PaddlePALM (PArallel Learning from Multi-tasks) is a flexible, general and easy-to-use NLP large-scale pretraining and multi-task learning friendly framework. PALM is a high level framework aiming at **fastly** develop **high-performance** NLP models.
##### Features:
-**Easy-to-use:** with PALM, *8 steps* to achieve a typical NLP task. Moreover, the model backbone, dataset reader and task output layers have been decoupled, which allows the replacement of any component to other candidates with quite minor changes of your code.
-**Multi-task Learning friendly:***6 steps* to achieve multi-task learning for prepared tasks.
-**Large Scale and Pre-training freiendly:** automatically utilize multi-gpus (if exists) to accelerate training and inference. Minor codes is required for distributed training on clusters.
-**Popular NLP Backbones and Pre-trained models:** multiple state-of-the-art general purpose model architectures and pretrained models (e.g., BERT,ERNIE,RoBERTa,...) are built-in.
-**Easy to Customize:** support customized development of any component (e.g, backbone, task head, reader and optimizer) with reusement of pre-defined ones, which gives developers high flexibility and effeciency to adapt for diverse NLP scenes.
With PaddlePALM, it is easy to achieve effecient exploration of robust learning of reading comprehension models with multiple auxilary tasks, and the produced model, [D-Net](), achieve **the 1st place** in [EMNLP2019 MRQA](mrqa.github.io) track.
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Beyond the research scope, PaddlePALM has been applied on **Baidu Search Engine** to seek for more accurate user query understanding and answer mining, which implies the high reliability and performance of PaddlePALM.
##### Features:
-**Easy-to-use:** with PALM, *8 steps* to achieve a typical NLP task. Moreover, the model backbone, dataset reader and task output layers have been decoupled, which allows the replacement of any component to other candidates with quite minor changes of your code.
-**Multi-task Learning friendly:***6 steps* to achieve multi-task learning for prepared tasks.
-**Large Scale and Pre-training freiendly:** automatically utilize multi-gpus (if exists) to accelerate training and inference. Minor codes is required for distributed training on clusters.
-**Popular NLP Backbones and Pre-trained models:** multiple state-of-the-art general purpose model architectures and pretrained models (e.g., BERT,ERNIE,RoBERTa,...) are built-in.
-**Easy to Customize:** support customized development of any component (e.g, backbone, task head, reader and optimizer) with reusement of pre-defined ones, which gives developers high flexibility and effeciency to adapt for diverse NLP scenes.
You can easily re-produce following competitive results with minor codes, which covers most of NLP tasks such as classification, matching, sequence labeling, reading comprehension, dialogue understanding and so on. More details can be found in `examples`.
