1593526560
How to run ESLint using pre-commit hook
Developers tend to commit mistakes while coding. For example, you might forget to close a bracket or you might end up creating a variable that already exists. But, mistakes like this might end up breaking your app or site. We might push this buggy code to a cloud repository or it might take a considerable amount of time to resolve the issues caused due to such mistakes. If your code is being reviewed, it would consume your reviewer’s time to find such sloppy mistakes or they might miss it completely.
This is where linting comes in to picture. Linting is the process of running a program that will analyse code for potential errors. For linting such potential errors, we use Linters.
Linters help in making a developer write clean code. Every language might have their own linters to lint their code. Here is a link that I liked which has listed some good linters for writing cleaner code:
https://github.com/showcases/clean-code-linters
Among these, we’ll be specifically speaking about ESLint which is a modern linting tool for linting JavaScript/ECMAScript 6. As per the description on their website,
ESLint is an open source JavaScript linting utility originally created by Nicholas C. Zakas in June 2013. Code linting is a type of static analysis that is frequently used to find problematic patterns or code that doesn’t adhere to certain style guidelines.
In simple words, it is used to validate your JavaScript files against potential errors. The “ES” in ESLint stands for ECMAScript. You can create a .eslintrc configuration file and specify the rules against which you want to validate your code. You can lint your code on-the-fly in your desired editor (check if there is a plugin/API for integrating ESLint with your editor) or you can lint it using the command line tools. The choice is yours. I prefer linting on-the-fly.
#eslint #javascript #linter #git #es6
What is GEEK
Buddha Community
1686166020
PyTorch Unsupervised Sentiment Discovery
** DEPRECATED **
This repo has been deprecated. Please visit Megatron-LM for our up to date Large-scale unsupervised pretraining and finetuning code.
If you would still like to use this codebase, see our tagged releases and install required software/dependencies that was available publicly at that date.
PyTorch Unsupervised Sentiment Discovery
This codebase contains pretrained binary sentiment and multimodel emotion classification models as well as code to reproduce results from our series of large scale pretraining + transfer NLP papers: Large Scale Language Modeling: Converging on 40GB of Text in Four Hours and Practical Text Classification With Large Pre-Trained Language Models. This effort was born out of a desire to reproduce, analyze, and scale the Generating Reviews and Discovering Sentiment paper from OpenAI.
The techniques used in this repository are general purpose and our easy to use command line interface can be used to train state of the art classification models on your own difficult classification datasets.
This codebase supports mixed precision training as well as distributed, multi-gpu, multi-node training for language models (support is provided based on the NVIDIA APEx project). In addition to training language models, this codebase can be used to easily transfer and finetune trained models on custom text classification datasets.
For example, a Transformer language model for unsupervised modeling of large text datasets, such as the amazon-review dataset, is implemented in PyTorch. We also support other tokenization methods, such as character or sentencepiece tokenization, and language models using various recurrent architectures.
The learned language model can be transferred to other natural language processing (NLP) tasks where it is used to featurize text samples. The featurizations provide a strong initialization point for discriminative language tasks, and allow for competitive task performance given only a few labeled samples. For example, we consider finetuning our models on the difficult task of multimodal emotion classification based on a subset of the plutchik wheel of emotions.
Created by Robert Plutchik, this wheel is used to illustrate different emotions in a compelling and nuanced way. He suggested that there are 8 primary bipolar emotions (joy versus sadness, anger versus fear, trust versus disgust, and surprise versus anticipation) with different levels of emotional intensity. For our classification task we utilize tweets from the SemEval2018 Task 1E-c emotion classification dataset to perform multilabel classification of anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. This is a difficult task that suffers from real world classification problems such as class imbalance and labeler disagreement.
On the full SemEval emotion classification dataset we find that finetuning our model on the data achieves competitive state of the art performance with no additional domain-specific feature engineering.
Setup
Install
Install the sentiment_discovery package with python3 setup.py install in order to run the modules/scripts within this repo.
Python Requirements
At this time we only support python3.
- numpy
- pytorch (>= 0.4.1)
- pandas
- scikit-learn
- matplotlib
- unidecode
- sentencepiece
- seaborn
- emoji
Pretrained models
We've included our sentencepiece tokenizer model and vocab as a zip file:
- sentencepiece tokenizer [1MB]
We've included a transformer language model base as well as a 4096-d mlstm language model base. For examples on how to use these models please see our finetuning and transfer sections. Even though these models were trained with FP16 they can be used in FP32 training/inference.
- FP16 Transformer LM [311MB]
- FP16 mLSTM LM [169MB]
We've also included classifiers trained on a subset of SemEval emotions corresponding to the 8 plutchik emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust):
- Finetuned Plutchik Transformer [673MN]
- Finetuned Plutchik mLSTM [433MB]
Lastly, we've also included already trained classification models for SST and IMDB binary sentiment classification:
- Finetuned SST Transformer [621MB]
- Transferred SST mLSTM [325MB]
- Transferred IMDB mLSTM [325MB]
To use classification models that reproduce results from our original large batch language modeling paper please use the following commit hash and set of models.
We did not include pretrained models leveraging ELMo. To reproduce our papers' results with ELMo, please see our available resources.
Each file has a dictionary containing a PyTorch state_dict consisting of a language model (lm_encoder keys) trained on Amazon reviews and a classifier (classifier key) as well as accompanying args necessary to run a model with that state_dict.
Data Downloads
In the ./data folder we've provided processed copies of the Binary Stanford Sentiment Treebank (Binary SST), IMDB Movie Review, and the SemEval2018 Tweet Emotion datasets as part of this repository. In order to train on the amazon dataset please download the "aggressively deduplicated data" version from Julian McAuley's original site. Access requests to the dataset should be approved instantly. While using the dataset make sure to load it with the --loose-json flag.
Usage
In addition to providing easily reusable code of the core functionalities (models, distributed, fp16, etc.) of this work, we also provide scripts to perform the high-level functionalities of the original paper:
- sentiment classification of input text
- unsupervised reconstruction/language modeling of a corpus of text (+ script for launching distributed workers)
- transfer of learned language model to perform sentiment analysis on a specified corpus
- sampling from language model to generate text (possibly of fixed sentiment) + heatmap visualization of sentiment in text
Classifying text
Classify an input csv/json using one of our pretrained models or your own. Performs classification on Binary SST by default. Output classification probabilities are saved to a .npy file
python3 run_classifier.py --load_model ama_sst.pt # classify Binary SST
python3 run_classifier.py --load_model ama_sst_16.pt --fp16 # run classification in fp16
python3 run_classifier.py --load_model ama_sst.pt --text-key <text-column> --data <path.csv> # classify your own dataset
See here for more documentation.
Training Language Models (+ Distributed/FP16 Training)
Train a language model on a csv/json corpus. By default we train a weight-normalized, 4096-d mLSTM, with a 64-d character embedding. This is the first step of a 2-step process to training your own sentiment classifier. Saves model to lang_model.pt by default.
python3 pretrain.py #train a large model on imdb
python3 pretrain.py --model LSTM --nhid 512 #train a small LSTM instead
python3 pretrain.py --fp16 --dynamic-loss-scale #train a model with fp16
python3 -m multiproc pretrain.py #distributed model training
python3 pretrain.py --data ./data/amazon/reviews.json --lazy --loose-json \ #train a model on amazon data
--text-key reviewText --label-key overall --optim Adam --split 1000,1,1
python3 pretrain.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \ #train a model with our sentencepiece tokenization
--tokenizer-type bpe --tokenizer-path ama_32k_tokenizer.model
python3 pretrain.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \ #train a transformer model with our sentencepiece tokenization
--tokenizer-type bpe --tokenizer-path ama_32k_tokenizer.model --model transformer \
--decoder-layers 12 --decoder-embed-dim 768 --decoder-ffn-embed-dim 3072 \
--decoder-learned-pos --decoder-attention-heads 8
bash ./experiments/train_mlstm_singlenode.sh #run our mLSTM training script on 1 DGX-1V
bash ./experiments/train_transformer_singlenode.sh #run our transformer training script on 1 DGX-1V
For more documentation of our language modeling functionality look here
In order to learn about our language modeling experiments and reproduce results see the training reproduction section in analysis.
For information about how we achieve numerical stability with FP16 training see our fp16 training analysis.
Sentiment Transfer
Given a trained language model, this script will featurize text from train, val, and test csv/json's. It then uses sklearn logistic regression to fit a classifier to predict sentiment from these features. Lastly it performs feature selection to try and fit a regression model to the top n most relevant neurons (features). By default only one neuron is used for this second regression.
python3 transfer.py --load mlstm.pt #performs transfer to SST, saves results to `<model>_transfer/` directory
python3 transfer.py --load mlstm.pt --neurons 5 #use 5 neurons for the second regression
python3 transfer.py --load mlstm.pt --fp16 #run model in fp16 for featurization step
bash ./experiments/run_sk_sst.sh #run transfer learning with mlstm on imdb dataset
bash ./experiments/run_sk_imdb.sh #run transfer learning with mlstm on sst dataset
Additional documentation of the command line arguments available for transfer can be found here
Classifier Finetuning
Given a trained language model and classification dataset, this script will build a classifier that leverages the trained language model as a text feature encoder. The difference between this script and transfer.py is that the model training is performed end to end: the loss from the classifier is backpropagated into the language model encoder as well. This script allows one to build more complex classification models, metrics, and loss functions than transfer.py. This script supports building arbitrary multilable, multilayer, and multihead perceptron classifiers. Additionally it allows using language modeling as an auxiliary task loss during training and multihead variance as an auxiliary loss during training. Lastly this script supports automatically selecting classification thresholds from validation performance. To measure validation performance this script includes more complex metrics including: f1-score, mathew correlation coefficient, jaccard index, recall, precision, and accuracy.
python3 finetune_classifier.py --load mlstm.pt --lr 2e-5 --aux-lm-loss --aux-lm-loss-weight .02 #finetune mLSTM model on sst (default dataset) with auxiliary loss
python3 finetune_classifier.py --load mlstm.pt --automatic-thresholding --threshold-metric f1 #finetune mLSTM model on sst and automatically select classification thresholds based on the validation f1 score
python3 finetune_classifier.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \ #finetune transformer with sentencepiece on SST
--tokenizer-type bpe tokenizer-path ama_32k_tokenizer.model --model transformer --lr 2e-5 \
--decoder-layers 12 --decoder-embed-dim 768 --decoder-ffn-embed-dim 3072 \
--decoder-learned-pos --decoder-attention-heads 8 --load transformer.pt --use-final-embed
python3 finetune_classifier.py --automatic-thresholding --non-binary-cols l1 l2 l3 --lr 2e-5\ #finetune multilayer classifier with 3 classes and 4 heads per class on some custom dataset and automatically select classfication thresholds
--classifier-hidden-layers 2048 1024 3 --heads-per-class 4 --aux-head-variance-loss-weight 1. #`aux-head-variance-loss-weight` is an auxiliary loss to increase the variance between each of the 4 head's weights
--data <custom_train>.csv --val <custom_val>.csv --test <custom_test>.csv --load mlstm.pt
bash ./experiments/se_transformer_multihead.sh #finetune a multihead transformer on 8 semeval categories
See how to reproduce our finetuning experiments in the finetuning reproduction section of analysis.
Additional documentation of the command line arguments available for finetune_classifier.py can be found here
Analysis
Acknowledgement
A special thanks to our amazing summer intern Neel Kant for all the work he did with transformers, tokenization, and pretraining+finetuning classification models.
A special thanks to @csarofeen and @Michael Carilli for their help developing and documenting our RNN interface, Distributed Data Parallel model, and fp16 optimizer. The latest versions of these utilities can be found at the APEx github page.
Thanks to @guillitte for providing a lightweight pytorch port of openai's sentiment-neuron repo.
This project uses the amazon review dataset collected by J. McAuley
Thanks
Want to help out? Open up an issue with questions/suggestions or pull requests ranging from minor fixes to new functionality.
May your learning be Deep and Unsupervised.
Download details:
Author: NVIDIA
Source: https://github.com/NVIDIA/sentiment-discovery
License: View license
1620729846
Why Use WordPress? What Can You Do With WordPress?
Can you use WordPress for anything other than blogging? To your surprise, yes. WordPress is more than just a blogging tool, and it has helped thousands of websites and web applications to thrive. The use of WordPress powers around 40% of online projects, and today in our blog, we would visit some amazing uses of WordPress other than blogging.
What Is The Use Of WordPress?
WordPress is the most popular website platform in the world. It is the first choice of businesses that want to set a feature-rich and dynamic Content Management System. So, if you ask what WordPress is used for, the answer is – everything. It is a super-flexible, feature-rich and secure platform that offers everything to build unique websites and applications. Let’s start knowing them:
1. Multiple Websites Under A Single Installation
WordPress Multisite allows you to develop multiple sites from a single WordPress installation. You can download WordPress and start building websites you want to launch under a single server. Literally speaking, you can handle hundreds of sites from one single dashboard, which now needs applause.
It is a highly efficient platform that allows you to easily run several websites under the same login credentials. One of the best things about WordPress is the themes it has to offer. You can simply download them and plugin for various sites and save space on sites without losing their speed.
2. WordPress Social Network
WordPress can be used for high-end projects such as Social Media Network. If you don’t have the money and patience to hire a coder and invest months in building a feature-rich social media site, go for WordPress. It is one of the most amazing uses of WordPress. Its stunning CMS is unbeatable. And you can build sites as good as Facebook or Reddit etc. It can just make the process a lot easier.
To set up a social media network, you would have to download a WordPress Plugin called BuddyPress. It would allow you to connect a community page with ease and would provide all the necessary features of a community or social media. It has direct messaging, activity stream, user groups, extended profiles, and so much more. You just have to download and configure it.
If BuddyPress doesn’t meet all your needs, don’t give up on your dreams. You can try out WP Symposium or PeepSo. There are also several themes you can use to build a social network.
3. Create A Forum For Your Brand’s Community
Communities are very important for your business. They help you stay in constant connection with your users and consumers. And allow you to turn them into a loyal customer base. Meanwhile, there are many good technologies that can be used for building a community page – the good old WordPress is still the best.
It is the best community development technology. If you want to build your online community, you need to consider all the amazing features you get with WordPress. Plugins such as BB Press is an open-source, template-driven PHP/ MySQL forum software. It is very simple and doesn’t hamper the experience of the website.
Other tools such as wpFoRo and Asgaros Forum are equally good for creating a community blog. They are lightweight tools that are easy to manage and integrate with your WordPress site easily. However, there is only one tiny problem; you need to have some technical knowledge to build a WordPress Community blog page.
4. Shortcodes
Since we gave you a problem in the previous section, we would also give you a perfect solution for it. You might not know to code, but you have shortcodes. Shortcodes help you execute functions without having to code. It is an easy way to build an amazing website, add new features, customize plugins easily. They are short lines of code, and rather than memorizing multiple lines; you can have zero technical knowledge and start building a feature-rich website or application.
There are also plugins like Shortcoder, Shortcodes Ultimate, and the Basics available on WordPress that can be used, and you would not even have to remember the shortcodes.
5. Build Online Stores
If you still think about why to use WordPress, use it to build an online store. You can start selling your goods online and start selling. It is an affordable technology that helps you build a feature-rich eCommerce store with WordPress.
WooCommerce is an extension of WordPress and is one of the most used eCommerce solutions. WooCommerce holds a 28% share of the global market and is one of the best ways to set up an online store. It allows you to build user-friendly and professional online stores and has thousands of free and paid extensions. Moreover as an open-source platform, and you don’t have to pay for the license.
Apart from WooCommerce, there are Easy Digital Downloads, iThemes Exchange, Shopify eCommerce plugin, and so much more available.
6. Security Features
WordPress takes security very seriously. It offers tons of external solutions that help you in safeguarding your WordPress site. While there is no way to ensure 100% security, it provides regular updates with security patches and provides several plugins to help with backups, two-factor authorization, and more.
By choosing hosting providers like WP Engine, you can improve the security of the website. It helps in threat detection, manage patching and updates, and internal security audits for the customers, and so much more.
#use of wordpress #use wordpress for business website #use wordpress for website #what is use of wordpress #why use wordpress #why use wordpress to build a website
1593526560
How to run ESLint using pre-commit hook
Developers tend to commit mistakes while coding. For example, you might forget to close a bracket or you might end up creating a variable that already exists. But, mistakes like this might end up breaking your app or site. We might push this buggy code to a cloud repository or it might take a considerable amount of time to resolve the issues caused due to such mistakes. If your code is being reviewed, it would consume your reviewer’s time to find such sloppy mistakes or they might miss it completely.
This is where linting comes in to picture. Linting is the process of running a program that will analyse code for potential errors. For linting such potential errors, we use Linters.
Linters help in making a developer write clean code. Every language might have their own linters to lint their code. Here is a link that I liked which has listed some good linters for writing cleaner code:
https://github.com/showcases/clean-code-linters
Among these, we’ll be specifically speaking about ESLint which is a modern linting tool for linting JavaScript/ECMAScript 6. As per the description on their website,
ESLint is an open source JavaScript linting utility originally created by Nicholas C. Zakas in June 2013. Code linting is a type of static analysis that is frequently used to find problematic patterns or code that doesn’t adhere to certain style guidelines.
In simple words, it is used to validate your JavaScript files against potential errors. The “ES” in ESLint stands for ECMAScript. You can create a .eslintrc configuration file and specify the rules against which you want to validate your code. You can lint your code on-the-fly in your desired editor (check if there is a plugin/API for integrating ESLint with your editor) or you can lint it using the command line tools. The choice is yours. I prefer linting on-the-fly.
#eslint #javascript #linter #git #es6
1607768450
What are hooks in React JS? - INFO AT ONE
In this article, you will learn what are hooks in React JS? and when to use react hooks? React JS is developed by Facebook in the year 2013. There are many students and the new developers who have confusion between react and hooks in react. Well, it is not different, react is a programming language and hooks is a function which is used in react programming language.
Read More:- https://infoatone.com/what-are-hooks-in-react-js/
#react #hooks in react #react hooks example #react js projects for beginners #what are hooks in react js? #when to use react hooks
1592827920
How to Write Better Code using ESLint | VSCode ESLint Tutorial
In today’s episode, I am talking about all you need to know about ESLint. You will see what is ESLint and why you need to use ESLint in your JavaScript projects. All the steps to configure and customize it for VSCode are described step by step.
ESLint is a pluggable JavaScript linter that is designed to reduce our mistakes, bad practices, and errors in JavaScript. We can use this linter in order to have a better code format in our team and increase our output quality. If you are working individually or in a team, I highly recommend using ESLint for your projects to avoid mistakes and unwanted errors and have the same code style with your co-workers.
By watching this ESLint tutorial, you will learn how to configure it, disable annoying rules, or define your custom configuration. You will learn how to use it in Vanilla Js or ReactJs or VueJS projects and enable popular configs like Airbnb or standard. At the end of the video, you will see how to enable ESLint auto fix on save for VSCode to make it easier to use.
#vscode eslint tutorial #eslint