Paraphrase Multilingual MiniLM L12 V2

🚀 DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2

本模型是 sentence-transformers 模型库中 此模型仓库 在特定提交版本 d66eff4d8a8598f264f166af8db67f7797164651 下的副本。它可以将句子和段落映射到384维的密集向量空间，可用于聚类或语义搜索等任务。

🚀 快速开始

本模型可通过 sentence-transformers 库或 HuggingFace Transformers 库使用。下面将分别介绍两种使用方式。

✨ 主要特性

• 多语言支持：能够处理多种语言的句子和段落。
• 向量映射：将文本映射到384维的密集向量空间。
• 应用广泛：可用于聚类、语义搜索等多种自然语言处理任务。

📦 安装指南

若要使用 sentence-transformers 库，可通过以下命令进行安装：

pip install -U sentence-transformers

💻 使用示例

基础用法（Sentence-Transformers）

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

model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
embeddings = model.encode(sentences)
print(embeddings)

高级用法（HuggingFace Transformers）

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

📚 详细文档

评估结果

若要对本模型进行自动化评估，可参考 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: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

🔧 技术细节

本模型基于 sentence-transformers 框架构建，使用 BertModel 作为基础的Transformer模型，并采用了平均池化（Mean Pooling）操作来获取句子的嵌入表示。

📄 许可证

本模型采用 Apache-2.0 许可证。

📖 引用与作者

本模型由 sentence-transformers 团队训练。 若您觉得本模型有帮助，可引用以下论文：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",
}