Sentence Transformers Alephbertgimmel Small

🚀 {MODEL_NAME}

這是一個句子轉換器模型，它能將句子和段落映射到一個512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

如果你安裝了句子轉換器，使用這個模型會很方便：

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('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)

高級用法（HuggingFace Transformers）

如果沒有安裝句子轉換器，你可以這樣使用該模型：首先，將輸入傳遞給轉換器模型，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 = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# 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)

📚 詳細文檔

🔍 評估結果

關於此模型的自動評估，請參閱句子嵌入基準測試：https://seb.sbert.net

🔧 技術細節

訓練參數

該模型使用以下參數進行訓練：

• 數據加載器： __main__.MultiDatasetDataLoader，長度為10819，參數如下：

  {'batch_size': 'unknown'}
  

• 損失函數： sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss，參數如下：

  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  

• 擬合方法參數：

  {
      "epochs": 1,
      "evaluation_steps": 500,
      "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
      "max_grad_norm": 1,
      "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
      "optimizer_params": {
          "lr": 2e-05
      },
      "scheduler": "WarmupLinear",
      "steps_per_epoch": null,
      "warmup_steps": 500,
      "weight_decay": 0.01
  }
  

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': 512, '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

文檔未提供相關許可證信息。

📖 引用與作者

文檔未提供相關引用與作者信息。

📋 信息表格