Almost State-of-the-art Text Generation library

Overview

Ps: we are adding transformer model soon

Text Gen 🐐

Downloads python tensorflow PyPI

Almost State-of-the-art Text Generation library

Text gen is a python library that allow you build a custom text generation model with ease 😄 Something sweet built with Tensorflow and Pytorch(coming soon) - This is the brain of Rosalove ai (https://rosalove.xyz/)

How to use it

Install text-gen

pip install -U text-gen

import the library

from text_gen import ten_textgen as ttg

Load your data. your data must be in a text format.

Download the example data from the example folder

load data

data = 'rl.csv'
text = ttg.loaddata(data)

build our Model Architeture

pipeline = ttg.tentext(text)
seq_text = pipeline.sequence(padding_method = 'pre')
configg = pipeline.configmodel(seq_text, lstmlayer = 128, activation = 'softmax', dropout = 0.25)

train model

model_history = pipeline.fit(loss = 'categorical_crossentropy', optimizer = 'adam', batch = 300, metrics = 'accuracy', epochs = 500, verbose = 0, patience = 10)

generate text using the phrase

pipeline.predict('hello love', word_length = 200, segment = True)

plot loss and accuracy

pipeline.plot_loss_accuracy()

Hyper parameter optimization

Tune your model to know the best optimizer, activation method to use.

pipeline.hyper_params(epochs = 500)
pipeline.saveModel('model')

use a saved model for prediction

#the corpus is the train text file
ttg.load_model_predict(corpus = corpus, padding_method = 'pre', modelname = '../input/model2/model2textgen.h5', sample_text = 'yo yo', word_length = 100)

Give us a star 🐉

If you want to contribute, take a look at the issues and the Futurework.md file

Contributors

Comments
  • use pipenv for managing dependencies

    use pipenv for managing dependencies

    Consider using (pipenv)[https://pypi.org/project/pipenv/] to pin your dependencies. This would allow contributors to easily reproduce the project without messing up the dependencies and its also good on the long run for maintainability

    opened by paularah 1
  • [Snyk] Security upgrade pillow from 6.2.2 to 8.3.2

    [Snyk] Security upgrade pillow from 6.2.2 to 8.3.2

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- high severity | 661/1000
    Why? Recently disclosed, Has a fix available, CVSS 7.5 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-PILLOW-1319443 | pillow:
    6.2.2 -> 8.3.2
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic

    opened by snyk-bot 0
  • Read on how to create a simple python library

    Read on how to create a simple python library

    https://towardsdatascience.com/how-to-build-your-first-python-package-6a00b02635c9

    https://medium.com/analytics-vidhya/how-to-create-a-python-library-7d5aea80cc3f

    opened by Emekaborisama 0
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    tensorflow 1.14.0 requires protobuf, which is not installed.
    tensorflow-serving-api 1.12.0 requires protobuf, which is not installed.
    tensorboard 1.14.0 requires protobuf, which is not installed.
    GPyOpt 1.2.6 requires GPy, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3180413 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Regular Expression Denial of Service (ReDoS)

    opened by Emekaborisama 0
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    tensorflow 1.14.0 requires grpcio, which is not installed.
    tensorflow 1.14.0 requires protobuf, which is not installed.
    tensorboard 1.14.0 requires protobuf, which is not installed.
    tensorboard 1.14.0 requires grpcio, which is not installed.
    parameter-sherpa 1.0.6 requires pymongo, which is not installed.
    parameter-sherpa 1.0.6 requires GPyOpt, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3092128 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Regular Expression Denial of Service (ReDoS)

    opened by Emekaborisama 0
  • [Snyk] Fix for 23 vulnerabilities

    [Snyk] Fix for 23 vulnerabilities

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    parameter-sherpa 1.0.6 requires scikit-learn, which is not installed.
    GPy 1.10.0 requires paramz, which is not installed.
    GPy 1.10.0 requires cython, which is not installed.
    GPy 1.10.0 has requirement scipy<1.5.0,>=1.3.0, but you have scipy 1.2.3.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Out-of-bounds Read
    SNYK-PYTHON-PILLOW-1055461 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Out-of-bounds Read
    SNYK-PYTHON-PILLOW-1055462 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit medium severity | 509/1000
    Why? Has a fix available, CVSS 5.9 | Out-of-bounds Write
    SNYK-PYTHON-PILLOW-1059090 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Out-of-bounds Read
    SNYK-PYTHON-PILLOW-1080635 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-PILLOW-1080654 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Denial of Service (DoS)
    SNYK-PYTHON-PILLOW-1081494 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Denial of Service (DoS)
    SNYK-PYTHON-PILLOW-1081501 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Denial of Service (DoS)
    SNYK-PYTHON-PILLOW-1081502 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 654/1000
    Why? Has a fix available, CVSS 8.8 | Heap-based Buffer Overflow
    SNYK-PYTHON-PILLOW-1082329 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Insufficient Validation
    SNYK-PYTHON-PILLOW-1082750 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Denial of Service (DoS)
    SNYK-PYTHON-PILLOW-1090584 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Denial of Service (DoS)
    SNYK-PYTHON-PILLOW-1090586 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Denial of Service (DoS)
    SNYK-PYTHON-PILLOW-1090587 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Denial of Service (DoS)
    SNYK-PYTHON-PILLOW-1090588 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Out-of-bounds Read
    SNYK-PYTHON-PILLOW-1292150 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit high severity | 589/1000
    Why? Has a fix available, CVSS 7.5 | Out-of-bounds Read
    SNYK-PYTHON-PILLOW-1292151 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit medium severity | 566/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.6 | Buffer Overflow
    SNYK-PYTHON-PILLOW-1316216 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit medium severity | 414/1000
    Why? Has a fix available, CVSS 4 | Out-of-Bounds
    SNYK-PYTHON-PILLOW-574573 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit medium severity | 414/1000
    Why? Has a fix available, CVSS 4 | Out-of-bounds Read
    SNYK-PYTHON-PILLOW-574574 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit medium severity | 414/1000
    Why? Has a fix available, CVSS 4 | Out-of-bounds Read
    SNYK-PYTHON-PILLOW-574575 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit medium severity | 414/1000
    Why? Has a fix available, CVSS 4 | Out-of-bounds Read
    SNYK-PYTHON-PILLOW-574576 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit medium severity | 469/1000
    Why? Has a fix available, CVSS 5.1 | Buffer Overflow
    SNYK-PYTHON-PILLOW-574577 | pillow:
    6.2.2 -> 8.1.0
    | No | No Known Exploit low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-SCIKITLEARN-1079100 | scikit-learn:
    0.20.4 -> 0.24.2
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic

    opened by snyk-bot 0
Releases(v1.9.0)
Owner
Emeka boris ama
Machine Learning Engineer, Data Scientist, Youtuber and Advocacy
Emeka boris ama
LeBenchmark: a reproducible framework for assessing SSL from speech

LeBenchmark: a reproducible framework for assessing SSL from speech

11 Nov 30, 2022
Fast, general, and tested differentiable structured prediction in PyTorch

Torch-Struct: Structured Prediction Library A library of tested, GPU implementations of core structured prediction algorithms for deep learning applic

HNLP 1.1k Dec 16, 2022
A demo for end-to-end English and Chinese text spotting using ABCNet.

ABCNet_Chinese A demo for end-to-end English and Chinese text spotting using ABCNet. This is an old model that was trained a long ago, which serves as

Yuliang Liu 45 Oct 04, 2022
PhoNLP: A BERT-based multi-task learning toolkit for part-of-speech tagging, named entity recognition and dependency parsing

PhoNLP is a multi-task learning model for joint part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing. Experiments on Vietnamese benchmark datasets show that PhoNLP prod

VinAI Research 109 Dec 02, 2022
本项目是作者们根据个人面试和经验总结出的自然语言处理(NLP)面试准备的学习笔记与资料,该资料目前包含 自然语言处理各领域的 面试题积累。

【关于 NLP】那些你不知道的事 作者:杨夕、芙蕖、李玲、陈海顺、twilight、LeoLRH、JimmyDU、艾春辉、张永泰、金金金 介绍 本项目是作者们根据个人面试和经验总结出的自然语言处理(NLP)面试准备的学习笔记与资料,该资料目前包含 自然语言处理各领域的 面试题积累。 目录架构 一、【

1.4k Dec 30, 2022
A workshop with several modules to help learn Feast, an open-source feature store

Workshop: Learning Feast This workshop aims to teach users about Feast, an open-source feature store. We explain concepts & best practices by example,

Feast 52 Jan 05, 2023
Blazing fast language detection using fastText model

Luga A blazing fast language detection using fastText's language models Luga is a Swahili word for language. fastText provides a blazing fast language

Prayson Wilfred Daniel 18 Dec 20, 2022
A natural language modeling framework based on PyTorch

Overview PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapi

Meta Research 6.4k Jan 08, 2023
Various Algorithms for Short Text Mining

Short Text Mining in Python Introduction This package shorttext is a Python package that facilitates supervised and unsupervised learning for short te

Kwan-Yuet 466 Dec 06, 2022
Use AutoModelForSeq2SeqLM in Huggingface Transformers to train COMET

Training COMET using seq2seq setting Use AutoModelForSeq2SeqLM in Huggingface Transformers to train COMET. The codes are modified from run_summarizati

tqfang 9 Dec 17, 2022
A raytrace framework using taichi language

ti-raytrace The code use Taichi programming language Current implement acceleration lvbh disney brdf How to run First config your anaconda workspace,

蕉太狼 73 Dec 11, 2022
Tool which allow you to detect and translate text.

Text detection and recognition This repository contains tool which allow to detect region with text and translate it one by one. Description Two pretr

Damian Panek 176 Nov 28, 2022
A paper list of pre-trained language models (PLMs).

Large-scale pre-trained language models (PLMs) such as BERT and GPT have achieved great success and become a milestone in NLP.

RUCAIBox 124 Jan 02, 2023
Entity Disambiguation as text extraction (ACL 2022)

ExtEnD: Extractive Entity Disambiguation This repository contains the code of ExtEnD: Extractive Entity Disambiguation, a novel approach to Entity Dis

Sapienza NLP group 121 Jan 03, 2023
Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables

Mortgage-Application-Analysis Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables: age, in

1 Jan 29, 2022
Anuvada: Interpretable Models for NLP using PyTorch

Anuvada: Interpretable Models for NLP using PyTorch So, you want to know why your classifier arrived at a particular decision or why your flashy new d

EDGE 102 Oct 01, 2022
The source code of "Language Models are Few-shot Multilingual Learners" (MRL @ EMNLP 2021)

Language Models are Few-shot Multilingual Learners Paper This is the source code of the paper [Arxiv] [ACL Anthology]: This code has been written usin

Genta Indra Winata 45 Nov 21, 2022
This is my reading list for my PhD in AI, NLP, Deep Learning and more.

This is my reading list for my PhD in AI, NLP, Deep Learning and more.

Zhong Peixiang 156 Dec 21, 2022
Translators - is a library which aims to bring free, multiple, enjoyable translation to individuals and students in Python

Translators - is a library which aims to bring free, multiple, enjoyable translation to individuals and students in Python

UlionTse 907 Dec 27, 2022
GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates

GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates Vibhor Agarwal, Sagar Joglekar, Anthony P. Young an

Vibhor Agarwal 2 Jun 30, 2022