@@ -19,7 +19,7 @@ Beyond the research scope, PaddlePALM has been applied on **Baidu Search Engine*
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@@ -19,7 +19,7 @@ Beyond the research scope, PaddlePALM has been applied on **Baidu Search Engine*
-**Easy-to-use:** with PALM, *8 steps* to achieve a typical NLP task. Moreover, all basic components (e.g., the model backbone, dataset reader, task output head, optimizer...) have been decoupled, which allows the replacement of any component to other candidates with quite minor changes of your code.
-**Easy-to-use:** with PALM, *8 steps* to achieve a typical NLP task. Moreover, all basic components (e.g., the model backbone, dataset reader, task output head, optimizer...) 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.
-**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.
-**Large Scale and Pre-training friendly:** 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.
-**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.
-**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.
