用法

稀疏编码器模型的特点

  1. 计算稀疏向量表示，其中大多数维度为零

  2. 由于嵌入的稀疏性，为大规模检索系统提供效率优势

  3. 通常比密集嵌入更具可解释性，非零维度对应于特定标记

  4. 与密集嵌入互补，支持结合两种方法优势的混合搜索系统

安装 Sentence Transformers 后，您可以轻松使用稀疏编码器模型

from sentence_transformers import SparseEncoder

# 1. Load a pretrained SparseEncoder model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")

# The sentences to encode
sentences = [
    "The weather is lovely today.",
    "It's so sunny outside!",
    "He drove to the stadium.",
]

# 2. Calculate sparse embeddings by calling model.encode()
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 30522] - sparse representation with vocabulary size dimensions

# 3. Calculate the embedding similarities (using dot product by default)
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[   35.629,     9.154,     0.098],
#         [    9.154,    27.478,     0.019],
#         [    0.098,     0.019,    29.553]])

# 4. Check sparsity statistics
stats = SparseEncoder.sparsity(embeddings)
print(f"Sparsity: {stats['sparsity_ratio']:.2%}")  # Typically >99% zeros
print(f"Avg non-zero dimensions per embedding: {stats['active_dims']:.2f}")

任务和高级用法