Mmarco Sentence BERTino

🚀 mmarco-sentence-BERTino

mmarco-sentence-BERTino 是一個 sentence-transformers 模型，它能夠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。該模型在 mmarco 數據集上進行訓練。

Mohan Samant, Midnight Fishing Party, 1978

🚀 快速開始

✨ 主要特性

• 能夠將句子和段落映射到 768 維的密集向量空間。
• 適用於聚類、語義搜索等任務。
• 在 mmarco 數據集上訓練。

📦 安裝指南

若要使用此模型，需安裝 sentence-transformers：

pip install -U sentence-transformers

💻 使用示例

基礎用法

使用 sentence-transformers 庫調用該模型：

from sentence_transformers import SentenceTransformer
sentences = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]

model = SentenceTransformer('efederici/mmarco-sentence-BERTino')
embeddings = model.encode(sentences)
print(embeddings)

高級用法

若未安裝 sentence-transformers，可以按如下方式使用該模型：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


#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 = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('efederici/mmarco-sentence-BERTino')
model = AutoModel.from_pretrained('efederici/mmarco-sentence-BERTino')

# 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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)

🔧 技術細節

完整模型架構

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': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

📄 許可證

本項目採用 Apache-2.0 許可證。