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eval/train_and_eval.py

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+"""General-sentiment trainer — SST-2, targeting the 3.9M-dl/mo distilbert-sst2 incumbent.
+
+Same discipline as finsense_train.py: fp32, best-ckpt by val metric, held-out
+reporting. SST-2 official: train 67,349 / validation 872 (the reported split —
+incumbent's 91.3 is on validation; test is unlabeled). We carve 5% of train as
+our early-stop val and report on the official validation set, untouched.
+
+Usage: python sst2_train.py [--seed 42] [--base answerdotai/ModernBERT-base]
+"""
+import argparse
+import json
+import os
+
+import torch
+from datasets import load_dataset
+from sklearn.metrics import accuracy_score, f1_score
+from transformers import (AutoModelForSequenceClassification, AutoTokenizer,
+                          Trainer, TrainingArguments)
+
+p = argparse.ArgumentParser()
+p.add_argument("--seed", type=int, default=42)
+p.add_argument("--base", default="answerdotai/ModernBERT-base")
+p.add_argument("--epochs", type=int, default=2)
+args = p.parse_args()
+
+OUT = os.path.expanduser(f"~/finsense_runs/sst2-{args.base.split('/')[-1]}-s{args.seed}")
+os.makedirs(OUT, exist_ok=True)
+LABELS = ["negative", "positive"]
+
+ds = load_dataset("nyu-mll/glue", "sst2")
+full_train = ds["train"].shuffle(seed=42)
+n_val = int(len(full_train) * 0.05)
+early_val = full_train.select(range(n_val))
+train = full_train.select(range(n_val, len(full_train)))
+report_val = ds["validation"]  # incumbent's benchmark split — never used for selection
+print(f"train={len(train)} early_val={len(early_val)} report_val={len(report_val)}", flush=True)
+
+tok = AutoTokenizer.from_pretrained(args.base)
+
+def enc(d):
+    return d.map(lambda b: tok(b["sentence"], truncation=True, max_length=128),
+                 batched=True, remove_columns=[c for c in d.column_names if c not in ("label",)])
+
+torch.manual_seed(args.seed)
+model = AutoModelForSequenceClassification.from_pretrained(
+    args.base, num_labels=2,
+    id2label=dict(enumerate(LABELS)), label2id={l: i for i, l in enumerate(LABELS)})
+
+def metrics(pred):
+    y, yhat = pred.label_ids, pred.predictions.argmax(-1)
+    return {"accuracy": accuracy_score(y, yhat)}
+
+import transformers as _tf
+_tok_kw = {"processing_class": tok} if int(_tf.__version__.split(".")[0]) >= 5 else {"tokenizer": tok}
+trainer = Trainer(
+    model=model, **_tok_kw,
+    train_dataset=enc(train), eval_dataset=enc(early_val),
+    compute_metrics=metrics,
+    args=TrainingArguments(
+        output_dir=f"{OUT}/ckpt",
+        num_train_epochs=args.epochs,
+        per_device_train_batch_size=32, per_device_eval_batch_size=64,
+        learning_rate=2e-5, warmup_ratio=0.06, weight_decay=0.01,
+        eval_strategy="steps", eval_steps=500,
+        save_strategy="steps", save_steps=500, save_total_limit=1,
+        load_best_model_at_end=True, metric_for_best_model="accuracy",
+        logging_steps=100, seed=args.seed, report_to=[],
+        fp16=False, bf16=False,
+    ),
+)
+trainer.train()
+
+pred = trainer.predict(enc(report_val))
+y, yhat = pred.label_ids, pred.predictions.argmax(-1)
+res = {"base": args.base, "seed": args.seed, "task": "sst2",
+       "val_accuracy": float(accuracy_score(y, yhat)),
+       "val_f1": float(f1_score(y, yhat)),
+       "incumbent": "distilbert-sst2 = 0.913 on this same validation split"}
+json.dump(res, open(f"{OUT}/result.json", "w"), indent=1)
+print("SST2_RESULT", json.dumps(res), flush=True)
+model.save_pretrained(f"{OUT}/model")
+tok.save_pretrained(f"{OUT}/model")