Sapbert Mnli Snli Scinli Scitail Mednli Stsb

🚀 pritamdeka/SapBERT-mnli-snli-scinli-scitail-mednli-stsb

这是一个 sentence-transformers 模型，它能将句子和段落映射到一个 768 维的密集向量空间，可用于聚类或语义搜索等任务。该模型在 SNLI、MNLI、SCINLI、SCITAIL、MEDNLI 和 STSB 数据集上进行了训练，以提供稳健的句子嵌入。

🚀 快速开始

✨ 主要特性

• 基于 sentence-transformers，可将句子和段落映射到 768 维的密集向量空间。
• 经过多数据集训练，能提供稳健的句子嵌入，适用于聚类、语义搜索等任务。

📦 安装指南

若要使用此模型，需先安装 sentence-transformers：

pip install -U sentence-transformers

💻 使用示例

基础用法

使用 sentence-transformers 库时，使用此模型十分简单：

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('pritamdeka/SapBERT-mnli-snli-scinli-scitail-mednli-stsb')
embeddings = model.encode(sentences)
print(embeddings)

高级用法

若未安装 sentence-transformers，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


#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('pritamdeka/SapBERT-mnli-snli-scinli-scitail-mednli-stsb')
model = AutoModel.from_pretrained('pritamdeka/SapBERT-mnli-snli-scinli-scitail-mednli-stsb')

# 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

若要对该模型进行自动评估，请参考 Sentence Embeddings Benchmark：https://seb.sbert.net

训练信息

该模型的训练参数如下：

• 数据加载器（DataLoader）： torch.utils.data.dataloader.DataLoader，长度为 90，参数如下：

  {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
  

• 损失函数（Loss）： sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss
• fit() 方法的参数：

  {
      "epochs": 4,
      "evaluation_steps": 1000,
      "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
      "max_grad_norm": 1,
      "optimizer_class": "<class 'transformers.optimization.AdamW'>",
      "optimizer_params": {
          "lr": 2e-05
      },
      "scheduler": "WarmupLinear",
      "steps_per_epoch": null,
      "warmup_steps": 36,
      "weight_decay": 0.01
  }
  

完整模型架构

SentenceTransformer(
  (0): Transformer({'max_seq_length': 100, '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})
)

📄 许可证

文档未提及许可证相关信息。

🔗 引用与作者

如果您使用了该模型，请引用以下文献：

@inproceedings{deka2022evidence,
  title={Evidence Extraction to Validate Medical Claims in Fake News Detection},
  author={Deka, Pritam and Jurek-Loughrey, Anna and others},
  booktitle={International Conference on Health Information Science},
  pages={3--15},
  year={2022},
  organization={Springer}
}