auto_code_complete is a auto word-completetion program which allows you to customize it on your need

Last update: Feb 22, 2022

Overview

auto_code_complete v1.3

purpose and usage

auto_code_complete is a auto word-completetion program which allows you to customize it on your needs. the model for this program is a combined model of a deep-learning NLP(Natural Language Process) model structure called 'GRU(gated recurrent unit)' and 'LSTM(Long Short Term Memory)'.

the model for this program is one of the deep-learning NLP(Natural Language Process) model structure called 'GRU(gated recurrent unit)'.

data preprocessing

model structure

how to use (terminal)

first, download the repository on your local environment.
install the neccessary libraries on your dependent environment.

pip install -r requirements.txt

change your working directory to auto-complete/ and execute the line below

python -m auto_complete_model

it will require for you to enter the data you want to train with the model

ENTER THE CODE YOU WANT TO TRAIN IN YOUR MODEL : tensorflow tf.keras tf.keras.layers LSTM
==== TRAINING START ====
2022-01-08 18:24:14.308919: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
Epoch 1/100
3/3 [==============================] - 1s 59ms/step - loss: 4.7865 - acc: 0.0532
Epoch 2/100
3/3 [==============================] - 0s 62ms/step - loss: 3.9297 - acc: 0.2872
Epoch 3/100
3/3 [==============================] - 0s 58ms/step - loss: 2.9941 - acc: 0.5532
...
Epoch 31/100
3/3 [==============================] - 0s 75ms/step - loss: 0.2747 - acc: 0.8617
Epoch 32/100
3/3 [==============================] - 0s 65ms/step - loss: 0.2700 - acc: 0.8298
==== TRAINING DONE ====
Now, Load the best weights on your model.

if you input your dataset successfully, it will ask for any uncompleted word to be entered.

ENTER THE UNCOMPLETED CODE YOU WANT TO COMPLETE : t tf te l la li k ke tf.kera tf.keras.l
t  - best recommendation : tensorflow
		 - all recommendations :  ['tensorflow']
tf  - best recommendation : tf.keras
		 - all recommendations :  ['tfkeras', 'tf.keras']
te  - best recommendation : tensorflow
		 - all recommendations :  ['tensorflow']
l  - best recommendation : list
		 - all recommendations :  ['list', 'layers']
la  - best recommendation : lange
		 - all recommendations :  ['layers', 'lange']
li  - best recommendation : list
		 - all recommendations :  ['list']
k  - best recommendation : keras
		 - all recommendations :  ['keras']
ke  - best recommendation : keras
		 - all recommendations :  ['keras']
tf.kera  - best recommendation : tf.keras
		 - all recommendations :  []
tf.keras.l  - best recommendation : tf.keras.layers
		 - all recommendations :  ['tf.keras.layers']

it will return the best matched word to complete and other recommendations

Do you want to check only the recommendations? (y/n) : y
['tensorflow'], 
['tfkeras', 'tf.keras'], 
['tensorflow'], 
['list', 'layers'], 
['layers', 'lange'], 
['list'], 
['keras'], 
['keras'], 
[], 
['tf.keras.layers']

version update & issues

v1.2 update

2022.01.08

change deep-learning model from GRU to GRU+LSTM to improve the performance

By adding the same structrue of new LSTM layers to concatenate before the output layer to an existing model, it shows faster learning and better accuracies in predicting matched recommendations for given incomplete words.

v1.3.1 update

2022.01.09

fix the glitches in data preprocessing

We solved the problem that it wouldn't add a new dataset on an existing dataset.

add plot_history function in a model class

v1.3.2 update

2022.01.10

add model_save,model_load mode in order that users can save and load their model while training a customized model
add data_split mode so that the big data can be trained seperately.

samp_model = auto_coding(new_code=samp_text,
                      # verbose=0,
                       batch_size=100,
                       epochs=200,
                       patience=10,
                       model_summary=True,
                       model_save=True,
                       model_name='samp_test', # samp_test/samp_test.h5
                       model_load=True,
                       data_split=True,
                       data_split_num=3 # the number into which users want to split the data
                      )

v1.3.3 update

2022.01.11

add new metrics Accuracy for Recommendations to evaluate the model's instant performance when predicting the recommendation list for words.

t  - best match : tf
	 - all recommendations :  ['tensorflow', 'tf']
tup  - best match : tuple
	 - all recommendations :  []
p  - best match : pd
	 - all recommendations :  ['plt', 'pd', 'pandas']
li  - best match : list
	 - all recommendations :  []
d  - best match : dataset
	 - all recommendations :  ['dic', 'dataset']
I  - best match : Import
	 - all recommendations :  []
so  - best match : sort
	 - all recommendations :  ['sort']
m  - best match : matplotlib.pyplot
	 - all recommendations :  []
Accuracy for Best:  0.875
Accuracy for Recommendations :  1.0

auto_code_complete is a auto word-completetion program which allows you to customize it on your need

Related tags

Overview

auto_code_complete v1.3

purpose and usage

data preprocessing

model structure

how to use (terminal)

version update & issues

v1.2 update

v1.3.1 update

v1.3.2 update

v1.3.3 update

Owner

RUO

Revisiting Pre-trained Models for Chinese Natural Language Processing (Findings of EMNLP 2020)

숭실대학교 컴퓨터학부 전공종합설계프로젝트

CPC-big and k-means clustering for zero-resource speech processing

Predicting the usefulness of reviews given the review text and metadata surrounding the reviews.

Simple, Fast, Powerful and Easily extensible python package for extracting patterns from text, with over than 60 predefined Regular Expressions.

Code for the paper: Sequence-to-Sequence Learning with Latent Neural Grammars

Code for paper Multitask-Finetuning of Zero-shot Vision-Language Models

Idea is to build a model which will take keywords as inputs and generate sentences as outputs.

Python library to make development of portfolio analysis faster and easier

Weird Sort-and-Compress Thing

Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE

NumPy String-Indexed is a NumPy extension that allows arrays to be indexed using descriptive string labels

This is the main repository of open-sourced speech technology by Huawei Noah's Ark Lab.

BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions

Chinese segmentation library

(ACL-IJCNLP 2021) Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models.

SimCTG - A Contrastive Framework for Neural Text Generation

An open source library for deep learning end-to-end dialog systems and chatbots.

Ecommerce product title recognition package

TextFlint is a multilingual robustness evaluation platform for natural language processing tasks,