# PaddlePALM 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. 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.

Sample

MRQA2019 Leaderboard

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`.
Dataset
chnsenticorp
Quora Question Pairs matching
 MSRA-NER
(SIGHAN2006)		 CMRC2018

Metric

precision

 recall
f1-score
precision

 recall
f1-score
precision

 recall
f1-score
em
f1-score
test
test
test
dev
ERNIE Base 95.7 95.0 95.7 85.8 82.4 81.5 94.9 94.5 94.7 96.3 84.0
## Package Overview | module | illustration | | - | - | | **paddlepalm** | an open source NLP pretraining and multitask learning framework, built on paddlepaddle. | | **paddlepalm.reader** | a collection of elastic task-specific dataset readers. | | **paddlepalm.backbone** | a collection of classic NLP representation models, e.g., BERT, ERNIE, RoBERTa. | | **paddlepalm.head** | a collection of task-specific output layers. | | **paddlepalm.lr_sched** | a collection of learning rate schedualers. | | **paddlepalm.optimizer** | a collection of optimizers. | | **paddlepalm.downloader** | a download module for pretrained models with configure and vocab files. | | **paddlepalm.Trainer** | the core unit to start a single task training/predicting session. A trainer is to build computation graph, manage training and evaluation process, achieve model/checkpoint saving and pretrain_model/checkpoint loading.| | **paddlepalm.MultiHeadTrainer** | the core unit to start a multi-task training/predicting session. A MultiHeadTrainer is built based on several Trainers. Beyond the inheritance of Trainer, it additionally achieves model backbone reuse across tasks, trainer sampling for multi-task learning, and multi-head inference for effective evaluation and prediction. | ## Installation PaddlePALM support both python2 and python3, linux and windows, CPU and GPU. The preferred way to install PaddlePALM is via `pip`. Just run following commands in your shell environment. ```bash pip install paddlepalm ``` ### Installing via source ```shell git clone https://github.com/PaddlePaddle/PALM.git cd PALM && python setup.py install ``` ### Library Dependencies - Python >= 2.7 - cuda >= 9.0 - cudnn >= 7.0 - PaddlePaddle >= 1.6.3 (请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装) ### Downloading pretrain models We incorporate many pretrained models to initialize model backbone parameters. Training big NLP model, e.g., 12-layer transformers, with pretrained models is practically much more effective than that with randomly initialized parameters. To see all the available pretrained models and download, run following code in python interpreter (input command `python` in shell): ```python >>> from paddlepalm import downloader >>> downloader.ls('pretrain') Available pretrain items: => roberta-cn-base => roberta-cn-large => bert-cn-base => bert-cn-large => bert-en-uncased-base => bert-en-uncased-large => bert-en-cased-base => bert-en-cased-large => ernie-en-uncased-base => ernie-en-uncased-large ... >>> downloader.download('pretrain', 'bert-en-uncased-base', './pretrain_models') ... ``` ## Usage 8 steps to start a typical NLP training task. 1. use `paddlepalm.reader` to create a *reader* for dataset loading and input features generation, then call `reader.load_data` method to load your training data. 2. use `paddlepalm.backbone` to create a model *backbone* to extract text features (e.g., contextual word embedding, sentence embedding). 3. register your *reader* with your *backbone* through `reader.register_with` method. After this step, your reader is able to yield input features used by backbone. 4. use `paddlepalm.head` to create a task output *head*. This head can provide task loss for training and predicting results for model inference. 5. create a task *trainer* with `paddlepalm.Trainer`, then build forward graph with backbone and task head (created in step 2 and 4) through `trainer.build_forward`. 6. use `paddlepalm.optimizer` (and `paddlepalm.lr_sched` if is necessary) to create a *optimizer*, then build backward through `trainer.build_backward`. 7. fit prepared reader and data (achieved in step 1) to trainer with `trainer.fit_reader` method. 8. load pretrain model with `trainer.load_pretrain`, or load checkpoint with `trainer.load_ckpt` or nothing to do for training from scratch, then do training with `trainer.train`. More implementation details see following demos: [Sentiment Classification](), [Quora Question Pairs matching](), [Tagging](), [SQuAD machine Reading Comprehension](). To save models/checkpoints during training, just call `trainer.set_saver` method. More implementation details see [this](). To do predict/evaluation after a training stage, just create another three reader, backbone and head instance with `phase='predict'` (repeat step 1~4 above). Then do predicting with `predict` method in trainer (no need to create another trainer). More implementation details see [this](). To run with multi-task learning mode: 1. repeatedly create components (i.e., reader, backbone and head) for each task followed with step 1~5 above. 2. create empty trainers (each trainer is corresponded to one task) and pass them to create a `MultiHeadTrainer`. 3. build multi-task forward graph with `multi_head_trainer.build_forward` method. 4. use `paddlepalm.optimizer` (and `paddlepalm.lr_sched` if is necessary) to create a *optimizer*, then build backward through `multi_head_trainer.build_backward`. 5. fit all prepared readers and data to multi_head_trainer with `multi_head_trainer.fit_readers` method. 6. randomly initialize model parameters with `multi_head_trainer.random_init_params` (and `multi_head_trainer.load_pretrain` if needed), then do training with `multi_head_trainer.train`. The save/load and predict operations of a multi_head_trainer is the same as a trainer. More implementation details of running multi-task learning with multi_head_trainer can be found [here](). ## License This tutorial is contributed by [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) and licensed under the [Apache-2.0 license](https://github.com/PaddlePaddle/models/blob/develop/LICENSE). ## 许可证书 此向导由[PaddlePaddle](https://github.com/PaddlePaddle/Paddle)贡献，受[Apache-2.0 license](https://github.com/PaddlePaddle/models/blob/develop/LICENSE)许可认证。