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。

.. toctree::
   :maxdepth: 3

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

Transformer预训练模型适用任务汇总

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).
• Zhang, zhengyan, et al. "CPM: A Large-scale Generative Chinese Pre-trained Language Model." arXiv preprint arXiv:2012.00413 (2020).
• 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).