This is the code of our paper NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction. We use a sentence-level pre-training task NSP (Next Sentence Prediction) to realize prompt-learning and perform various downstream tasks, such as single sentence classification, sentence pair classification, coreference resolution, cloze-style task, entity linking, entity typing.
On the FewCLUE benchmark, our NSP-BERT outperforms other zero-shot methods (GPT-1-zero and PET-zero) on most of these tasks and comes close to the few-shot methods. We hope NSP-BERT can be an unsupervised tool that can assist other language tasks or models.
2022/8/17 New version! For both zero-shot and few-shot NSP-BERT, English and Chinese. Baselines such as fine-tuning, NSP and PET are also implemented. https://github.com/sunyilgdx/Prompts4Keras
2022/6/16 There will be a major update soon！！！
2021/11/12 GLUE and more English datasets have been added. We can download these datasets on LM-BFF. Thanks to Gao Tianyu.
2021/10/11 We uploaded the code on several English classification task datasets, AG’s News, DBPedia, Amazon and IMDB. The Acc. of NSP-BERT on those datasets are about 81.8, 70.9, 71.9 and 70.7 (We tested about 1K samples). Thanks to Shengding Hu and his KnowledgeablePromptTuning.
| Section | Description |
|---|---|
| Environment | The required deployment environment |
| Downloads | Download links for the models' checkpoints used by NSP-BERT |
| Demos | Chinese and English demos |
| Evaluation | Evaluate NSP-BERT for different downstream tasks |
| Baselines | Baseline results for several Chinese NLP datasets (partial) |
| Model Comparison | Compare the models published in this repository |
| Strategy Details | Some of the strategies used in the paper |
| Discussion | Discussion and Discrimination for future work |
| Acknowledgements | Acknowledgements |
The environments are as follows:
Python 3.6
bert4keras 0.10.6
tensorflow-gpu 1.15.0
We should dowmload the checkpoints of different models. The vocab.txt and the config.json are already in our repository.
| Organization | Model Name | Model Parameters | Download Linking | Tips |
|---|---|---|---|---|
| BERT-uncased | L=12 H=769 A=12 102M | Tensorflow | ||
| BERT-Chinese | L=12 H=769 A=12 102M | Tensorflow | ||
| HFL | BERT-wwm | L=12 H=769 A=12 102M | Tensorflow | |
| BERT-wwm-ext | L=12 H=769 A=12 102M | Tensorflow | ||
| UER | BERT-mixed-tiny | L=3 H=384 A=6 14M | Pytorch | * |
| BERT-mixed-Small | L=6 H=512 A=8 31M | Pytorch | * | |
| BERT-mixed-Base | L=12 H=769 A=12 102M | Pytorch | * | |
| BERT-mixed-Large | L=24 H=1024 A=16 327M | Pytorch | * |
* We need to use UER's convert tool to convert UER pytorch to Original Tensorflow.
We use FewCLUE datasets and DuEL2.0 (CCKS2020) in our experiments.
| Datasets | Download Links |
|---|---|
| FewCLUE | https://github.com/CLUEbenchmark/FewCLUE/tree/main/datasets |
| DuEL2.0 (CCKS2020) | https://aistudio.baidu.com/aistudio/competition/detail/83 |
| EnEval | https://github.com/ShengdingHu/KnowledgeablePromptTuning |
Put the datasets into the NSP-BERT/datasets/.
Try to use ./demos/nsp_bert_classification_demo.py and ./demos/nsp_bert_classification_demo_en.py to accomplish your own classification tasks. Edit your own Labels and Samples, then create your own Prompt Templates, then you can classify them.
...
label_names = ['entertainment', 'sports', 'music', 'games', 'economics', 'education']
patterns = ["This is {} news".format(label) for label in label_names]
demo_data_en = ['FIFA unveils biennial World Cup plan, UEFA threatens boycott',
'COVID vaccines hold up against severe Delta: US data',
'Justin Drew Bieber was born on March 1, 1994 at St. ',
'Horizon launches latest chip to take on global rivals',
'Twitch video gamers rise up to stop ‘hate raids’']
...
Output
Sample 0:
Original Text: FIFA unveils biennial World Cup plan, UEFA threatens boycott
Predict label: sports
Logits: [0.50525445, 0.9874593, 0.40805838, 0.9633584, 0.39732504, 0.22665949]
Sample 1:
Original Text: COVID vaccines hold up against severe Delta: US data
Predict label: economics
Logits: [0.8868228, 0.9359472, 0.795272, 0.93895626, 0.99118936, 0.86002237]
Sample 2:
Original Text: Justin Drew Bieber was born on March 1, 1994 at St.
Predict label: music
Logits: [0.98517805, 0.97300863, 0.98871416, 0.95968705, 0.9250582, 0.9211884]
...
We can run individual python files in the project directly to evaluate our NSP-BERT.
NSP-BERT
|- datasets
|- clue_datasets
|- ...
|- DuEL 2.0
|- dev.json
|- kb.json
|- enEval
|- agnews
|- amazon
|- dbpedia
|- imdb
|- demos
|- nsp_bert_classification_demo.py
|- nsp_bert_classification_demo_en.py
|- models
|- uer_mixed_corpus_bert_base
|- bert_config.json
|- vocab.txt
|- bert_model.ckpt...
|- ...
|- nsp_bert_classification.py # Single Sentence Classification
|- nsp_bert_sentence_pair.py # Sentence Pair Classification
|- nsp_bert_cloze_style.py # Cloze-style Task
|- nsp_bert_coreference_resolution.py # Coreference Resolution
|- nsp_bert_entity_linking.py # Entity Linking and Entity Typing
|- utils.py
| Python File | Task | Datasets |
|---|---|---|
| nsp_bert_classification.py | Single Sentence Classification | EPRSTMT, TNEWS, CSLDCP, IFLYTEK |
| AG’s News, DBPedia, Amazon, IMDB | ||
| nsp_bert_sentence_pair.py | Sentence Pair Classification | OCNLI, BUSTM, CSL |
| nsp_bert_cloze_style.py | Cloze-style Task | ChID |
| nsp_bert_coreference_resolution.py | Coreference Resolution | CLUEWSC |
| nsp_bert_entity_linking.py | Entity Linking and Entity Typing | DuEL2.0 |
Reference FewCLUE, we choos 3 training scenarios, fine-tuning, few-shot and zero-shot. The baselines use Chineses-RoBERTa-Base and Chinses-GPT-1 as the backbone model.
| Scenarios | Methods |
|---|---|
| Fine-tuning | BERT, RoBERTa |
| Few-Shot | PET, ADAPET, P-tuning, LM-BFF, EFL |
| Zero-Shot | GPT-zero, PET-zero |
| Organization | Model Name | Model Parameters | Download Linking |
|---|---|---|---|
| huawei-noah | Chinese GPT | L=12 H=769 A=12 102M | Tensorflow |
| HFL | RoBERTa-wwm-ext | L=12 H=769 A=12 102M | Tensorflow |
- Sincce NSP-BERT is a sentence-level prompt-learning model, it is significantly superior to GPT-zero and PET-zero in terms of Single Sentence Classification tasks (TNEWS, CSLDCP and IFLYTEK). At the same time, it can solve the Entity Linking task (DuEL2.0), and the model is not limited by the non-fixed-length entity description, which GPT-zero and PET-zero cannot do this.
- However, it doesn't work as well on Token-Level tasks, such as Cloze-style task and Entity Typing.
- In future work, it is essential to extend NSP-BERT to the few-shot scenario.
- Our code is based on Jianlin Su's bert4keras.
- Thanks to teacher Su Jianlin's blog, Science Space, his series of blogs, and his open source spirit, which inspired and inspired my paper writing process.
@misc{sun2021nspbert,
title={NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task--Next Sentence Prediction},
author={Yi Sun and Yu Zheng and Chao Hao and Hangping Qiu},
year={2021},
eprint={2109.03564},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{sun-etal-2022-nsp,
title = "{NSP}-{BERT}: A Prompt-based Few-Shot Learner through an Original Pre-training Task {---}{---} Next Sentence Prediction",
author = "Sun, Yi and
Zheng, Yu and
Hao, Chao and
Qiu, Hangping",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.286",
pages = "3233--3250"
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent