GNER T5 Large V2

🚀 重新思考生成式命名实体识别中的负样本

本项目提出了一种生成式命名实体识别（GNER）框架，该框架在未见实体领域展现出出色的零样本学习能力。通过在两个代表性生成模型（LLaMA和Flan - T5）的训练过程中引入负样本，显著提升了模型性能。所得到的GNER - LLaMA和GNER - T5模型大幅超越了当前的先进方法，$F_1$分数分别提高了8分和11分。项目代码和模型均已公开。

• 💻 代码：https://github.com/yyDing1/GNER/
• 📖 论文：重新思考生成式命名实体识别中的负样本
• 💾 HuggingFace模型库：GNER - 模型
• 🧪 复现材料：复现材料
• 🎨 Jupyter Notebook示例：GNER Notebook

📦 安装指南

你需要安装以下依赖：

pip install torch datasets deepspeed accelerate transformers protobuf

💻 使用示例

基础用法

请参考 Jupyter Notebook示例 来使用GNER模型。

以下是一个使用 GNER - T5 的简单推理示例：

>>> import torch
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("dyyyyyyyy/GNER-T5-xxl")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("dyyyyyyyy/GNER-T5-xxl", torch_dtype=torch.bfloat16).cuda()
>>> model = model.eval()
>>> instruction_template = "Please analyze the sentence provided, identifying the type of entity for each word on a token-by-token basis.\nOutput format is: word_1(label_1), word_2(label_2), ...\nWe'll use the BIO-format to label the entities, where:\n1. B- (Begin) indicates the start of a named entity.\n2. I- (Inside) is used for words within a named entity but are not the first word.\n3. O (Outside) denotes words that are not part of a named entity.\n"
>>> sentence = "did george clooney make a musical in the 1980s"
>>> entity_labels = ["genre", "rating", "review", "plot", "song", "average ratings", "director", "character", "trailer", "year", "actor", "title"]
>>> instruction = f"{instruction_template}\nUse the specific entity tags: {', '.join(entity_labels)} and O.\nSentence: {sentence}"
>>> inputs = tokenizer(instruction, return_tensors="pt").to("cuda")
>>> outputs = model.generate(**inputs, max_new_tokens=640)
>>> response = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>> print(response)
"did(O) george(B-actor) clooney(I-actor) make(O) a(O) musical(B-genre) in(O) the(O) 1980s(B-year)"

📚 详细文档

预训练模型

我们发布了五个基于LLaMA（7B）和Flan - T5（base、large、xl和xxl）的GNER模型。

📄 许可证

本项目采用Apache - 2.0许可证。

📖 引用

如果你使用了本项目的代码或模型，请引用以下论文：

@misc{ding2024rethinking,
      title={Rethinking Negative Instances for Generative Named Entity Recognition}, 
      author={Yuyang Ding and Juntao Li and Pinzheng Wang and Zecheng Tang and Bowen Yan and Min Zhang},
      year={2024},
      eprint={2402.16602},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}