🚀 kamalkraj/BioSimCSE - BioLinkBERT - BASE

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。

🚀 快速开始

✨ 主要特性

• 可将句子和段落映射到 768 维的密集向量空间。
• 适用于聚类和语义搜索等任务。

📦 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 库调用模型的示例代码如下：

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

model = SentenceTransformer('kamalkraj/BioSimCSE-BioLinkBERT-BASE')
embeddings = model.encode(sentences)
print(embeddings)

高级用法

若不使用 sentence - transformers，可按以下步骤使用模型：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('kamalkraj/BioSimCSE-BioLinkBERT-BASE')
model = AutoModel.from_pretrained('kamalkraj/BioSimCSE-BioLinkBERT-BASE')

# 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

该模型使用以下参数进行训练：

• 数据加载器： torch.utils.data.dataloader.DataLoader，长度为 7708，参数如下：

  {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
  

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

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

• fit() 方法的参数：

  {
      "epochs": 1,
      "evaluation_steps": 0,
      "evaluator": "NoneType",
      "max_grad_norm": 1,
      "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
      "optimizer_params": {
          "lr": 5e-05
      },
      "scheduler": "WarmupLinear",
      "steps_per_epoch": null,
      "warmup_steps": 771,
      "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': 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

文档中未提及相关许可证信息。

🔧 技术细节

该模型将句子和段落映射到 768 维的密集向量空间，在训练过程中使用了特定的数据加载器、损失函数和优化器参数。通过对上下文词嵌入应用池化操作，得到句子的嵌入表示。在评估方面，可通过 Sentence Embeddings Benchmark 进行自动评估。

📄 引用与作者

@inproceedings{kanakarajan-etal-2022-biosimcse,
    title = "{B}io{S}im{CSE}: {B}io{M}edical Sentence Embeddings using Contrastive learning",
    author = "Kanakarajan, Kamal raj  and
      Kundumani, Bhuvana  and
      Abraham, Abhijith  and
      Sankarasubbu, Malaikannan",
    booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.louhi-1.10",
    pages = "81--86",
    abstract = "Sentence embeddings in the form of fixed-size vectors that capture the information in the sentence as well as the context are critical components of Natural Language Processing systems. With transformer model based sentence encoders outperforming the other sentence embedding methods in the general domain, we explore the transformer based architectures to generate dense sentence embeddings in the biomedical domain. In this work, we present BioSimCSE, where we train sentence embeddings with domain specific transformer based models with biomedical texts. We assess our model{'}s performance with zero-shot and fine-tuned settings on Semantic Textual Similarity (STS) and Recognizing Question Entailment (RQE) tasks. Our BioSimCSE model using BioLinkBERT achieves state of the art (SOTA) performance on both tasks.",
}

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