这是我的测试脚本 ```python import argparse import sys import torch from sentence_transformers import SentenceTransformer def main() -> int: parser = argparse.ArgumentParser(description="Check text encoder output health") parser.add_argument( "--model", type=str, default="sentence-transformers/sentence-t5-xxl", help="SentenceTransformer model path or model id", ) parser.add_argument( "--batch-size", type=int, default=2, help="Batch size for encoding", ) args = parser.parse_args() texts = [ "hello world", "test item title", "Title: Toy Story; Brand: Pixar; Categories: Movies; Price: 19.99;", ] model = SentenceTransformer(args.model) emb = model.encode( inputs=texts, batch_size=args.batch_size, show_progress_bar=False, convert_to_tensor=True, ) nan_count = torch.isnan(emb).sum().item() inf_count = torch.isinf(emb).sum().item() finite_ratio = torch.isfinite(emb).float().mean().item() print("model:", args.model) print("shape:", tuple(emb.shape)) print("dtype:", emb.dtype) print("nan:", nan_count) print("inf:", inf_count) print("finite_ratio:", finite_ratio) if finite_ratio < 1.0: print("ERROR: encoder output contains non-finite values.") return 1 print("OK: encoder output is fully finite.") return 0 if __name__ == "__main__": sys.exit(main()) ``` 结果全是NaN