update readme

Author: NaiveWang

README.md

@@ -1,43 +1,25 @@
 ![NeuralClassifier Logo](readme/logo.png)
 
 
-# NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit
+# NeuralClassifierService: A Service for Multi-label Text Classification Toolkit
 
 ## Introduction
 
-NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. It is built on [PyTorch](https://pytorch.org/). Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.
+This service is for post use of the parent repository after trained with data sets, and if you want to use your model as some DeepLearning backend, fork this web wrapper repository.
 
-## Support tasks
+## Notice
 
-* Binary-class text classifcation
-* Multi-class text classification
-* Multi-label text classification
-* Hiearchical (multi-label) text classification (HMC)
-
-## Support text encoders
-
-* TextCNN ([Kim, 2014](https://arxiv.org/pdf/1408.5882.pdf))
-* RCNN ([Lai et al., 2015](https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9745/9552))
-* TextRNN ([Liu et al., 2016](https://arxiv.org/pdf/1605.05101.pdf))
-* FastText ([Joulin et al., 2016](https://arxiv.org/pdf/1607.01759.pdf))
-* VDCNN ([Conneau et al., 2016](https://arxiv.org/pdf/1606.01781.pdf))
-* DPCNN ([Johnson and Zhang, 2017](https://www.aclweb.org/anthology/P17-1052))
-* AttentiveConvNet ([Yin and Schutze, 2017](https://arxiv.org/pdf/1710.00519.pdf))
-* DRNN ([Wang, 2018](https://www.aclweb.org/anthology/P18-1215))
-* Region embedding ([Qiao et al., 2018](http://research.baidu.com/Public/uploads/5acc1e230d179.pdf))
-* Transformer encoder ([Vaswani et al., 2017](https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf))
-* Star-Transformer encoder ([Guo et al., 2019](https://arxiv.org/pdf/1902.09113.pdf))
+* I assume you have trained your model well and you are ready to use it for production.
+* Production did not require GPU evniroment, and current repo config is using pytorch CPU opt by default
+* Please use the config file while you are training instead of using my config file.
+* I assume you are using GNU operating systems and using UTF-8 as encoding
 
 ## Requirement
 
 * Python 3
 * PyTorch 0.4+
 * Numpy 1.14.3+
-
-## System Architecture
-
-![NeuralClassifier Architecture](readme/deeptext_arc.png)
-
+* Flask
 
 ## Usage
 
@@ -59,7 +41,7 @@ The training info will be outputted in standard output and log.logger\_file.
 The evaluation info will be outputed in eval.dir.
 
 ### Prediction
-    python predict.py conf/train.json data/predict.json 
+    python predict.py conf/train.json data/predict.json
 
 * predict.json should be of json format, while each instance has a dummy label like "其他" or any other label in label map.
 * eval.model\_dir is the model to predict.
@@ -80,81 +62,31 @@ The predict info will be outputed in predict.txt.
     }
 
     "doc_keyword" and "doc_topic" are optional.
+## Start classification service
+
+This service contains two module: a deep learning backend and a flask wrapper, they communicate via socket at port 4444. Above this is a shell wrapper to manipulate the two process: start them, keep them and kill them together when signal receieved.
+
+>./start.sh #to start the whole service
+
+the web page start at '0.0.0.0', port 55555 by default, or edit them in server.py.
+
+The main page is to test if your config is valid, paste a post to the textarea,
+`purge newline`, and click `Go` to get predict results, each post is splited by a newline which explains why you should purge new lines from a single post.
+
+
+
+Also, headless json response is provided, send a json to `[host]:[port]/headless` and get a json with results
+
+**send:**
+
+{"posts":["blah","blah blah", "blah blah"]}
+
+**and get:**
+
+{"results" :["class of blah", "class of blah blah", "class of blah blah blah"]}
+
+## Demo enviroment
+
+* Training dataset : toutiao, 16 single labels, 280k posts, length 1536, eval precision 91%
 
-## Performance
-
-### 0. Dataset
-
-<table>
-<tr><th>Dataset<th>Taxonomy<th>#Label<th>#Training<th>#Test
-<tr><td>RCV1<td>Tree<td>103<td>23,149<td>781,265
-<tr><td>Yelp<td>DAG<td>539<td>87,375<td>37,265
-</table>
-
-* RCV1: [Lewis et al., 2004](http://www.jmlr.org/papers/volume5/lewis04a/lewis04a.pdf)
-* Yelp: [Yelp](https://www.yelp.com/dataset/challenge)
-
-### 1. Compare with state-of-the-art
-<table>
-<tr><th>Text Encoders<th>Micro-F1 on RCV1<th>Micro-F1 on Yelp
-<tr><td>HR-DGCNN (Peng et al., 2018)<td>0.7610<td>-
-<tr><td>HMCN (Wehrmann et al., 2018)<td>0.8080<td>0.6640
-<tr><td>Ours<td><strong>0.8313</strong><td><strong>0.6704</strong>
-</table>
-
-* HR-DGCNN: [Peng et al., 2018](http://www.cse.ust.hk/~yqsong/papers/2018-WWW-Text-GraphCNN.pdf)
-* HMCN: [Wehrmann et al., 2018](http://proceedings.mlr.press/v80/wehrmann18a/wehrmann18a.pdf)
-
-### 2. Different text encoders
-
-<table>
-<tr><th row_span='2'>Text Encoders<th colspan='2'>RCV1<th colspan='2'>Yelp
-<tr><td><th>Micro-F1<th>Macro-F1<th>Micro-F1<th>Macro-F1
-<tr><td>TextCNN<td>0.7717<td>0.5246<td>0.6281<td>0.3657
-<tr><td>TextRNN<td>0.8152<td>0.5458<td><strong>0.6704</strong><td>0.4059
-<tr><td>RCNN<td><strong>0.8313</strong><td><strong>0.6047</strong><td>0.6569<td>0.3951
-<tr><td>FastText<td>0.6887<td>0.2701 <td>0.6031<td>0.2323
-<tr><td>DRNN<td>0.7846 <td>0.5147<td>0.6579<td>0.4401
-<tr><td>DPCNN<td>0.8220 <td>0.5609 <td>0.5671 <td>0.2393
-<tr><td>VDCNN<td>0.7263 <td>0.3860<td>0.6395<td>0.4035
-<tr><td>AttentiveConvNet<td>0.7533<td>0.4373<td>0.6367<td>0.4040
-<tr><td>RegionEmbedding<td>0.7780 <td>0.4888 <td>0.6601<td><strong>0.4514</strong>
-<tr><td>Transformer<td>0.7603 <td>0.4274<td>0.6533<td>0.4121
-<tr><td>Star-Transformer<td>0.7668 <td>0.4840<td>0.6482<td>0.3895
-
-</table>
-
-### 3. Hierarchical vs Flat
-
-<table>
-<tr><th row_span='2'>Text Encoders<th colspan='2'>Hierarchical<th colspan='2'>Flat
-<tr><td><th>Micro-F1<th>Macro-F1<th>Micro-F1<th>Macro-F1
-<tr><td>TextCNN<td>0.7717<td>0.5246<td>0.7367<td>0.4224
-<tr><td>TextRNN<td>0.8152<td>0.5458<td>0.7546 <td>0.4505
-<tr><td>RCNN<td><strong>0.8313</strong><td><strong>0.6047</strong><td><strong>0.7955</strong><td><strong>0.5123</strong>
-<tr><td>FastText<td>0.6887<td>0.2701 <td>0.6865<td>0.2816
-<tr><td>DRNN<td>0.7846 <td>0.5147<td>0.7506<td>0.4450
-<tr><td>DPCNN<td>0.8220 <td>0.5609 <td>0.7423  <td>0.4261
-<tr><td>VDCNN<td>0.7263 <td>0.3860<td>0.7110<td>0.3593
-<tr><td>AttentiveConvNet<td>0.7533<td>0.4373<td>0.7511<td>0.4286
-<tr><td>RegionEmbedding<td>0.7780 <td>0.4888 <td>0.7640<td>0.4617
-<tr><td>Transformer<td>0.7603 <td>0.4274<td>0.7602<td>0.4339
-<tr><td>Star-Transformer<td>0.7668 <td>0.4840<td>0.7618<td>0.4745
-</table>
-
-## Acknowledgement
-
-Some public codes are referenced by our toolkit:
-
-* https://pytorch.org/docs/stable/
-* https://github.com/jadore801120/attention-is-all-you-need-pytorch/
-* https://github.com/Hsuxu/FocalLoss-PyTorch
-* https://github.com/Shawn1993/cnn-text-classification-pytorch
-* https://github.com/ailias/Focal-Loss-implement-on-Tensorflow/
-* https://github.com/brightmart/text_classification
-* https://github.com/NLPLearn/QANet
-* https://github.com/huggingface/pytorch-pretrained-BERT
-
-## Update
-
-* 2019-04-29, init version
+* Infer server performance : CPU, no delay

templates/root.html

@@ -66,7 +66,7 @@
           <br>
           returns:
           <br>
-          json string : {"class of blah", "class of blah blah", "class of blah blah blah"}
+          json string : {"results" :["class of blah", "class of blah blah", "class of blah blah blah"]}
     </div>
     <div class="right">
         <ul>