Sentence Transformers Alephbertgimmel Small

🚀 {MODEL_NAME}

这是一个句子转换器模型，它能将句子和段落映射到一个512维的密集向量空间，可用于聚类或语义搜索等任务。

🚀 快速开始

📦 安装指南

如果你安装了句子转换器，使用这个模型会很方便：

pip install -U sentence-transformers

💻 使用示例

基础用法（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)

高级用法（HuggingFace Transformers）

如果没有安装句子转换器，你可以这样使用该模型：首先，将输入传递给转换器模型，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


#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)

📚 详细文档

🔍 评估结果

关于此模型的自动评估，请参阅句子嵌入基准测试：https://seb.sbert.net

🔧 技术细节

训练参数

该模型使用以下参数进行训练：

• 数据加载器： __main__.MultiDatasetDataLoader，长度为10819，参数如下：

  {'batch_size': 'unknown'}
  

• 损失函数： sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss，参数如下：

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

• 拟合方法参数：

  {
      "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
  }
  

完整模型架构

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})
)

📄 许可证

文档未提供相关许可证信息。

📖 引用与作者

文档未提供相关引用与作者信息。

📋 信息表格