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PowerPoint Timeline Inspiration Even though you had never learned about machine learning. You can use Cherry to train your text classification model in 5 minutes with over 80% accuracy. Cherry also provides extra features for users who want to improve their model. Blog Email Examples

Post Layout Types Cherry provide Product Launching PPT Design Templates Download Free and Best Creative Writing Blogs api to help you debug and improve your model. Kids Learn How To Read

- Python (above 3.6) 

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pip install cherry # Cherry use nltk for text tokenizer pip install nltk # After install nltk, You need to download punkt for tokenizer >>> import nltk >>> nltk.download('punkt') 

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  LinkedIn Profile Icon These datasets contain 11,315 news. they were organized into 20 different newsgroups, each corresponding to one of the below topic: I Have Read Label

  • alt.atheism, comp.graphics, comp.os.ms-windows.misc, comp.sys.ibm.pc.hardware
  • comp.sys.mac.hardware, comp.windows.x, misc.forsale, rec.autos
  • rec.motorcycles, rec.sport.baseball, rec.sport.hockey, sci.crypt
  • sci.electronics, sci.med, sci.space, soc.religion.christian
  • talk.politics.guns, talk.politics.mideast, talk.politics.misc, talk.religion.misc

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  Good Instagram Stories These datasets contain 108,463 reviews from the Goodreads book review website, Every book review also has rating from 0 point to 5 points. Product Launch Event Photos

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  Good Title Examples These datasets contain 5,578 SMS messages manually extracted from the Grumbletext Web site and randomly chosen ham messages of the NUS SMS Corpus (NSC). How To Add Story Snapchat Web

How To Post Story On Instagram For Web In the Job Email Sample datasets, each review has a corresponding rating from 1 to 5. For example, if you want to predict the rating based on this book review: New Product Launch Email Template

Instagram Story Size Chart This is an extremely entertaining and often insightful collection by Nobel physicist Richard Feynman drawn from slices of his life experiences. Some might believe that the telling of a physicist’s life would be droll fare for anyone other than a fellow scientist, but in this instance, nothing could be further from the truth. Post Button On Instagram

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Python3 >>> cherry.train('review') 

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1. Download review datasets from remote server (User in China may need use VPN)
2. Train datasets using default settings (Kids Story Books Read Online Free and Secured Credit Cards)

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>>> res = cherry.classify('review', text='This is an extremely entertaining and often insightful collection by Nobel physicist Richard Feynman drawn from slices of his life experiences. Some might believe that the telling of a physicist’s life would be droll fare for anyone other than a fellow scientist, but in this instance, nothing could be further from the truth.') 

Writing A Blog Post About A Book You Read The return res is a Classify object has two built-in method. get_probability() will return an array contains the probability of each category. The order of the return array depend on category name, in this case would be 0, 1, 2, 3, 4. We can see that there is 99.63% (9.96313288e-01) this review is rated 4 point. Product Story Ideas

# The probability of this review had been rating as 4 points is 99.6% >>> res.get_probability() array([[6.99908424e-11, 2.48677319e-11, 6.17978214e-06, 3.39472694e-03, 9.96313288e-01, 2.85805135e-04]]) 

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>>> res.get_word_list() [[(2, 'physicist'), (2, 'life'), (1, 'truth'), (1, 'telling'), (1, 'slices'), (1, 'scientist'), (1, 'richard'), (1, 'nobel'), (1, 'instance'), (1, 'insightful'), (1, 'feynman'), (1, 'fellow'), (1, 'fare'), (1, 'extremely'), (1, 'experiences'), (1, 'entertaining'), (1, 'droll'), (1, 'drawn'), (1, 'collection'), (1, 'believe')]] 

Blank Newspaper Template For Kids Some of the words in the review didin't show up here. There are two reasons for this 1) The training data didn't contain that word. For instance, The word Backend and Engineer never show up in training data. So the model don't know how to classify these words. 2) the word is a A Home Library Can Tell Your Life Story. PPT Template Free Product Introduction

Green Economy GIF In the cherry folder, you can find a new folder named datasets. The five folders inside correspond to 1 to 5 points respectively. cherry uses the word frequency inside different folders to determine which word belongs to which score. When performing a classification task, cherry will calculate the probability of all words in the review to determine which category it belongs to. Apple Company Template

Our Product Slide Create a folder your_model_name under datasets in project path like this: New Product Icon Vector

├── project path │   ├── datasets | │   ├── your_model_name | │   │   ├── category1 | | │   ├── file_1 | | │   ├── file_2 | | │   ├── … | │   │   ├── category2 | | │   ├── file_10 | | │   ├── file_11 | | │   ├── … 

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# By default, encoding will be utf-8, # You only need to run `train` at the first time >>> cherry.train('your_model_name', encoding='your_encoding') # Classify text, `text` can be a list of text too. >>> res = cherry.classify('your_model_name', text='text to be classified') 

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mkdir tutorial cd tutorial # Create a virtual environment to isolate our package dependencies locally python3 -m venv env source env/bin/activate # On Windows use `env\Scripts\activate` # Install cherry and nltk pip install cherry pip install nltk >>> import nltk >>> nltk.download('punkt') # Create a new folder for email dataset mkdir -p datasets/email_tutorial 

1. Best Credit Card For Home Improvement Download the datasets from Product Launch Road Map Template 2 Month then unzip it and put it inside tutorial/datasets/email_tutorial folder, now you got a file named SMSSpamCollection.txt which contains lots of emails. Ayurvedic Products Advertising

2. What Dos A Blog Look Like Create a folder name ham and spam inside email_tutorial dir. Very Short Non Fiction Story

3. Ayuvedic Product Post Create a script email.py in the same folder using code below to extract the email content and group them by category. every file would only contain text. My Credit Card Account

    import os import json ham_counter = 0 spam_counter = 0 with open('SMSSpamCollection.txt', 'r') as f: for line in f.readlines(): if line.startswith('ham'): ham_counter += 1 with open(os.path.join('ham', str(ham_counter)), 'w') as nf: _, text = line.split('ham', 1) nf.write(text.strip()) else: spam_counter += 1 with open(os.path.join('spam', str(spam_counter)), 'w') as nf: _, text = line.split('spam', 1) nf.write(text.strip()) 

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    tutorial ├── dataset │   ├── email_tutorial | | ├── email.py | | ├── SMSSpamCollection.txt │   │   ├── ham │   │   ├── spam 

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7. English Replying Emails Write Back to the path of tutorial, Like cd path_to/tutorial Instagram Story Art

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    >>> import cherry >>> cherry.train('email_tutorial', encoding='latin1') 

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    >>> res = cherry.classify('email_tutorial', 'Thank you for your interest in cherry! We wanted to let you' 'know we received your application for Backend Engineer, and we are delighted that you' 'would consider joining our team.') # 99.9% is a ham email >>> res.get_probability() array([[9.99985571e-01, 1.44288379e-05]]) >>> res.get_word_list() [[(1, 'wanted'), (1, 'thank'), (1, 'team'), (1, 'received'), (1, 'let'), (1, 'joining'), (1, 'consider'), (1, 'application')]] 

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     >>> res = cherry.performance('email_tutorial', encoding='latin1', output='files') >>> res.get_score() 

    Alpha Vs Beta Prototype The report will be save in report files, you can find the precision, recall, and f1-score. Post Example

     precision recall f1-score support 0 0.99 1.00 0.99 485 1 0.97 0.95 0.96 73 accuracy 0.99 558 macro avg 0.98 0.97 0.98 558 

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     >>> res = cherry.performance('email_tutorial', encoding='latin1') >>> res.get_score() Text: Dhoni have luck to win some big title.so we will win:) has been classified as: 1 should be: 0 Text: Back 2 work 2morro half term over! Can U C me 2nite 4 some sexy passion B4 I have 2 go back? Chat NOW 09099726481 Luv DENA Calls £1/minMobsmoreLKPOBOX177HP51FL has been classified as: 0 should be: 1 Text: Latest News! Police station toilet stolen, cops have nothing to go on! has been classified as: 0 should be: 1 ... 

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     >>> res.display('email_tutorial', encoding='latin1') 

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    >>> parameters = {'clf__alpha': [0.1, 0.5, 1],'clf__fit_prior': [True, False]} >>> cherry.search('email_tutorial', parameters) 

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  Elementor Pro Blog Post Template Cherry supports shortcut to set up feature extraction function when vectorizer is None. Count corresponds to CountVectorizer(tokenizer=tokenizer, stop_words=get_stop_words(model)), Tfidf corresponds to TfidfVectorizer and Hashing corresponds to HashingVectorizer. ECommerce Web Design

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  Product Launch Event Photos Cherry supports shortcut to set up classify function when clf is None, MNB corresponds to MultinomialNB(alpha=0.1), SGD corresponds to SGDClassifier, RandomForest corresponds to RandomForestClassifier, AdaBoost corresponds to AdaBoostClassifier. Low Credit Credit Cards

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  How To Add Story Snapchat Web training text data, if x_data and y_data is None, cherry will try to find the text files data in model Example Article To Read

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  Create A Image For Paragraphs Blog Post The name of the model, you can use build-in models email, review and newsgroups, or pass the folder name of your dataset. Individual Group Cartoon

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>>> python runtests.py