PaddleNLP: A Python repository from luyaojie

Features | Installation | Quick Start | API Reference | Community

PaddleNLP is an easy-to-use and powerful NLP library with awesome pre-trained model zoo, supporting wide-range of NLP tasks from research to industrial applications.

News

🔥 2021.5.18-19 We will introduce UIE (Universal Information Extraction) and ERNIE 3.0 light-weight model. Welcome to join us!
🔥 2022.5.16 PaddleNLP v2.3 Released!🎉
- 🔥Release ERNIE 3.0 light-weight model which achieved better results compared to ERNIE 2.0 on CLUE benchmark. Release ERNIE-Health, a biomedical SOTA pretrained model on CBLUE; Release PLATO-XL with FasterGeneration, which can do fast parallel inference with 11B large-scale model.
- 🔥Release UIE (Universal Information Extraction) technique, which single model can support NER, Relation Extraction, Event Extraction and Sentiment Anlaysis simultaneously.

Features

📦 Off-the-shelf NLP Pre-built Task

🤗 Awesome Chinese Pre-trained Model Zoo

🎛️ Industrial End-to-end System Cases

🚀 High Performance Distributed Training and Inference

Off-the-shelf NLP Pre-built Task

Taskflow aims to provide off-the-shelf NLP pre-built task covering NLU and NLG scenario, in the meanwhile with extreamly fast infernece satisfying industrial applications.

For more usage please refer to Taskflow Docs.

Awesome Chinese Pre-trained Model Zoo

🀄 Comprehensive Chinese Transformer Models

We provide 45+ network architectures and over 500+ pretrained models. Not only includes all the SOTA model like ERNIE, PLATO and SKEP released by Baidu, but also integrates most of the high quality Chinese pretrained model developed by other organizations. Use AutoModel API to ⚡FAST⚡ download pretrained mdoels of different architecture. We welcome all developers to contribute your Transformer models to PaddleNLP!

from paddlenlp.transformers import *

ernie = AutoModel.from_pretrained('ernie-3.0-medium-zh')
bert = AutoModel.from_pretrained('bert-wwm-chinese')
albert = AutoModel.from_pretrained('albert-chinese-tiny')
roberta = AutoModel.from_pretrained('roberta-wwm-ext')
electra = AutoModel.from_pretrained('chinese-electra-small')
gpt = AutoModelForPretraining.from_pretrained('gpt-cpm-large-cn')

Unified API experience for NLP task like semantic representation, text classification, sentence matching, sequence labeling, question answering, etc.

import paddle
from paddlenlp.transformers import *

tokenizer = AutoTokenizer.from_pretrained('ernie-3.0-medium-zh')
text = tokenizer('natural language processing')

# Semantic Representation
model = AutoModel.from_pretrained('ernie-3.0-medium-zh')
sequence_output, pooled_output = model(input_ids=paddle.to_tensor([text['input_ids']]))
# Text Classificaiton and Matching
model = AutoModelForSequenceClassification.from_pretrained('ernie-3.0-medium-zh')
# Sequence Labeling
model = AutoModelForTokenClassification.from_pretrained('ernie-3.0-medium-zh')
# Question Answering
model = AutoModelForQuestionAnswering.from_pretrained('ernie-3.0-medium-zh')

Wide-range NLP Task Support

PaddleNLP provides rich application examples covering mainstream NLP task to help developers accelerate problem solving. You can find our powerful transformer Model Zoo, and wide-range NLP application exmaples with detailed instructions.

Also you can run our interactive Notebook tutorial on AI Studio, a powerful platform with FREE computing resource.

PaddleNLP Transformer model summary, click to show more detials

Model	Sequence Classification	Token Classification	Question Answering	Text Generation	Multiple Choice
ALBERT	✅	✅	✅	❌	✅
BART	✅	✅	✅	✅	❌
BERT	✅	✅	✅	❌	✅
BigBird	✅	✅	✅	❌	✅
BlenderBot	❌	❌	❌	✅	❌
ChineseBERT	✅	✅	✅	❌	❌
ConvBERT	✅	✅	✅	❌	✅
CTRL	✅	❌	❌	❌	❌
DistilBERT	✅	✅	✅	❌	❌
ELECTRA	✅	✅	✅	❌	✅
ERNIE	✅	✅	✅	❌	✅
ERNIE-CTM	❌	✅	❌	❌	❌
ERNIE-Doc	✅	✅	✅	❌	❌
ERNIE-GEN	❌	❌	❌	✅	❌
ERNIE-Gram	✅	✅	✅	❌	❌
ERNIE-M	✅	✅	✅	❌	❌
FNet	✅	✅	✅	❌	✅
Funnel-Transformer	✅	✅	✅	❌	❌
GPT	✅	✅	❌	✅	❌
LayoutLM	✅	✅	❌	❌	❌
LayoutLMv2	❌	✅	❌	❌	❌
LayoutXLM	❌	✅	❌	❌	❌
LUKE	❌	✅	✅	❌	❌
mBART	✅	❌	✅	❌	✅
MegatronBERT	✅	✅	✅	❌	✅
MobileBERT	✅	❌	✅	❌	❌
MPNet	✅	✅	✅	❌	✅
NEZHA	✅	✅	✅	❌	✅
PP-MiniLM	✅	❌	❌	❌	❌
ProphetNet	❌	❌	❌	✅	❌
Reformer	✅	❌	✅	❌	❌
RemBERT	✅	✅	✅	❌	✅
RoBERTa	✅	✅	✅	❌	✅
RoFormer	✅	✅	✅	❌	❌
SKEP	✅	✅	❌	❌	❌
SqueezeBERT	✅	✅	✅	❌	❌
T5	❌	❌	❌	✅	❌
TinyBERT	✅	❌	❌	❌	❌
UnifiedTransformer	❌	❌	❌	✅	❌
XLNet	✅	✅	✅	❌	✅

For more pretrained model usage, please refer to Transformer API Docs.

Industrial End-to-end System Cases

We provide high value scenarios including information extraction, semantic retrieval, questionn answering high-value.

For more details industial cases please refer to Applications.

🧠 Neural Search System

For more details please refer to Neural Search.

❓ Question Answering System

We provide question answering pipeline which can support FAQ system, Document-level Visual Question answering system based on RocketQA technique.

For more details please refer to Question Answering.

💌 Opinion Extraction and Sentiment Analysis

We build an opinion extraction system for product review and fine-grained sentiment analysis based on SKEP Model.

For more details please refer to Sentiment Analysis.

🎤 Speech Command Analysis

Integrated ASR Model, Information Extraction, we provide a speech command analysis pipeline that show how to use PaddleNLP and PaddleSpeech to solve Speech + NLP real scenarios.

For more details please refer to Speech Command Analysis.

High Performance Distributed Training and Inference

🚀 Fleet API: 4D Hybrid Distributed Training

For more super large-scale model training details please refer to GPT-3.

⚡ FasterTokenizers: High Performance Text Preprocessing Library

For more usage please refer to FasterTokenizers.

⚡ FasterGeneration: High Perforance Generation Utilities

For more usage please refer to FasterGeneration.

Community

Special Interest Group (SIG)

Welcome to join PaddleNLP SIG for contribution, eg. Dataset, Models and Toolkit.

Slack

To connect with other users and contributors, welcome to join our Slack channel.

WeChat

Scan the QR code below with your Wechat⬇️. You can access to official technical exchange group. Look forward to your participation.

Installation

Prerequisites

python >= 3.6
paddlepaddle >= 2.2

More information about PaddlePaddle installation please refer to PaddlePaddle's Website.

Python pip Installation

pip install --upgrade paddlenlp

Quick Start

Taskflow aims to provide off-the-shelf NLP pre-built task covering NLU and NLG scenario, in the meanwhile with extreamly fast infernece satisfying industrial applications.

from paddlenlp import Taskflow

# Chinese Word Segmentation
seg = Taskflow("word_segmentation")
seg("第十四届全运会在西安举办")
>>> ['第十四届', '全运会', '在', '西安', '举办']

# POS Tagging
tag = Taskflow("pos_tagging")
tag("第十四届全运会在西安举办")
>>> [('第十四届', 'm'), ('全运会', 'nz'), ('在', 'p'), ('西安', 'LOC'), ('举办', 'v')]

# Named Entity Recognition
ner = Taskflow("ner")
ner("《孤女》是2010年九州出版社出版的小说，作者是余兼羽")
>>> [('《', 'w'), ('孤女', '作品类_实体'), ('》', 'w'), ('是', '肯定词'), ('2010年', '时间类'), ('九州出版社', '组织机构类'), ('出版', '场景事件'), ('的', '助词'), ('小说', '作品类_概念'), ('，', 'w'), ('作者', '人物类_概念'), ('是', '肯定词'), ('余兼羽', '人物类_实体')]

# Dependency Parsing
ddp = Taskflow("dependency_parsing")
ddp("9月9日上午纳达尔在亚瑟·阿什球场击败俄罗斯球员梅德韦杰夫")
>>> [{'word': ['9月9日', '上午', '纳达尔', '在', '亚瑟·阿什球场', '击败', '俄罗斯', '球员', '梅德韦杰夫'], 'head': [2, 6, 6, 5, 6, 0, 8, 9, 6], 'deprel': ['ATT', 'ADV', 'SBV', 'MT', 'ADV', 'HED', 'ATT', 'ATT', 'VOB']}]

# Sentiment Analysis
senta = Taskflow("sentiment_analysis")
senta("这个产品用起来真的很流畅，我非常喜欢")
>>> [{'text': '这个产品用起来真的很流畅，我非常喜欢', 'label': 'positive', 'score': 0.9938690066337585}]

API Reference

Please find more API Reference from our readthedocs.

Citation

If you find PaddleNLP useful in your research, please consider cite

@misc{=paddlenlp,
    title={PaddleNLP: An Easy-to-use and High Performance NLP Library},
    author={PaddleNLP Contributors},
    howpublished = {\url{https://github.com/PaddlePaddle/PaddleNLP}},
    year={2021}
}

Acknowledge

We have borrowed from Hugging Face's Transformer🤗 excellent design on pretrained models usage, and we would like to express our gratitude to the authors of Hugging Face and its open source community.

License

PaddleNLP is provided under the Apache-2.0 License.

luyaojie/PaddleNLP