Paraphrase Multilingual MiniLM L12 V2

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

本模型是 sentence-transformers 模型庫中 此模型倉庫 在特定提交版本 d66eff4d8a8598f264f166af8db67f7797164651 下的副本。它可以將句子和段落映射到384維的密集向量空間，可用於聚類或語義搜索等任務。

🚀 快速開始

本模型可通過 sentence-transformers 庫或 HuggingFace Transformers 庫使用。下面將分別介紹兩種使用方式。

✨ 主要特性

• 多語言支持：能夠處理多種語言的句子和段落。
• 向量映射：將文本映射到384維的密集向量空間。
• 應用廣泛：可用於聚類、語義搜索等多種自然語言處理任務。

📦 安裝指南

若要使用 sentence-transformers 庫，可通過以下命令進行安裝：

pip install -U sentence-transformers

💻 使用示例

基礎用法（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)

高級用法（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)

📚 詳細文檔

評估結果

若要對本模型進行自動化評估，可參考 Sentence Embeddings Benchmark：https://seb.sbert.net

完整模型架構

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})
)

🔧 技術細節

本模型基於 sentence-transformers 框架構建，使用 BertModel 作為基礎的Transformer模型，並採用了平均池化（Mean Pooling）操作來獲取句子的嵌入表示。

📄 許可證

本模型採用 Apache-2.0 許可證。

📖 引用與作者

本模型由 sentence-transformers 團隊訓練。 若您覺得本模型有幫助，可引用以下論文：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",
}