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Whether you’re looking to make multitasking easier by having your iPhone, iPod touch, or iPad right next to your computer, or you’re using some kind of screen-extension software like Duet Display, the Mountie by Ten 1 Design is a great way to use your iOS device side-by-side with your Mac or PC.

The Mountie, which can be had in either Blue or Green flavors on Amazon for just under $25, is an affordable way to keep your iOS device right where you want it – in front of your face – without needing to shell out big bucks on fancy desk stands.

In this review, we’ll show you how the Mountie works and give you our general opinion on why you should or shouldn’t consider it for your Apple accessory wish list.

How the Mountie works

Mountie uses a gentle clamping system to attach your iOS device to the side of your MacBook, MacBook Air, MacBook Pro, or other notebook PC. Because of the design, this accessory only works with notebook computers – not desktop computers like the iMac.

The clamping system uses a gentle rubber boot system so that the displays of your MacBook computer and iOS devices are never worn into or scuffed up. The materials used to build the Mountie are intended to keep the iOS device as sturdy as possible in a light-weight design without causing any damage to either device.

It’s also worth noting that the Mountie is built on a one-size-fits-all platform. It comes with additional rubber boots of all different sizes, so you will always have a rubber boot that fits your computer and iOS device combo, whether you have a case on it or not. The boots are labeled by letter, and the Mountie comes with instructions that outline which letter is best for certain iOS devices.

As you can see, each boot has two rubber nipples; these need to be seated into the Mountie so they remain in place. Once you’re satisfied, you can go ahead and mount your iOS device up to your notebook at the desired height and in the desired orientation and then press down on the colored clamps to hold it into place:

From the front, you will see the isolator, and the leverage “T” peeking out from between the device’s displays:

And now, we can run our iPad as a secondary display in a very comfortable viewing position all thanks to a piece of software that we enjoy so much called Duet Display:

Our thoughts on the Mountie

I was immediately impressed with the Mountie; not only when I first learned about it, but also when I received my package in the mail and opened it up for the first time. I could tell it was exactly what I was looking for.

Because I do so much at a single time while I write for iDownloadBlog, the Mountie lets me use my iPad as a secondary display with Duet Display, which is very useful for having another window open for chatting with the team and chatting with other friends and family. It also makes it easier to watch a video and multitask simultaneously, or to read and write at the same time.

The design of Mountie is pretty simple – so simple, in fact, that the creators need not charge a whole lot for it. At just $25, there’s very little negative to say about the Mountie. It’s well-priced and it does a fantastic job keeping your iOS device right next to your notebook’s display.

The one-size-fits-all thing that the Mountie has going on is also alluring. This accessory will work with any brand smartphone, as well as almost any tablet and almost any notebook. In fact, I can’t even think of anything that it won’t work with, right off the bat, except maybe an early fat and bulky Panasonic ToughBook.

Would I recommend the Mountie? I absolutely would! I haven’t been able to stop using it since I took it out of the box. Here is a quick outline of the Mountie’s pros and cons if you were having a TL;DR moment this entire time:

Pros:

Universal fit for almost any smartphone or tablet

Universal fit for almost any MacBook model or PC notebook

Can support the weight of heavy tablets with ease

Simple to put on and take off

Allows you to place your mobile device at any height or orientation

Doesn’t cause damage to the computer screen or mobile device

Low profile design isn’t bulky and is hardly noticeable

Super portable

Super affordable

Cons:

Limited color options; just blue or green

Plastic clamp handles used instead of fancy colored anodized aluminum

Easy to lose extraneous rubber boots because of lack of carrying case

Wrapping up

The Mountie is a great solution for anyone looking to run a side-by-side setup between their Mac or PC notebook and their mobile devices. For just $25 on Amazon for either the Blue or Green colors, anyone can afford one of these universally capable mounts.

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Navigation Device Lets You Feel Your Way Through A City

Navigating through a crowded city like New York or London can be a challenge, especially for those who are visually impaired. Adam Spiers, a postdoc in robotics at Yale University, set out to create a tool that helps visually impaired individuals easily follow navigation instructions. His tool, a 3D printed device called Animotus, sits in the palm of your hand and changes shape to guide you to your destination by touch.

The Animotus communicates in two ways. The top piece twists right and left to indicate the direction the traveler should turn, and slides forward to show how far in that direction the user should move. Once it’s ready for the next directional step, the top piece slides back into its original place.

“The idea is that it relies only on the sense of touch,” Spiers said. Spiers opted out of using vibrations or sounds as they can quickly become distracting for visually impaired individuals, especially in a big city where pedestrians are constantly bombarded by noise.

How the Animotus device works

The project was funded by NESTA, an independent charity organization in the United Kingdom. To test out his product, Spiers partnered with the British theater company Extant, and adapted a version of Flatland–a satirical novel about a fictional two-dimensional world–around the Animotus device. Audience members, both sighted and visually impaired participants, became the actors and were guided around a pitch-dark stage (which happened to be the inside of an old church). They followed the Animotus device’s instructions to reach various destinations. In addition, participants listened to other actors read the narrative and heard sound effects that told the rest of the story. By the end of the play, participants became so comfortable with their Animotus devices that they didn’t want to give them up, said Spiers. “It was quite endearing for me to see them become so attached to to the device.”

An audience member using the Animotus device in the play

OLYMPUS DIGITAL CAMERA

The current version of Animotus works with wireless location sensors mounted on the walls of the space where it operates. The ideal next step, according to Spiers, will be to enable it to connect to smart phones and other GPS devices so that it can be used as an alternative to staring at a screen to find a new location. He also hopes to see how the device works when used in the middle of a busy street as well as with different terrains, such as a hiker using it to find her way. While the Animotus is still far from mass production, Spiers envisions the tool as an easier way for both visually impaired and sighted individuals to find their way.

A Simple Guide To Hypothesis Testing For Dummies!

Result of the experiment

While performing this experiment, an observation is made which is X = 5.

In hypothesis testing, the probability of observing the value of the test statistic by experimentation i.e. X = 5 if the null hypothesis is true is 3%.

Here, the test statistic i.e. X = 5 is the observation which is the ground truth. Hence, this fact cannot be revoked.

So, given that this observation has already been made, the probability of this observation if the assumption is also true is only 3% which is very low.

This probability value is called the p-value; the result of the statistical hypothesis testing.

Typically, the p-value is said to be small if it is less than or equal to 5%. This is just a rule of thumb.

Here the experiment was designed saying the coin was flipped 5 times. What if the coin was flipped 3 times or 10 times? The probability values will change. So, this experiment is dependent on the number of flips. This is often called a sample size.

This probability value is much greater than 5%. Hence, the assumption is failing to reject i.e. the coin is not biased.

The results of the hypothesis testing must be wisely interpreted to make claims about the data. The results can be interpreted in different ways. They are the p-values and critical values.

How to interpret the p-value?

A statistical hypothesis test may return a p-value. A p-value is defined as the probability of making the observation made, given the null hypothesis is true. It is calculated using the sample distribution of the test statistic, under the assumption i.e. null hypothesis.

The p-value is used to quantify the result of the test given the null hypothesis. This is done by comparing the p-value to the threshold value also known as significance level referred to by the Greek letter alpha.

Typically, the alpha value is 0.05 or 5%.

The p-value is compared to the pre-defined alpha value. The result of the experiment is significant when the p-value is less than, equal to the alpha value signifying that a change was detected, rejecting the null hypothesis.

Source: simplypsychology.org

Let us assume, we performed a statistical hypothesis test of whether the data sample is normally distributed and calculated a p-value of 0.9, we can say that the hypothesis test found that the sample is normally distributed, failing to reject the null hypothesis at a 5% significance level.

There is one mistake that a lot of people often make. Some people think that it is the probability that the null hypothesis (H0) is true.

This is incorrect! We cannot state anything about the null hypothesis (H0).

Instead, the p-value is the probability of the observation you have made given that the null hypothesis (H0) is true.

How to interpret critical values?

Not all statistical tests return a p-value. Instead, they might return a critical value and associated significance level along with the test statistic.

The interpretation of the results is similar to the p-value results. Instead of comparing the p-value to a pre-defined significance level, the test statistic is compared to the critical value at a chosen significance level.

test statistic < critical value: fail to reject H0

 

Source: geo.fu-berlin.de

Representation of the results using the critical values are in the same way as they are interpreted using the p-value.

While performing a statistical hypothesis test of whether the data sample is normally distributed is calculated and the test statistic was compared to the critical value at the 5% significance level, we can say that the hypothesis test found that the sample is normally distributed, failing to reject the null hypothesis at a 5% significance level.

Errors in Hypothesis Testing

Hypothesis testing provides confidence in favor of a certain hypothesis. In other words, hypothesis testing refers to the use of statistical analysis to determine if observed differences between two or more data samples are due to random chance or to be true differences in the samples.

Since we are computing the probability of an experiment it can be deduced that interpretation of the hypothetical test is purely probabilistic. This means that the outcome of the experiment can be misunderstood.

If the p-value is small; rejecting the null hypothesis indicates either of these 2 scenarios:

The null hypothesis is false – we are right

The null hypothesis is true and some rare and unlikely event occurred – we made a mistake.

This type of error is called False Positive – Type I Error.

On contrary, if the p-value is large; failing to reject the null hypothesis indicates either of these 2 scenarios:

The null hypothesis is true – we are right

The null hypothesis is false and some rare and unlikely event occurred – we made a mistake.

This type of error is called False Negative – Type II Error.

There is always a possibility of making these kinds of errors while interpreting the results of hypothesis testing. Hence, we must be cautious of encountering such errors and verify the findings before drawing conclusions.

SUMMARY

In machine learning, mostly hypothesis testing is used in a test that assumes that the data has a normal distribution and in a test that assumes that 2 or more sample data are drawn from the same population.

Remember these 2 most important things while performing hypothesis testing:

1. Design the Test statistic

Design the experiment ingeniously.

Here in the above experiment, a coin is flipped 5 and 3 times. The sample sizes have to be carefully chosen while designing the experiment.

2. Design the null hypothesis (H0) carefully

It should be designed in such a manner that it makes the probability computation easy and feasible.

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.

9 Useful Tasker Profiles To Automate Your Android Device

While Tasker for Android is powerful, it can sometimes be difficult to use. If setting up Tasker profiles has been a pain, then this is your chance to enjoy some automation on your Android device.

1. Launch Music Player When You Plug in Headphones

3. Give this task any name you want when prompted.

4. Now tap the “+” button. Choose Apps, then “Launch App”. Select your preferred music app. You are done!

2. Set Phone to Mute Calls When Turned Upside-Down

When you are in a lecture or a meeting, this Tasker profile can spare you some real embarrassment. It uses your phone’s orientation feature to detect a change in the position of your phone.

1. Create a new Tasker profile, but this time choose “State,” and then select “Sensor.”

2. Open the menu options and select “Face Down.” Go back.

That’s all!

3. Open Some Apps in a Sequence

If you need to consume information fast, here’s your profile. It’s helpful if you love the feeling of automating your day.

1. Create a new Tasker profile and select “Application.” A list will show on the next screen. Choose the application you want to open first. Go back.

4. Send Text Message When Battery Juice Is About to Run Out

Sometimes you may be on a trip or a volunteer mission and can’t afford to plug your phone in to charge. Having a flat battery could mean that you are cut off from family and friends for a while.

You don’t want them worrying about your safety. This Tasker profile sends a text to them as soon as your phone hits a set low battery percentage.

1. Create a new profile and choose “State.”

2. From the options, choose “Power,” and then select “Battery Level.”

3. Set the battery level for which you want Tasker to send the text message. Go back.

5. Use Tasker Profiles to Secure Your Apps

Privacy is a sensitive topic, and you need as much of it as you can get. Thankfully, the Tasker app provides the means to lock away some of your essential applications from prying eyes.

1. Create a profile and choose “Application.” From the list, select the applications that you need to secure. Go back. Choose a name for the new task and hit the “+” button. Select “Display,” then choose “Lock.”

2. Now select the lock key for the section.

6. Switch Mobile Data Off When Battery Is Low

2. Choose a name for the new task profile and tap on the “+” button. On the next screen choose “Net,” then “Mobile Data,” and then select “Turn On.”

7. Set Up an Alarm to Catch Privacy Invaders

Remember those apps you secured for privacy reasons? This Tasker profile helps you protect them further. It sets up an alarm that alerts you the moment someone attempts to open them.

2. In the next set of options, tweak the settings such as frequency, duration, and amplitude per your liking.

8. Switch WiFi on When You Open Google Maps

Google Maps work best when using WiFi, so it’s best that you switch to this mode while using maps. Also, WiFi helps you save on mobile data cost. These instructions show how to configure the Tasker mode to help you achieve this.

2. In the next screen, change the status to “On.” Now you are all set.

9. Turn Off Auto-Rotate During Bedtime

Nothing is as annoying as having your phone flip orientation when you are using it on the bed. This Tasker profile automatically turns off the auto-rotate feature during bedtime hours.

Wrapping Up

Tasker is a powerful tool that can turn your Android phone into a powerful personal assistant. If you haven’t used the Tasker app before now, this is your chance. Tasker profiles should not be a pain to set up anymore.

Nicholas Godwin

Nicholas Godwin is a technology researcher who helps businesses tell profitable brand stories that their audiences love. He’s worked on projects for Fortune 500 companies, global tech corporations and top consulting firms, from Bloomberg Beta, Accenture, PwC, and Deloitte to HP, Shell, and AT&T. You may follow his work on Twitter or simply say hello. His website is Tech Write Researcher.

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Introduction To Flair For Nlp: A Simple Yet Powerful State

Introduction

Last couple of years have been incredible for Natural Language Processing (NLP) as a domain! We have seen multiple breakthroughs – ULMFiT, ELMo, Facebook’s PyText, Google’s BERT, among many others. These have rapidly accelerated the state-of-the-art research in NLP (and language modeling, in particular).

We can now predict the next sentence, given a sequence of preceding words.

What’s even more important is that machines are now beginning to understand the key element that had eluded them for long.

Context! Understanding context has broken down barriers that had prevented NLP techniques making headway before. And today, we are going to talk about one such library – Flair.

Until now, the words were either represented as a sparse matrix or as word embeddings such as GLoVe, Bert and ELMo, and the results have been pretty impressive. But, there’s always room for improvement and Flair is willing to stand up to it.

In this article, we will first understand what Flair is and the concept behind it. Then we’ll dive into implementing NLP tasks using Flair. Get ready to be impressed by its accuracy!

Please note that this article assumes familiarity with NLP concepts. You can go through the below articles if you need a quick refresher:

Table of contents

What is ‘Flair’ Library?

What gives Flair the Edge

Introduction to Contextual String Embeddings for Sequence Labeling

Performing NLP Tasks in Python using Flair

What’s Next for Flair?

What is ‘Flair’ Library?

Flair is a simple natural language processing (NLP) library developed and open-sourced by Zalando Research. Flair’s framework builds directly on PyTorch, one of the best deep learning frameworks out there. The Zalando Research team has also released several pre-trained models for the following NLP tasks:

Name-Entity Recognition (NER): It can recognise whether a word represents a person, location or names in the text.

Parts-of-Speech Tagging (PoS): Tags all the words in the given text as to which “part of speech” they belong to.

Text Classification: Classifying text based on the criteria (labels)

Training Custom Models: Making our own custom models.

All of this looks promising. But what truly caught my attention was when I saw Flair outperforming several state-of-the-art results in NLP. Check out this table:

Note: F1 score is an evaluation metric primarily used for classification tasks. It’s often used in machine learning projects over the accuracy metric when evaluating models. The F1 score takes into consideration the distribution of the classes present.

What Gives Flair the Edge?

There are plenty of awesome features packaged into the Flair library. Here’s my pick of the most prominent ones:

It comprises of popular and state-of-the-art word embeddings, such as GloVe, BERT, ELMo, Character Embeddings, etc. There are very easy to use thanks to the Flair API

‘Flair Embedding’ is the signature embedding provided within the Flair library. It is powered by contextual string embeddings. We’ll understand this concept in detail in the next section

Flair supports a number of languages – and is always looking to add new ones

Introduction to Contextual String Embeddings for Sequence Labeling

Context is so vital when working on NLP tasks. Learning to predict the next character based on previous characters forms the basis of sequence modeling.

Contextual String Embeddings leverage the internal states of a trained character language model to produce a novel type of word embedding. In simple terms, it uses certain internal principles of a trained character model, such that words can have different meaning in different sentences.

Note: A language and character model is a probability distribution of Words / Characters such that every new word or character depends on the words or characters that came before it. Have a look here to know more about it.

There are two primary factors powering contextual string embeddings:

The words are trained as characters (without any notion of words). Aka, it works similar to character embeddings

The embeddings are contextualised by their surrounding text. This implies that the same word can have different embeddings depending on the context. Quite similar to natural human language, isn’t it? The same word may have different meanings in different situations

Let’s look at an example to understand this:

Case 1: Reading a

book

Case 2: Please

book

a train ticket

Explanation:

In case 1, book is an 

OBJECT

In case 2, book is a

VERB

Language is such a wonderful yet complex thing. You can read more about Contextual String Embeddings in this Research Paper.

Performing NLP Tasks in Python using Flair

It’s time to put Flair to the test! We’ve seen what this awesome library is all about. Now let’s see firsthand how it works on our machines.

We’ll use Flair to perform all the below NLP tasks in Python:

Text Classification using the Flair embeddings

Part of Speech Tagging (PoS) and comparison with the NLTK library

Setting up the Environment

We will be using Google Colaboratory for running our code. One of the best things about Colab is that it provides GPU support for free! It is pretty handy for training deep learning models.

Why use Colab?

Completely free

Comes with pretty decent hardware configuration

It’s on your web browser so even old machines with outdated hardware can run it

Connected to your Google Drive

Very well integrated with Github

All you need is a stable internet connection.

About the Dataset

We’ll be working on the Twitter Sentiment Analysis practice problem. Go ahead and download the dataset from there (you’ll need to register/log in first).

The problem statement posed by this challenge is:

The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

1. Text Classification Using Flair Embeddings

Overview of steps:

Step 1: Import the data into the local Environment of Colab:

Step 2: Installing Flair

Step 3: Preparing text to work with Flair

Step 4: Word Embeddings with Flair

Step 5: Vectorizing the text

Step 6: Partitioning the data for Train and Test Sets

Step 7: Time for predictions!

     Step 1: Import the data into the local Environment of Colab:

# Install the PyDrive wrapper & import libraries.

# This only needs to be done once per notebook.

!pip install -U -q PyDrive

from chúng tôi import GoogleAuth

from pydrive.drive import GoogleDrive

from google.colab import auth

from oauth2client.client import GoogleCredentials

# Authenticate and create the PyDrive client.

# This only needs to be done once per notebook.

auth.authenticate_user()

gauth = GoogleAuth()

gauth.credentials = GoogleCredentials.get_application_default()

drive = GoogleDrive(gauth)

# Download a file based on its file ID.

# A file ID looks like: laggVyWshwcyP6kEI-y_W3P8D26sz

file_id = '1GhyH4k9C4uPRnMAMKhJYOqa-V9Tqt4q8' ### File ID ###

data = drive.CreateFile({'id': file_id})

#print('Downloaded content "{}"'.format(downloaded.GetContentString()))

You can find the file ID in the shareable link of the dataset file in the drive.

Importing the dataset into the Colab notebook:

import io

Import pandas as pd

data = pd.read_csv(io.StringIO(data.GetContentString()))

data.head()

All the emoticons and symbols have been removed from the data and the characters have been converted to lowercase. Additionally, our dataset has already been divided into train and test sets. You can download this clean dataset from here.

Step 2: Installing Flair

# download flair library #

import torch

!pip install flair

import flair

A Brief look at Flair Data Types

There are two types of objects central to this library – Sentence and Token objects. A Sentence holds a textual sentence and is essentially a list of Tokens:

from chúng tôi import Sentence

# create a sentence #

sentence = Sentence('Blogs of Analytics Vidhya are Awesome.')

# print the sentence to see what’s in it. #

print(Sentence)

Step 3: Preparing text to work with Flair

#extracting the tweet part#

text = data['tweet']

## txt is a list of tweets ##

txt = text.tolist() print(txt[:10])

Step 4: Word Embeddings with Flair

Feel free to first go through this article if you’re new to word embeddings: An Intuitive Understanding of Word Embeddings.

## Importing the Embeddings ##

from flair.embeddings import WordEmbeddings

from flair.embeddings import CharacterEmbeddings

from flair.embeddings import StackedEmbeddings

from flair.embeddings import FlairEmbeddings

from flair.embeddings import BertEmbeddings

from flair.embeddings import ELMoEmbeddings

from flair.embeddings import FlairEmbeddings

#glove_embedding = WordEmbeddings('glove')

#character_embeddings = CharacterEmbeddings()

flair_forward  = FlairEmbeddings('news-forward-fast')

flair_backward = FlairEmbeddings('news-backward-fast')

#bert_embedding = BertEmbedding()

#elmo_embedding = ElmoEmbedding()

stacked_embeddings = StackedEmbeddings(

embeddings = [

flair_forward-fast,

flair_backward-fast

])

Now you might be asking – What in the world are “Stacked Embeddings”? Here, we can combine multiple embeddings to build a powerful word representation model without much complexity. Quite like ensembling, isn’t it?

We are using the stacked embedding of Flair only for reducing the computational time in this article. Feel free to play around with this and other embeddings by using any combination you like.

Testing the stacked embeddings:

# create a sentence #

sentence = Sentence(‘ Analytics Vidhya blogs are Awesome .')

# embed words in sentence #

stacked.embeddings(sentence)

for token in sentence:

 print(token.embedding)

# data type and size of embedding #

print(type(token.embedding))

# storing size (length) #

z = token.embedding.size()[0]

Step 5: Vectorizing the text

We’ll be showcasing this using two approaches.

Mean of Word Embeddings within a Tweet

We will be calculating the following in this approach:

For each sentence:

Generate word embedding for each word

Calculate the mean of the embeddings of each word to obtain the embedding of the sentence

from tqdm import tqdm ## tracks progress of loop ##

# creating a tensor for storing sentence embeddings #

s = torch.zeros(0,z)

# iterating Sentence (tqdm tracks progress) #

for tweet in tqdm(txt):   

 # empty tensor for words #

 w = torch.zeros(0,z)   

 sentence = Sentence(tweet)

 stacked_embeddings.embed(sentence)

 # for every word #

 for token in sentence:

   # storing word Embeddings of each word in a sentence #

   w = torch.cat((w,token.embedding.view(-1,z)),0)

 # storing sentence Embeddings (mean of embeddings of all words)   #

 s = torch.cat((s, w.mean(dim = 0).view(-1, z)),0)

 Document Embedding: Vectorizing the entire Tweet

from flair.embeddings import DocumentPoolEmbeddings

### initialize the document embeddings, mode = mean ###

document_embeddings = DocumentPoolEmbeddings([

                                             flair_embedding_backward,

                                             flair_embedding_forward ])

# Storing Size of embedding #

z = sentence.embedding.size()[1]

### Vectorising text ###

# creating a tensor for storing sentence embeddings

s = torch.zeros(0,z)

# iterating Sentences #

for tweet in tqdm(txt):   

 sentence = Sentence(tweet)

 document_embeddings.embed(sentence)

 # Adding Document embeddings to list #

 s = torch.cat((s, sentence.embedding.view(-1,z)),0)

You can choose either approach for your model. Now that our text is vectorised, we can feed it to our machine learning model!

Step 6: Partitioning the data for Train and Test Sets

## tensor to numpy array ##

X = s.numpy()   

## Test set ##

test = X[31962:,:]

train = X[:31962,:]

# extracting labels of the training set #

target = data['label'][data['label'].isnull()==False].values

Step 6: Building the Model and Defining Custom Evaluator (for F1 Score)

Defining custom F1 evaluator for XGBoost

def custom_eval(preds, dtrain):

   labels = dtrain.get_label().astype(np.int)

   return [('f1_score', f1_score(labels, preds))]

Building the XGBoost model

import xgboost as xgb

from sklearn.model_selection import train_test_split

from sklearn.metrics import f1_score

### Splitting training set ###

x_train, x_valid, y_train, y_valid = train_test_split(train, target,  

                                                     random_state=42,

                                                         test_size=0.3)

### XGBoost compatible data ###

dtrain = xgb.DMatrix(x_train,y_train)         

dvalid = xgb.DMatrix(x_valid, label = y_valid)

### defining parameters ###

params = {

    'colsample': 0.9,

'colsample_bytree': 0.5,

'eta': 0.1,

'max_depth': 8,

'min_child_weight': 6,

'objective': 'binary:logistic',

'subsample': 0.9

}

### Training the model ###

xgb_model = xgb.train(

    params,

    dtrain,

    feval= custom_eval,

    num_boost_round= 1000,

    maximize=True,

    evals=[(dvalid, "Validation")],

    early_stopping_rounds=30

)

Our model has been trained and is ready for evaluation! Note: The parameters were taken from this Notebook.

Step 7: Time for predictions!

### Reformatting test set for XGB ###

dtest = xgb.DMatrix(test)

### Predicting ###

predict = xgb_model.predict(dtest) # predicting

I uploaded the predictions to the practice problem page with 0.2 as probability threshold:

Word Embedding

F1- Score

Glove

0.53

flair-forward -fast

0.45

flair-backward-fast

0.48

Stacked (flair-forward-fast + flair-backward-fast)

0.54

Note: According to Flair’s official documentation, stacking of the flair embedding with other embeddings often yields even better results, But, there is a catch..

It might take a VERY LONG time to compute on a CPU. I highly recommend leveraging a GPU for faster results. You can use the free one within Colab!

2. Part of Speech (POS) Tagging with Flair

We will be using a subset of the Conll-2003 dataset, is a pre-tagged dataset in English. Download the dataset from here.

Overview of steps:

Step 1: Importing the dataset

Step 2 : Extracting Sentences and PoS Tags from the dataset

Step 3: Tagging the text using NLTK and Flair

Step 4: Evaluating the PoS tags from NLTK and Flair against the tagged dataset

Step 1: Importing the dataset

### file was uploaded manually to local environment of Colab ###

data = open('pos-tagged_corpus.txt','r')

txt = data.read()

#print(txt)

The data file contains one word per line, with empty lines representing sentence boundaries.

Step 2 : Extracting Sentences and PoS Tags from the dataset

### converting text in form of list of (words with their tags) ###

txt = txt.split('n')

### removing DOCSTART (document header)

txt = [x for x in txt if x != '-DOCSTART- -X- -X- O']

### check ### for i in range(10):

 print(txt[i])

 print(‘-’*10)

### Extracting Sentences ###

# Initialize empty list for storing words

words = []

# initialize empty list for storing sentences #

corpus = []

for i in tqdm(txt):

 ## if blank sentence encountered ##

 if i =='':

   ## previous words form a sentence ##

   corpus.append(' '.join(words))

   ## Refresh Word list ##

   words = []

 else:

## word at index 0 ##

   words.append(i.split()[0])

  

# did it work? #

for i in range(10):

 print(corpus[i])

 print(‘-’*10)

### Extracting POS ###

# Initialize empty list for storing word pos

w_pos = []

#initialize empty list for storing sentence pos #

POS = []

for i in tqdm(txt):

 ## blank sentence = new line ##

 if i =='':

   ## previous words form a sentence POS ##

   POS.append(' '.join(w_pos))

   ## Refresh words list ##

   w_pos = []

 else:

## pos tag from index 1 ##

   w_pos.append(i.split()[1])

  

# did it work? #

for i in range(10):

 print(corpus[i])

 print(POS[i])

### Removing blanks form sentence and pos ###

corpus = [x for x in corpus if x!= '']

POS = [x for x in POS if x!= '']

### Check ###

For i in range(10):

 print(corpus[i])

 print(POS[i])

We have extracted the essentials aspects we require from the dataset. Let’s move on to step 3.

Step 3: Tagging the text using NLTK and Flair

Tagging using NLTK:

First, import the required libraries:

import nltk

nltk.download('tagsets')

nltk.download('punkt')

nltk.download('averaged_perceptron_tagger')

from nltk import word_tokenize

This will download all the necessary files to tag the text using NLTK.

### Tagging the corpus with NLTK ###

#for storing results#

nltk_pos = []

##for every sentence ##

for i in tqdm(corpus):

 # Tokenize sentence #

 text = word_tokenize(i)

 #tag Words#

 z = nltk.pos_tag(text)

 # store #

 nltk_pos.append(z)

The PoS tags are in this format:

[(‘token_1’, ‘tag_1’), ………….. , (‘token_n’, ‘tag_n’)]

Lets extract PoS from this:

### Extracting final pos by nltk in a list ###

tmp = []

nltk_result = []

## every tagged sentence ##

for i in tqdm(nltk_pos):

 tmp = []

## every word ##

 for j in i:

   ## append tag (from index 1) ##

   tmp.append(j[1])

 # join the tags of every sentence #

 nltk_result.append(' '.join(tmp))

### check ### for i in range(10):

 print(nltk_result[i])

 print(corpus[i])

The NLTK tags are ready for business.

Turning our attention to Flair now

Importing the libraries first:

!pip install flair

from chúng tôi import Sentence

from flair.models import SequenceTagger

Tagging using Flair

# initiating object #

pos = SequenceTagger.load('pos-fast')

#for storing pos tagged string#

f_pos = []

## for every sentence ##

for i in tqdm(corpus):

 sentence = Sentence(i)

 pos.predict(sentence)

## append tagged sentence ##

 f_pos.append(sentence.to_tagged_string())

###check ###

for i in range(10):

 print(f_pos[i])

 print(corpus[i])

The result is in the below format:

Note: We can use different taggers available within the Flair library. Feel free to tinker around and experiment. You can find the list here.

Extract the sentence-wise tags as we did in NLTK

Import re

### Extracting POS tags ###

## in every sentence by index ##

for i in tqdm(range(len(f_pos))):

 ## for every words ith sentence ##

 for j in corpus[i].split():

   ## replace that word from ith sentence in f_pos ##

   f_pos[i] = str(f_pos[i]).replace(j,"",1)

   f_pos[i] = str(f_pos[i]).replace(j,"")

   ## removing redundant spaces ##

   f_pos[i] = re.sub(' +', ' ', str(f_pos[i]))

   f_pos[i] = str(f_pos[i]).lstrip()

### check ###

for i in range(10):

 print(f_pos[i])

 print(corpus[i])

Aha! We have finally tagged the corpus and extracted them sentence-wise. We are free to remove all the punctuation and special symbols.

### Removing Symbols and redundant space ###

## in every sentence by index ## for i in tqdm(range(len(corpus))):

 # Removing Symbols #

 corpus[i] = re.sub('[^a-zA-Z]', ' ', str(corpus[i]))

 POS[i] = re.sub('[^a-zA-Z]', ' ', str(POS[i]))

 f_pos[i] = re.sub('[^a-zA-Z]', ' ', str(f_pos[i]))

 nltk_result[i] = re.sub('[^a-zA-Z]', ' ', str(nltk_result[i]))

 ## Removing HYPH SYM (they are for symbols) ##

 f_pos[i] = str(f_pos[i]).replace('HYPH',"")

 f_pos[i] = str(f_pos[i]).replace('SYM',"")

 POS[i] = str(POS[i]).replace('SYM',"")

 POS[i] = str(POS[i]).replace('HYPH',"")

 nltk_result[i] = str(nltk_result[i].replace('HYPH',''))

 nltk_result[i] = str(nltk_result[i].replace('SYM',''))    

                 

 ## Removing redundant space ##

 POS[i] = re.sub(' +', ' ', str(POS[i]))

 f_pos[i] = re.sub(' +', ' ', str(f_pos[i]))

 corpus[i] = re.sub(' +', ' ', str(corpus[i]))

 nltk_result[i] = re.sub(' +', ' ', str(nltk_result[i]))

 

We have tagged the corpus using NLTK and Flair, extracted and removed all the unnecessary elements. Let’s see it for ourselves:

for i in range(1000):

 print('corpus   '+corpus[i])

 print('actual   '+POS[i])

 print('nltk     '+nltk_result[i])

 print('flair    '+f_pos[i])

 print('-'*50)

OUTPUT:

flair        NNP NNP NNP NNP CD

That looks convincing!

Step 4: Evaluating the PoS tags from NLTK and Flair against the tagged dataset

Here, we are doing word-wise evaluation of the tags with the help of a custom-made evaluator.

flair        NNP NN NNP NNP VBD DT JJ JJ NN VBD JJ IN PRP

Note that in the example above, the actual POS tags contain redundancy compared to NLTK and flair tags as shown (in bold). Therefore we will not be considering the POS tagged sentences where the sentences are of unequal length.

### EVALUATION FUNCTION ###

def eval(x,y):

 # correct match #

 count = 0

 #Total comparisons made#

 comp = 0

 ## for every sentence index in dataset ##

 for i in range(len(x)):

   ## if the sentence length match ##

   if len(x[i].split()) == len(y[i].split()):

     ## compare each word ##

     for j in range(len(x[i].split())):

       if x[i][j] == y[i][j] :

         ## Match! ## count = count+1

         comp = comp + 1

       else:

         comp = comp + 1

 return (count/comp)*100

Finally we evaluate the POS tags of NLTK and Flair against the POS tags provided by the dataset.

print("nltk Score ", eval2(POS,nltk_result)) print("Flair Score ", eval2(POS,f_pos))

Our Result:

NLTK Score: 85.38654023442645

Flair Score: 90.96172124773179

Well, well, well. I can see why Flair has been getting so much attention in the NLP community.

End Notes

Flair clearly provides an edge in word embeddings and stacked word embeddings. These can be implemented without much hassle due to its high level API. The Flair embedding is something to keep an eye on in the near future.

I love that the Flair library supports multiple languages. The developers are additionally currently working on “Frame Detection” using flair. The future looks really bright for this library.

Related

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