🚀 nthakur/contriever-base-msmarco
這是一個將 Contriever MSMARCO 模型 遷移到 sentence-transformers 的模型。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。
🚀 快速開始
📦 安裝指南
如果你已經安裝了 sentence-transformers,使用這個模型會很容易:
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('nthakur/contriever-base-msmarco')
embeddings = model.encode(sentences)
print(embeddings)
高級用法(HuggingFace Transformers)
如果沒有安裝 sentence-transformers,你可以這樣使用該模型:首先,將輸入數據傳入 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/contriever-base-msmarco')
model = AutoModel.from_pretrained('nthakur/contriever-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
有關該模型的自動評估,請參閱 Sentence Embeddings Benchmark: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
更多信息請參考:Contriever Model。