README_en.md

English | 简体中文

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Introduction

PaddleNLP aims to accelerate NLP applications through powerful model zoo, easy-to-use API and high performance distributed training. It's also the NLP best practice for PaddlePaddle 2.0 API system.

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 and End-to-End 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 Distributed Training

• We provide a highly optimized ditributed training implementation for BERT with Fleet API, bnd based the mixed precision training strategy based on PaddlePaddle 2.0, it can fully utilize GPU clusters for large-scale model pre-training.

Installation

Prerequisites

• python >= 3.6
• paddlepaddle >= 2.0.0

pip install paddlenlp>=2.0.0rc

Quick Start

Quick Dataset Loading

from paddlenlp.datasets import load_dataset

train_ds, dev_ds, test_ds = load_dataset("chnsenticorp", splits=["train", "dev", "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

Rich Chinsese Pre-trained Models

from paddlenlp.transformers import ErnieModel, BertModel, RobertaModel, ElectraModel, GPT2ForPretraining

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')
gpt2 = GPT2ForPretraining.from_pretrained('gpt2-base-cn')

For more pretrained model selection, please refer to Pretrained-Models

便捷获取文本特征

import paddle
from paddlenlp.transformers import ErnieTokenizer, ErnieModel

tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
model = ErnieModel.from_pretrained('ernie-1.0')

text = tokenizer('自然语言处理')
pooled_output, sequence_output = model.forward(input_ids=paddle.to_tensor([text['input_ids']]))

Model Zoo and Applications

For model zoo introduction please refer toPaddleNLP Model Zoo. As for applicaiton senario please refer to PaddleNLP Examples。

• Word Embedding
• Lexical Analysis
• Name Entity Recognition
• Language Model
• Text Classification
• Text Gneeration
• Semantic Maching
• Text Graph
• Information Extraction
• General Dialogue
• Machine Translation
• Machine Readeng Comprehension

Advanced Application

• Model Compression

API Usage

• Transformer API
• Data API
• Dataset API
• Embedding API
• Metrics 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 simple API to finish LSTM model and solve sentiment analysis task.

• Sentiment Analysis with ERNIE shows how to exploit the pretrained ERNIE to solve sentiment analysis problem.

• Waybill Information Extraction with BiGRU-CRF Model shows how to make use of Bi-GRU plus CRF to finish information extraction task.

• Waybill Information Extraction with ERNIE shows how to use ERNIE, the Chinese pre-trained model improve information extraction performance.

• Use TCN Model to predict COVID-19 confirmed cases

Community

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

License

PaddleNLP is provided under the Apache-2.0 License.