PaddleNLP Transformer预训练模型

随着深度学习的发展，NLP领域涌现了一大批高质量的Transformer类预训练模型，多次刷新了不同NLP任务的SOTA（State of the Art），极大地推动了自然语言处理的进展。 PaddleNLP为用户提供了常用的预训练模型及其相应权重，如 BERT、ERNIE、ALBERT、RoBERTa、XLNet 等，采用统一的API进行加载、训练和调用， 让开发者能够方便快捷地应用各种Transformer类预训练模型及其下游任务，且相应预训练模型权重下载速度快、稳定。

预训练模型使用方法

PaddleNLP Transformer API在提供丰富预训练模型的同时，也降低了用户的使用门槛。 使用Auto模块，可以加载不同网络结构的预训练模型，无需查找模型对应的类别。只需十几行代码，用户即可完成模型加载和下游任务Fine-tuning。

from functools import partial
import numpy as np

import paddle
from paddlenlp.datasets import load_dataset
from paddlenlp.transformers import AutoModelForSequenceClassification, AutoTokenizer

train_ds = load_dataset("chnsenticorp", splits=["train"])

model = AutoModelForSequenceClassification.from_pretrained("bert-wwm-chinese", num_classes=len(train_ds.label_list))

tokenizer = AutoTokenizer.from_pretrained("bert-wwm-chinese")

def convert_example(example, tokenizer):
    encoded_inputs = tokenizer(text=example["text"], max_seq_len=512, pad_to_max_seq_len=True)
    return tuple([np.array(x, dtype="int64") for x in [
            encoded_inputs["input_ids"], encoded_inputs["token_type_ids"], [example["label"]]]])
train_ds = train_ds.map(partial(convert_example, tokenizer=tokenizer))

batch_sampler = paddle.io.BatchSampler(dataset=train_ds, batch_size=8, shuffle=True)
train_data_loader = paddle.io.DataLoader(dataset=train_ds, batch_sampler=batch_sampler, return_list=True)

optimizer = paddle.optimizer.AdamW(learning_rate=0.001, parameters=model.parameters())

criterion = paddle.nn.loss.CrossEntropyLoss()

for input_ids, token_type_ids, labels in train_data_loader():
    logits = model(input_ids, token_type_ids)
    loss = criterion(logits, labels)
    loss.backward()
    optimizer.step()
    optimizer.clear_grad()

上面的代码给出使用预训练模型的简要示例，更完整详细的示例代码， 可以参考：使用预训练模型Fine-tune完成中文文本分类任务

1. 加载数据集：PaddleNLP内置了多种数据集，用户可以一键导入所需的数据集。

2. 加载预训练模型：PaddleNLP的预训练模型可以很容易地通过 from_pretrained() 方法加载。 Auto模块（包括AutoModel, AutoTokenizer, 及各种下游任务类）提供了方便易用的接口， 无需指定类别，即可调用不同网络结构的预训练模型。 第一个参数是汇总表中对应的 Pretrained Weight，可加载对应的预训练权重。 AutoModelForSequenceClassification 初始化 __init__ 所需的其他参数，如 num_classes 等， 也是通过 from_pretrained() 传入。Tokenizer 使用同样的 from_pretrained 方法加载。

3. 通过 Dataset 的 map 函数，使用 tokenizer 将 dataset 从原始文本处理成模型的输入。

4. 定义 BatchSampler 和 DataLoader，shuffle数据、组合Batch。

5. 定义训练所需的优化器，loss函数等，就可以开始进行模型fine-tune任务。

Transformer预训练模型汇总

PaddleNLP的Transformer预训练模型包含从 huggingface.co 直接转换的模型权重和百度自研模型权重，方便社区用户直接迁移使用。 目前共包含了40多个主流预训练模型，500多个模型权重。

• ALBERT
• BART
• BERT
• BigBird
• Blenderbot
• Blenderbot-Small
• ChineseBert
• ConvBert
• CTRL
• Deberta
• DebertaV2
• DistilBert
• ELECTRA
• ERNIE
• ERNIE-CTM
• ERNIE-DOC
• ERNIE-GEN
• ERNIE-GRAM
• ERNIE-M
• FNet
• Funnel
• GPT
• LayoutLM
• LayoutLMV2
• LayoutXLM
• Luke
• MBart
• MegatronBert
• MobileBert
• MPNet
• NeZha
• PPMiniLM
• ProphetNet
• Reformer
• RemBert
• RoBERTa
• RoFormer
• SKEP
• SqueezeBert
• T5
• TinyBert
• UnifiedTransformer
• UNIMO
• XLNet

Transformer预训练模型适用任务汇总

Model	Sequence Classification	Token Classification	Question Answering	Text Generation	Multiple Choice
ALBERT	✅	✅	✅	❌	✅
BART	✅	✅	✅	✅	❌
BERT	✅	✅	✅	❌	✅
BigBird	✅	✅	✅	❌	✅
Blenderbot	❌	❌	❌	✅	❌
Blenderbot-Small	❌	❌	❌	✅	❌
ChineseBert	✅	✅	✅	❌	❌
ConvBert	✅	✅	✅	❌	✅
CTRL	✅	❌	❌	❌	❌
Deberta	✅	✅	✅	❌	✅
DebertaV2	✅	✅	✅	❌	✅
DistilBert	✅	✅	✅	❌	❌
ELECTRA	✅	✅	✅	❌	✅
ERNIE	✅	✅	✅	❌	✅
ERNIE-CTM	❌	✅	❌	❌	❌
ERNIE-DOC	✅	✅	✅	❌	❌
ERNIE-GEN	❌	❌	❌	✅	❌
ERNIE-GRAM	✅	✅	✅	❌	❌
ERNIE-M	✅	✅	✅	❌	❌
FNet	✅	✅	✅	❌	✅
Funnel	✅	✅	✅	❌	❌
GPT	✅	✅	❌	✅	❌
LayoutLM	✅	✅	❌	❌	❌
LayoutLMV2	❌	✅	❌	❌	❌
LayoutXLM	❌	✅	❌	❌	❌
Luke	❌	✅	✅	❌	❌
MBart	✅	❌	✅	❌	✅
MegatronBert	✅	✅	✅	❌	✅
MobileBert	✅	❌	✅	❌	❌
MPNet	✅	✅	✅	❌	✅
NeZha	✅	✅	✅	❌	✅
PPMiniLM	✅	❌	❌	❌	❌
ProphetNet	❌	❌	❌	✅	❌
Reformer	✅	❌	✅	❌	❌
RemBert	✅	✅	✅	❌	✅
RoBERTa	✅	✅	✅	❌	✅
RoFormer	✅	✅	✅	❌	❌
SKEP	✅	✅	❌	❌	❌
SqueezeBert	✅	✅	✅	❌	❌
T5	❌	❌	❌	✅	❌
TinyBert	✅	❌	❌	❌	❌
UnifiedTransformer	❌	❌	❌	✅	❌
XLNet	✅	✅	✅	❌	✅

Reference

• 部分中文预训练模型来自： brightmart/albert_zh, ymcui/Chinese-BERT-wwm, huawei-noah/Pretrained-Language-Model/TinyBERT, ymcui/Chinese-XLNet, huggingface/xlnet_chinese_large, Knover/luge-dialogue, huawei-noah/Pretrained-Language-Model/NEZHA-PyTorch/, ZhuiyiTechnology/simbert

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• Lewis, Mike, et al. “BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.” arXiv preprint arXiv:1910.13461 (2019).

• Devlin, Jacob, et al. “Bert: Pre-training of deep bidirectional transformers for language understanding.” arXiv preprint arXiv:1810.04805 (2018).

• Zaheer, Manzil, et al. “Big bird: Transformers for longer sequences.” arXiv preprint arXiv:2007.14062 (2020).

• Stephon, Emily, et al. “Blenderbot: Recipes for building an open-domain chatbot.” arXiv preprint arXiv:2004.13637 (2020).

• Stephon, Emily, et al. “Blenderbot-Small: Recipes for building an open-domain chatbot.” arXiv preprint arXiv:2004.13637 (2020).

• Sun, Zijun, et al. “Chinesebert: Chinese pretraining enhanced by glyph and pinyin information.” arXiv preprint arXiv:2106.16038 (2021).

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• Jiang, Zihang, et al. “ConvBERT: Improving BERT with Span-based Dynamic Convolution.” arXiv preprint arXiv:2008.02496 (2020).

• Nitish, Bryan, et al. “CTRL: A Conditional Transformer Language Model for Controllable Generation.” arXiv preprint arXiv:1909.05858 (2019).

• Sanh, Victor, et al. “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.” arXiv preprint arXiv:1910.01108 (2019).

• Clark, Kevin, et al. “Electra: Pre-training text encoders as discriminators rather than generators.” arXiv preprint arXiv:2003.10555 (2020).

• Sun, Yu, et al. “Ernie: Enhanced representation through knowledge integration.” arXiv preprint arXiv:1904.09223 (2019).

• Ding, Siyu, et al. “ERNIE-Doc: A retrospective long-document modeling transformer.” arXiv preprint arXiv:2012.15688 (2020).

• Xiao, Dongling, et al. “Ernie-gen: An enhanced multi-flow pre-training and fine-tuning framework for natural language generation.” arXiv preprint arXiv:2001.11314 (2020).

• Xiao, Dongling, et al. “ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding.” arXiv preprint arXiv:2010.12148 (2020).

• Ouyang, Xuan, et al. “ERNIE-M: enhanced multilingual representation by aligning cross-lingual semantics with monolingual corpora.” arXiv preprint arXiv:2012.15674 (2020).

• Lee-Thorp, James, et al. “Fnet: Mixing tokens with fourier transforms.” arXiv preprint arXiv:2105.03824 (2021).

• Dai, Zihang, et al. “Funnel-transformer: Filtering out sequential redundancy for efficient language processing.” Advances in neural information processing systems 33 (2020): 4271-4282.

• Radford, Alec, et al. “Language models are unsupervised multitask learners.” OpenAI blog 1.8 (2019): 9.

• Xu, Yiheng, et al. “LayoutLM: Pre-training of Text and Layout for Document Image Understanding.” arXiv preprint arXiv:1912.13318 (2019).

• Xu, Yang, et al. “LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding” arXiv preprint arXiv:2012.14740 (2020).

• Xu, Yiheng, et al. “LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding” arXiv preprint arXiv:2104.08836 (2021).

• Yamada, Ikuya, et al. “Luke: deep contextualized entity representations with entity-aware self-attention.” arXiv preprint arXiv:2010.01057 (2020).

• Liu, Yinhan, et al. “MBart: Multilingual Denoising Pre-training for Neural Machine Translation” arXiv preprint arXiv:2001.08210 (2020).

• Shoeybi, Mohammad, et al. “Megatron-lm: Training multi-billion parameter language models using model parallelism.” arXiv preprint arXiv:1909.08053 (2019).

• Sun, Zhiqing, et al. “MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices” arXiv preprint arXiv:2004.02984 (2020).

• Song, Kaitao, et al. “MPNet: Masked and Permuted Pre-training for Language Understanding.” arXiv preprint arXiv:2004.09297 (2020).

• Wei, Junqiu, et al. “NEZHA: Neural contextualized representation for chinese language understanding.” arXiv preprint arXiv:1909.00204 (2019).

• Qi, Weizhen, et al. “Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training.” arXiv preprint arXiv:2001.04063 (2020).

• Kitaev, Nikita, et al. “Reformer: The efficient Transformer.” arXiv preprint arXiv:2001.04451 (2020).

• Chung, Hyung Won, et al. “Rethinking embedding coupling in pre-trained language models.” arXiv preprint arXiv:2010.12821 (2020).

• Liu, Yinhan, et al. “Roberta: A robustly optimized bert pretraining approach.” arXiv preprint arXiv:1907.11692 (2019).

• Su Jianlin, et al. “RoFormer: Enhanced Transformer with Rotary Position Embedding.” arXiv preprint arXiv:2104.09864 (2021).

• Tian, Hao, et al. “SKEP: Sentiment knowledge enhanced pre-training for sentiment analysis.” arXiv preprint arXiv:2005.05635 (2020).

• Forrest, ALbert, et al. “SqueezeBERT: What can computer vision teach NLP about efficient neural networks?” arXiv preprint arXiv:2006.11316 (2020).

• Raffel, Colin, et al. “T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.” arXiv preprint arXiv:1910.10683 (2019).

• Vaswani, Ashish, et al. “Attention is all you need.” arXiv preprint arXiv:1706.03762 (2017).

• Jiao, Xiaoqi, et al. “Tinybert: Distilling bert for natural language understanding.” arXiv preprint arXiv:1909.10351 (2019).

• Bao, Siqi, et al. “Plato-2: Towards building an open-domain chatbot via curriculum learning.” arXiv preprint arXiv:2006.16779 (2020).

• Yang, Zhilin, et al. “Xlnet: Generalized autoregressive pretraining for language understanding.” arXiv preprint arXiv:1906.08237 (2019).

• Cui, Yiming, et al. “Pre-training with whole word masking for chinese bert.” arXiv preprint arXiv:1906.08101 (2019).

• Wang, Quan, et al. “Building Chinese Biomedical Language Models via Multi-Level Text Discrimination.” arXiv preprint arXiv:2110.07244 (2021).