Model Card for GNER-LLaMA-7B
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 and models are publicly available.
- 💻 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
- 🎨 Example Jupyter Notebooks: GNER Notebook
PreTrained Models
We release five GNER models based on LLaMA (7B) and Flan-T5 (base, large, xl and xxl).
| Model | # Params | Zero-shot Average $F_1$ | Supervised Average $F_1$ | 🤗 HuggingFaceDownload Link |
|---|---|---|---|---|
| GNER-LLaMA | 7B | 66.1 | 86.09 | link |
| GNER-T5-base | 248M | 59.5 | 83.21 | link |
| GNER-T5-large | 783M | 63.5 | 85.45 | link |
| GNER-T5-xl | 3B | 66.1 | 85.94 | link |
| GNER-T5-xxl | 11B | 69.1 | 86.15 | link |
Demo usage
You should install the dependencies:
pip install torch datasets deepspeed accelerate transformers protobuf
Please check out Example Jupyter Notebooks for guidance on utilizing GNER models.
A simple inference example is as follows:
Below is an example using GNER-LLaMA
>>> import torch
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("dyyyyyyyy/GNER-LLaMA-7B")
>>> model = AutoModelForCausalLM.from_pretrained("dyyyyyyyy/GNER-LLaMA-7B", 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}"
>>> instruction = f"[INST] {instruction} [/INST]"
>>> 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)
>>> response = response[response.find("[/INST]") + len("[/INST]"):].strip()
>>> print(response)
"did(O) george(B-actor) clooney(I-actor) make(O) a(O) musical(B-genre) in(O) the(O) 1980s(B-year)"
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}
}