具有多语言预训练维基单词向量的ValueError

import numpy as np

# 假设模型期望的输入维度是300
expected_dim = 300

# 加载预训练向量
vectors = np.load('path_to_vectors.npy')

# 检查第一个向量的维度
if vectors.shape[1] != expected_dim:
    raise ValueError(f"Expected vector dimension {expected_dim}, but got {vectors.shape[1]}")

# 尝试加载向量文件
try:
    vectors = np.load('path_to_vectors.npy')
except ValueError as e:
    print(f"Error loading vectors: {e}")

def load_vectors_in_batches(file_path, batch_size=1000):
    vectors = []
    with open(file_path, 'r', encoding='utf-8') as f:
        for i, line in enumerate(f):
            if i % batch_size == 0 and i > 0:
                yield np.array(vectors)
                vectors = []
            parts = line.strip().split()
            vector = [float(x) for x in parts[1:]]
            vectors.append(vector)
    yield np.array(vectors)

# 使用生成器加载向量
for batch in load_vectors_in_batches('path_to_vectors.txt'):
    # 处理每个批次
    pass

with open('path_to_vectors.txt', 'r', encoding='utf-8') as f:
    for line in f:
        # 处理每一行
        pass