Introduction

UNF(Universal NLP Framework) is built on pytorch and torchtext. Its design philosophy is：

• modularity: specifically, on the one hand, it is convenient to quickly run some nlp-related tasks; on the other hand, it is convenient for secondary development and research to implement some new models or technologies.
• efficiency: supports distributed training and half-precision training, which is convenient for quickly training the model, although the current support is relatively crude
• comprehensive: support pytorch trace into static graph, support c ++ server, provide web-server for debugging tools

Support Tasks

Now, support text classification and sequence labeling related tasks.

Related Papers

• Convolutional Neural Networks for Sentence Classification 2014
• Bag of Tricks for Efficient Text Classification 2016
• Deep Pyramid Convolutional Neural Networks for Text Categorization 2017, ACL
• Hierarchical Attention Networks for Document Classification 2017, ACL
• A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING 2017,ICLR
• Joint Embedding of Words and Labels for Text Classification2018,ACL
• Neural Architectures for Named Entity 2016,ACL
• Semi-supervised Multitask Learning for Sequence Labeling 2017, ACL

Framwork

Module relation

Module name	Module function
UNF.data	Load data from disk to RAM, include batch, padding,numerical
UNF.module	Neural network layer, include encoder, decoder, embedding, provided for use by the model
UNF.model	Neural network model structure, include DpCnn, SelAttention,Lstm-crf..and python predictor for those models
UNF.training	Model training, include early stopping, model save and reload, visualize metrics throuth Tensorboard
UNF.tracing	Trace pytorch dynamic graph to static graph, and provide c++ serving
UNF.web_server	Web server tool related

Requirement

python3

pip3 install -r requirement.txt

Training

#quick start
python3 train_flow.py

Only 5 line code need

#data loader
data_loader = DataLoader(data_loader_conf)
train_iter, dev_iter, test_iter = data_loader.generate_dataset()

#model loader
model, model_conf = ModelLoader.from_params(model_conf, data_loader.fields)

#learner loader
learner = LearnerLoader.from_params(model, train_iter, dev_iter, learner_conf, test_iter=test_iter, fields=data_loader.fields, model_conf=model_conf)

#learning
learner.learn()

train_flow.py is the demo code for training，Run directly！！

conf for multu-gpu and mixed precision

"use_fp16": False,
"multi_gpu": False

tensorboard demo

Python inference

#quick start
python3 score_flow.py

#core code
from models.predictor import Predictor

predictor = Predictor(model_path, device, model_type)
logits = predictor.predict(input)

(0.18, -0.67)

C++ inference

step1: Trace dynamic graph to static graph

#quick start
python3 trace.py

#core code
net = globals()[model_cls](**config.__dict__)
net.load_state_dict_trace(torch.load("%s/best.th" % model_path))
net.eval()

mock_input = net.mock_input_data()
tr = torch.jit.trace(net, mock_input)
tr.save("trace/%s" % save_path)

step2: c++ serving

• install cmake
• download libtorch and unzip to trace folder

cd trace
cmake -DCMAKE_PREFIX_PATH=libtorch .

make

./predict trace.pt predict_vocab.txt
output: 2.2128 -2.3287

RESTFUL-API web demo

cd web_server
python run.py