Paecter

🚀 PaECTER - 专利相似度模型

PaECTER（使用引用信息感知Transformer的专利嵌入）是一个专利相似度模型。它基于谷歌的专利BERT作为基础模型，能够从专利文本中生成1024维的密集向量嵌入。这些向量封装了给定专利文本的语义本质，非常适合用于与专利分析相关的各种下游任务。

论文链接：https://arxiv.org/pdf/2402.19411

✨ 主要特性

• 语义搜索
• 现有技术搜索
• 聚类分析
• 专利格局分析

🚀 快速开始

📦 安装指南

若要使用此模型，你需要安装 sentence-transformers：

pip install -U sentence-transformers

💻 使用示例

基础用法（使用Sentence-Transformers）

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

model = SentenceTransformer('mpi-inno-comp/paecter')
embeddings = model.encode(sentences)
print(embeddings)

高级用法（使用HuggingFace Transformers）

若未安装 sentence-transformers，可以按以下方式使用该模型：首先，将输入数据传入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('mpi-inno-comp/paecter')
model = AutoModel.from_pretrained('mpi-inno-comp/paecter')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt', max_length=512)

# 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

该模型的评估详情可在我们的论文 PaECTER: Patent-level Representation Learning using Citation-informed Transformers 中查看。

训练细节

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318750，参数如下：

{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

损失函数： sentence_transformers.losses.CustomTripletLoss.CustomTripletLoss，参数如下：

{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 1}

fit() 方法的参数：

{
    "epochs": 1,
    "evaluation_steps": 4000,
    "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 1e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 31875.0,
    "weight_decay": 0.01
}

完整模型架构

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)

引用与作者

@misc{ghosh2024paecter,
      title={PaECTER: Patent-level Representation Learning using Citation-informed Transformers}, 
      author={Mainak Ghosh and Sebastian Erhardt and Michael E. Rose and Erik Buunk and Dietmar Harhoff},
      year={2024},
      eprint={2402.19411},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}

📄 许可证

本项目采用Apache 2.0许可证。