.get("seed", 42))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
prep = cfg.get("prep_mode", "preserve")
train_df = load_split(cfg["train_csv"], prep)
dev_df = load_split(cfg["dev_csv"], prep)
trust = cfg.get("trust_remote_code", False)
tok = AutoTokenizer.from_pretrained(
cfg["model_hf"], trust_remote_code=trust
)
max_len = cfg.get("max_len", 128)
use_desc = cfg.get("use_description", False)
train_ds = StanceDataset(train_df, tok, max_len, use_desc)
dev_ds = StanceDataset(dev_df, tok, max_len, use_desc)
train_loader = DataLoader(
train_ds, batch_size=cfg.get("batch_size", 16), shuffle=True
)
dev_loader = DataLoader(
dev_ds, batch_size=cfg.get("eval_batch_size", 64), shuffle=False
)
model = AutoModelForSequenceClassification.from_pretrained(
cfg["model_hf"], num_labels=3,
id2label=ID2LABEL, label2id=LABEL2ID,
trust_remote_code=trust,
).to(device)
optim = torch.optim.AdamW(
model.parameters(),
lr=cfg.get("lr", 2e-5),
weight_decay=cfg.get("weight_decay", 0.01),
)
epochs = cfg.get("epochs", 10)
total_steps = len(train_loader) * epochs
sched = get_linear_schedule_with_warmup(
optim, int(0.06 * total_steps), total_steps
)
loss_type = cfg.get("loss", "ce")
weight = None
if loss_type in ("weighted", "focal_weighted"):
weight = class_weights(train_df, device)
gamma = cfg.get("focal_gamma", 2.0)
out_dir = cfg["out_dir"]
os.makedirs(out_dir, exist_ok=True)
best_favg2, best_epoch = -1.0, -1
patience = cfg.get("patience", 3)
for epoch in range(epochs):
model.train()
running = 0.0
for batch in train_loader:
batch = {k: v.to(device) for k, v in batch.items()}
labels = batch.pop("labels")
logits = model(**batch).logits
if loss_type.startswith("focal"):
loss = focal_loss(logits, labels, gamma, weight)
else:
loss = F.cross_entropy(logits, labels, weight=weight)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optim.step()
sched.step()
optim.zero_grad()
running += loss.item()
logits = predict_logits(model, dev_loader, device)
preds = logits_to_labels(logits, cfg.get("none_bias", 0.0))
print(
f"\n=== epoch {epoch + 1}/{epochs} "
f"train_loss={running / len(train_loader):.4f} ==="
)
res = score(dev_df[["target", "stance"]], preds)
favg2 = res["overall"]["Favg2"]
if favg2 > best_favg2:
best_favg2, best_epoch = favg2, epoch + 1
model.save_pretrained(out_dir)
tok.save_pretrained(out_dir)
np.save(os.path.join(out_dir, "best_dev_logits.npy"), logits)
json.dump(
{
"best_epoch": best_epoch,
"best_favg2": best_favg2,
"config": cfg,
},
open(os.path.join(out_dir, "best.json"), "w"),
ensure_ascii=False,
indent=2,
)
print(f" new best Favg2={best_favg2:.4f} (saved)")
elif epoch + 1 - best_epoch >= patience:
print(f" early stop after {patience} epochs without gain")
break
print(f"\nBEST dev Favg2={best_favg2:.4f} @ epoch {best_epoch} "
f"-> {out_dir}")
if __name__ == "__main__":
main()
|