GNER T5 Xxl

🚀 Rethinking Negative Instances for Generative Named Entity Recognition

This project introduces GNER, a Generative Named Entity Recognition framework. It shows enhanced zero - shot capabilities across unseen entity domains. Integrating negative instances into the training of LLaMA and Flan - T5 models leads to significant performance improvements, with GNER - LLaMA and GNER - T5 outperforming SoTA approaches by 8 and 11 points in $F_1$ score respectively. The code and models are publicly available.

🚀 Quick Start

We introduce GNER, a Generative Named Entity Recognition framework, which demonstrates enhanced zero - shot capabilities across unseen entity domains. Experiments on two representative generative models, i.e., LLaMA and Flan - T5, show that the integration of negative instances into the training process yields substantial performance enhancements. The resulting models, GNER - LLaMA and GNER - T5, outperform state - of - the - art (SoTA) approaches by a large margin, achieving improvements of 8 and 11 points in $F_1$ score, respectively.

• 💻 Code: https://github.com/yyDing1/GNER/
• 📖 Paper: Rethinking Negative Instances for Generative Named Entity Recognition
• 💾 Models in the 🤗 HuggingFace Hub: GNER - Models
• 🧪 Reproduction Materials: [Reproduction Materials](https://drive.google.com/drive/folders/1m2FqDgItEbSoeUVo - i18AwMvBcNkZD46?usp=drive_link)
• 🎨 Example Jupyter Notebooks: GNER Notebook

✨ Features

• Demonstrates enhanced zero - shot capabilities across unseen entity domains.
• Integrating negative instances into training leads to significant performance improvements.
• The resulting models outperform state - of - the - art approaches by a large margin in $F_1$ score.

📦 Installation

You should install the dependencies:

pip install torch datasets deepspeed accelerate transformers protobuf

💻 Usage Examples

Basic Usage

Please check out Example Jupyter Notebooks for guidance on utilizing GNER models.

Advanced Usage

A simple inference example is as follows:

Below is an example using GNER - T5

>>> 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)"

📚 Documentation

PreTrained Models

We release five GNER models based on LLaMA (7B) and Flan - T5 (base, large, xl and xxl).

📄 License

The model is licensed under Apache - 2.0.

📖 Citation

@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}
}