README_en.md

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Introduction

PaddleNLP aims to accelerate NLP applications through powerful model zoo, easy-to-use API with detailed tutorials, It's also the NLP best practice for PaddlePaddle 2.0 API system.

This project is still UNDER ACTIVE DEVELOPMENT.

Features

• Rich and Powerful Model Zoo
  • Our Model Zoo covers mainstream NLP applications, including Lexical Analysis, Syntactic Parsing, Machine Translation, Text Classification, Text Generation, Text Matching, General Dialogue and Question Answering etc.
• Easy-to-use API
  • The API is fully integrated with PaddlePaddle high-level API system. It minimizes the number of user actions required for common use cases like data loading, text pre-processing, training and evaluation. which enables you to deal with text problems more productively.
• High Performance and Large-scale Training
  • We provide a highly optimized ditributed training implementation for BERT with Fleet API, it can fully utilize GPU clusters for large-scale model pre-training. Please refer to our benchmark for more information.
• Detailed Tutorials and Industrial Practices
  • We offers detailed and interactable notebook tutorials to show you the best practices of PaddlePaddle 2.0.

Installation

Prerequisites

• python >= 3.6
• paddlepaddle >= 2.0.0-rc1

pip install paddlenlp>=2.0.0a

Quick Start

Quick Dataset Loading

from paddlenlp.datasets import ChnSentiCorp

train_ds, test_ds = ChnSentiCorp.get_datasets(['train','test'])

Chinese Text Emebdding Loading

from paddlenlp.embeddings import TokenEmbedding

wordemb = TokenEmbedding("w2v.baidu_encyclopedia.target.word-word.dim300")
print(wordemb.cosine_sim("国王", "王后"))
>>> 0.63395125
wordemb.cosine_sim("艺术", "火车")
>>> 0.14792643

One-Line Classical Model Building

from paddlenlp.models import Ernie

ernie = Ernie(Ernie.Task.SeqCls)
ernie.forward(input_ids, segment_ids)

Rich Chinsese Pre-trained Models

from paddlenlp.transformers import ErnieModel, BertModel, RobertaModel, ElectraModel

ernie = ErnieModel.from_pretrained('ernie-1.0')
bert = BertModel.from_pretrained('bert-wwm-chinese')
roberta = RobertaModel.from_pretrained('roberta-wwm-ext')
electra = ElectraModel.from_pretrained('chinese-electra-small')

For more pretrained model selection, please refer to PretrainedModels

API Usage

• Transformer API

• Dataset API

• Embedding API

• Metrics API

• Models API

Tutorials

Please refer to our official AI Studio account for more interactive tutorials: PaddleNLP on AI Studio

• What's Seq2Vec? shows how to use LSTM to do sentiment analysis.

• Sentiment Analysis with ERNIE shows how to exploit the pretrained ERNIE to make sentiment analysis better.

• Waybill Information Extraction with BiGRU-CRF Model shows how to make use of bigru and crf to do information extraction.

• Waybill Information Extraction with ERNIE shows how to exploit the pretrained ERNIE to do information extraction better.

Community

Join our QQ Technical Group for technical exchange right now! ⬇️

License

PaddleNLP is provided under the Apache-2.0 License.