README.md

简体中文 | [English](./README_en.md)

# PaddleNLP

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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](./benchmark/bert) 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

```python
from paddlenlp.datasets import ChnSentiCrop

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

For more Dataset API usage, please refer to [Dataset API](./docs/datasets.md).

### Chinese Text Emebdding Loading

```python

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
```

For more token embedding usage, please refer to [examples/word_embedding](./example/../examples/word_embedding/README.md).

### One-Line Classical Model Building

```python
from paddlenlp.models import Ernie, Senta, SimNet

ernie = Ernie("ernie-1.0", num_classes=2, task="seq-cls")

senta = Senta(network="bow", vocab_size=1024, num_classes=2)

simnet = SimNet(network="gru", vocab_size=1024, num_classes=2)

```

### Rich Chinsese Pre-trained Models

```python
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 [Pretrained-Models](./docs/transformers.md)

## API Usage

* [Transformer API](./docs/transformers.md)
* [Dataset API](./docs/datasets.md)
* [Embedding API](./docs/embeddings.md)
* [Metrics API](./docs/embeddings.md)
* [Models API](./docs/models.md)

## Tutorials

Please refer to our official AI Studio account for more interactive tutorials: [PaddleNLP on AI Studio](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/574995)

## Community

* SIG for Pretrained Model Contribution
* SIG for Dataset Integration
* SIG for Tutorial Writing

## License

PaddleNLP is provided under the [Apache-2.0 License](./LICENSE).