sentence-transformers/distiluse-base-multilingual-cased

这是一个 sentence-transformers 模型：它将句子和段落映射到一个512维的稠密向量空间，并可以用于聚类或语义搜索等任务。

使用（Sentence-Transformers）

安装了 sentence-transformers 后，使用该模型变得非常简单：

pip install -U sentence-transformers

然后您可以像这样使用模型：

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased')
embeddings = model.encode(sentences)
print(embeddings)

评估结果

有关此模型的自动化评估，请参见句子嵌入基准（Sentence Embeddings Benchmark）： https://seb.sbert.net

完整模型架构

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

引用和作者

这个模型是由 sentence-transformers 训练的。

如果您觉得这个模型有帮助，请随意引用我们的出版物 Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks ：

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "http://arxiv.org/abs/1908.10084",
}