🚀 sentence-transformers/msmarco-distilbert-base-tas-b
这是一个将 DistilBert TAS - B 模型 迁移到 sentence-transformers 的模型。它能将句子和段落映射到 768 维的密集向量空间,并针对语义搜索任务进行了优化。
🚀 快速开始
本模型可通过两种方式使用,下面为你详细介绍。
📦 安装指南
若要使用此模型,你需要安装 sentence-transformers:
pip install -U sentence-transformers
💻 使用示例
基础用法(Sentence - Transformers)
当你安装了 sentence-transformers 后,使用该模型会变得非常简单:
from sentence_transformers import SentenceTransformer, util
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
model = SentenceTransformer('sentence-transformers/msmarco-distilbert-base-tas-b')
query_emb = model.encode(query)
doc_emb = model.encode(docs)
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
doc_score_pairs = list(zip(docs, scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
for doc, score in doc_score_pairs:
print(score, doc)
高级用法(HuggingFace Transformers)
若未安装 sentence-transformers,你可以按以下方式使用该模型:首先,将输入传递给 Transformer 模型,然后对上下文词嵌入应用正确的池化操作。
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output):
return model_output.last_hidden_state[:,0]
def encode(texts):
encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input, return_dict=True)
embeddings = cls_pooling(model_output)
return embeddings
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b")
model = AutoModel.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b")
query_emb = encode(query)
doc_emb = encode(docs)
scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
doc_score_pairs = list(zip(docs, scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
for doc, score in doc_score_pairs:
print(score, doc)
📚 详细文档
评估结果
若要对该模型进行自动化评估,请参考 Sentence Embeddings Benchmark:https://seb.sbert.net
完整模型架构
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
引用与作者
更多信息请查看:DistilBert TAS - B 模型
📄 许可证
本项目采用 Apache - 2.0 许可证。
属性 |
详情 |
模型类型 |
sentence-transformers、feature-extraction、sentence-similarity、transformers |
训练数据 |
未提及 |