🚀 mcontriever-base-msmarco
mcontriever-base-msmarco是一個句子轉換器模型,它可以將句子和段落映射到768維的密集向量空間,可用於聚類或語義搜索等任務。
該模型是從facebook的mcontriever-msmarco模型轉換而來。使用此模型時,請參考相關論文:Unsupervised Dense Information Retrieval with Contrastive Learning。
🚀 快速開始
本部分將介紹如何使用mcontriever-base-msmarco
模型進行句子嵌入。
📦 安裝指南
若要使用該模型,需先安裝sentence-transformers
庫:
pip install -U sentence-transformers
💻 使用示例
基礎用法
使用sentence-transformers
庫調用模型:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('nthakur/mcontriever-base-msmarco')
embeddings = model.encode(sentences)
print(embeddings)
高級用法
若未安裝sentence-transformers
庫,可使用HuggingFace Transformers庫調用模型。首先,將輸入傳遞給Transformer模型,然後對上下文詞嵌入應用正確的池化操作。
from transformers import AutoTokenizer, AutoModel
import torch
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
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 = ['This is an example sentence', 'Each sentence is converted']
tokenizer = AutoTokenizer.from_pretrained('nthakur/mcontriever-base-msmarco')
model = AutoModel.from_pretrained('nthakur/mcontriever-base-msmarco')
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
📚 詳細文檔
評估結果
要對該模型進行自動評估,請參考句子嵌入基準測試:https://seb.sbert.net
完整模型架構
SentenceTransformer(
(0): Transformer({'max_seq_length': 509, 'do_lower_case': False}) with Transformer model: BertModel
(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})
)
引用與作者
使用該模型時,請參考相關論文:Unsupervised Dense Information Retrieval with Contrastive Learning。