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--- |
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license: apache-2.0 |
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datasets: |
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- Universal-NER/Pile-NER-type |
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language: |
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- en |
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metrics: |
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- f1 |
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library_name: transformers |
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pipeline_tag: text2text-generation |
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--- |
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<p align="center"><h2 align="center">Rethinking Negative Instances for Generative Named Entity Recognition</h2></p> |
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# Model Card for GNER-T5-base |
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<!-- Provide a quick summary of what the model is/does. --> |
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We introduce GNER, a **G**enerative **N**amed **E**ntity **R**ecognition 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. |
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* 💻 Code: [https://github.com/yyDing1/GNER/](https://github.com/yyDing1/GNER/) |
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* 📖 Paper: [Rethinking Negative Instances for Generative Named Entity Recognition](https://arxiv.org/abs/2402.16602) |
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* 💾 Models in the 🤗 HuggingFace Hub: [GNER-Models](https://huggingface.co/collections/dyyyyyyyy/gner-65dda2cb96c6e35c814dea56) |
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* 🧪 Reproduction Materials: [Reproduction Materials](https://drive.google.com/drive/folders/1m2FqDgItEbSoeUVo-i18AwMvBcNkZD46?usp=drive_link) |
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* 🎨 Example Jupyter Notebooks: [GNER Notebook](https://github.com/yyDing1/GNER/blob/main/notebook.ipynb) |
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<p align="center"> |
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<img src="https://github.com/yyDing1/GNER/raw/main/assets/zero_shot_results.png"> |
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</p> |
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## PreTrained Models |
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We release five GNER models based on LLaMA (7B) and Flan-T5 (base, large, xl and xxl). |
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| Model | # Params | Zero-shot Average $F_1$ | Supervised Average $F_1$ | 🤗 HuggingFace<br />Download Link | |
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| ------------- | -------: | :----------------------: | :-----------------------: | :-------------------------------------------------: | |
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| GNER-LLaMA | 7B | 66.1 | 86.09 | [link](https://huggingface.co/dyyyyyyyy/GNER-LLaMA-7B) | |
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| GNER-T5-base | 248M | 59.5 | 83.21 | [link](https://huggingface.co/dyyyyyyyy/GNER-T5-base) | |
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| GNER-T5-large | 783M | 63.5 | 85.45 | [link](https://huggingface.co/dyyyyyyyy/GNER-T5-large) | |
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| GNER-T5-xl | 3B | 66.1 | 85.94 | [link](https://huggingface.co/dyyyyyyyy/GNER-T5-xl) | |
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| GNER-T5-xxl | 11B | 69.1 | 86.15 | [link](https://huggingface.co/dyyyyyyyy/GNER-T5-xxl) | |
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## Demo usage |
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You should install the dependencies: |
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```bash |
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pip install torch datasets deepspeed accelerate transformers protobuf |
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``` |
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Please check out [Example Jupyter Notebooks](https://github.com/yyDing1/GNER/blob/main/notebook.ipynb) for guidance on utilizing GNER models. |
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A simple inference example is as follows: |
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Below is an example using `GNER-T5` |
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```python |
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>>> import torch |
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>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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>>> tokenizer = AutoTokenizer.from_pretrained("dyyyyyyyy/GNER-T5-xxl") |
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>>> model = AutoModelForSeq2SeqLM.from_pretrained("dyyyyyyyy/GNER-T5-xxl", torch_dtype=torch.bfloat16).cuda() |
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>>> model = model.eval() |
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>>> 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" |
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>>> sentence = "did george clooney make a musical in the 1980s" |
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>>> entity_labels = ["genre", "rating", "review", "plot", "song", "average ratings", "director", "character", "trailer", "year", "actor", "title"] |
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>>> instruction = f"{instruction_template}\nUse the specific entity tags: {', '.join(entity_labels)} and O.\nSentence: {sentence}" |
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>>> inputs = tokenizer(instruction, return_tensors="pt").to("cuda") |
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>>> outputs = model.generate(**inputs, max_new_tokens=640) |
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>>> response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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>>> print(response) |
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"did(O) george(B-actor) clooney(I-actor) make(O) a(O) musical(B-genre) in(O) the(O) 1980s(B-year)" |
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``` |
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## Citation |
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```bibtex |
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@misc{ding2024rethinking, |
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title={Rethinking Negative Instances for Generative Named Entity Recognition}, |
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author={Yuyang Ding and Juntao Li and Pinzheng Wang and Zecheng Tang and Bowen Yan and Min Zhang}, |
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year={2024}, |
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eprint={2402.16602}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
