NLTK | An Animal Crossing New Leaf Save Editor & Toolkit | Hacking library

 by   Slattz C++ Version: Nightly License: MIT

kandi X-RAY | NLTK Summary

kandi X-RAY | NLTK Summary

NLTK is a C++ library typically used in Security, Hacking applications. NLTK has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

NLTK is a WIP ACNL Toolkit without the hassle of taking out your SD card. Only Homebrew or CFW is needed to use it. The main developers of this project are Slattz and Cuyler.
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              NLTK has a low active ecosystem.
              It has 14 star(s) with 4 fork(s). There are 6 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 3 open issues and 1 have been closed. On average issues are closed in 48 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of NLTK is Nightly

            kandi-Quality Quality

              NLTK has no bugs reported.

            kandi-Security Security

              NLTK has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              NLTK is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              NLTK releases are available to install and integrate.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of NLTK
            Get all kandi verified functions for this library.

            NLTK Key Features

            No Key Features are available at this moment for NLTK.

            NLTK Examples and Code Snippets

            No Code Snippets are available at this moment for NLTK.

            Community Discussions

            QUESTION

            Replace periods and commas with space in each file within the folder
            Asked 2021-Jun-11 at 10:28

            I have a folder that contains a group of files, and each file contains a text string, periods, and commas. I want to replace the periods and commas with spaces and print all the files afterwards.

            I used Replace, but this error appeared to me:

            ...

            ANSWER

            Answered 2021-Jun-11 at 10:28

            It seems you are trying to use the string function "replace" on a list. If your intention is to use it on all of the list's members, you can do it like so:

            Source https://stackoverflow.com/questions/67935284

            QUESTION

            Empty content when moving files of a folder to another folder with a modification or deletion of stop words on these files
            Asked 2021-Jun-10 at 15:19

            I have this project.

            I have a folder called "Corpus" and it contains a set of files. It is required that I delete the "stop words" from these files and then save the new files that do not contain the stop words in a new folder called "Save-files".

            And when I opened the “Save-Files” folder, I saw inside it the files that I had saved, but they were without content, that is, when I open the number one file, it is empty without content.

            And as it is clear in the first picture, here is the “Save-Files” folder, and inside it there is a group of files that i saved.

            And when I open any of the files, it is empty.

            How can I solve the problem?

            ...

            ANSWER

            Answered 2021-Jun-10 at 14:10

            you need to update the line to read the file to

            Source https://stackoverflow.com/questions/67922770

            QUESTION

            How to read a text and label each word of it in Python
            Asked 2021-Jun-09 at 02:30
            data = ("Thousands of demonstrators have marched through London to protest the war in Iraq and demand the withdrawal of British troops from that country. Many people have been killed that day.",
                    {"entities": [(48, 54, 'Category 1'), (77, 81, 'Category 1'), (111, 118, 'Category 2'), (150, 173, 'Category 3')]})
            
            ...

            ANSWER

            Answered 2021-Jun-09 at 02:30

            Not sure if the final format is json, yet below is an example to process the data into the print format, i.e.

            Source https://stackoverflow.com/questions/67896141

            QUESTION

            Why are all my classification accuracy scores the same?
            Asked 2021-Jun-08 at 00:47

            I'm running several machine learning models to find the one which the highest accuracy score, however, all the accuracy scores are the exact same. I performed NLP on social media text and I'm training my models to tag sentiment based on sentiment determined from NLTK.

            I'm using the same training and test sets, but I've done this method before in the past and received different scores on different models. Why are all of mine the same? Am I overfitting perhaps?

            Here is my code where I'm splitting and training:

            ...

            ANSWER

            Answered 2021-Jun-08 at 00:47

            I'm not sure what is the cause of the problem, but since the output of you SVM model and DecisionTreeClassfier always output 1, I suggest you try a more complex model like RandomForestClassifier and see what it comes out.

            I've similar experience before, no matter how I tuned the training hyperparameters, the model always give the same performance metric -- this may cause by 2 probabilities:

            1. Our data is not suitable for the model, for example all values in the vector is zero: [0, 0, 0, 0, 0, 0, 0]
            2. Our model is too simple, which could only perform linear modeling, so that it could not learn too complex mapping function.

            Since your SVM is built with linear kernel, could you try an more complex model and see what it comes out? And could you examine that if your X_train_vectors is all zero's in the matrix?

            Source https://stackoverflow.com/questions/67878862

            QUESTION

            Run a code in the GPU or parallelize somehow
            Asked 2021-Jun-07 at 09:57

            I am running a NLP program in which I do a text preprocessing before running the main algorithms. The preprocessing is simple: I have an array of very long strings (around 20K words each string, 30K strings in total). I want to tokenize on each string with nltk.stem.porter.PorterStemmer:

            ...

            ANSWER

            Answered 2021-Jun-06 at 12:35

            Where speed is a concern, SpaCy is often preferable over NLTK. It offers both batch processing as well as GPU integration.

            With an iterable of strings, this is the basic procedure of how you'd perform batch processing (note that there are a lot of options to tweak, like disabling certain parts of the pipeline that you don't need and setting a batch size, all of which is explained in detail in the SpaCy docs).

            Source https://stackoverflow.com/questions/67858719

            QUESTION

            Training Word2Vec Model from sourced data - Issue Tokenizing data
            Asked 2021-Jun-07 at 01:50

            I have recently sourced and curated a lot of reddit data from Google Bigquery.

            The dataset looks like this:

            Before passing this data to word2vec to create a vocabulary and be trained, it is required that I properly tokenize the 'body_cleaned' column.

            I have attempted the tokenization with both manually created functions and NLTK's word_tokenize, but for now I'll keep it focused on using word_tokenize.

            Because my dataset is rather large, close to 12 million rows, it is impossible for me to open and perform functions on the dataset in one go. Pandas tries to load everything to RAM and as you can understand it crashes, even on a system with 24GB of ram.

            I am facing the following issue:

            • When I tokenize the dataset (using NTLK word_tokenize), if I perform the function on the dataset as a whole, it correctly tokenizes and word2vec accepts that input and learns/outputs words correctly in its vocabulary.
            • When I tokenize the dataset by first batching the dataframe and iterating through it, the resulting token column is not what word2vec prefers; although word2vec trains its model on the data gathered for over 4 hours, the resulting vocabulary it has learnt consists of single characters in several encodings, as well as emojis - not words.

            To troubleshoot this, I created a tiny subset of my data and tried to perform the tokenization on that data in two different ways:

            • Knowing that my computer can handle performing the action on the dataset, I simply did:
            ...

            ANSWER

            Answered 2021-May-27 at 18:28

            First & foremost, beyond a certain size of data, & especially when working with raw text or tokenized text, you probably don't want to be using Pandas dataframes for every interim result.

            They add extra overhead & complication that isn't fully 'Pythonic'. This is particularly the case for:

            • Python list objects where each word is a separate string: once you've tokenized raw strings into this format, as for example to feed such texts to Gensim's Word2Vec model, trying to put those into Pandas just leads to confusing list-representation issues (as with your columns where the same text might be shown as either ['yessir', 'shit', 'is', 'real'] – which is a true Python list literal – or [yessir, shit, is, real] – which is some other mess likely to break if any tokens have challenging characters).
            • the raw word-vectors (or later, text-vectors): these are more compact & natural/efficient to work with in raw Numpy arrays than Dataframes

            So, by all means, if Pandas helps for loading or other non-text fields, use it there. But then use more fundamntal Python or Numpy datatypes for tokenized text & vectors - perhaps using some field (like a unique ID) in your Dataframe to correlate the two.

            Especially for large text corpuses, it's more typical to get away from CSV and instead use large text files, with one text per newline-separated line, and any each line being pre-tokenized so that spaces can be fully trusted as token-separated.

            That is: even if your initial text data has more complicated punctuation-sensative tokenization, or other preprocessing that combines/changes/splits other tokens, try to do that just once (especially if it involves costly regexes), writing the results to a single simple text file which then fits the simple rules: read one text per line, split each line only by spaces.

            Lots of algorithms, like Gensim's Word2Vec or FastText, can either stream such files directly or via very low-overhead iterable-wrappers - so the text is never completely in memory, only read as needed, repeatedly, for multiple training iterations.

            For more details on this efficient way to work with large bodies of text, see this artice: https://rare-technologies.com/data-streaming-in-python-generators-iterators-iterables/

            Source https://stackoverflow.com/questions/67718791

            QUESTION

            "For loop" doesn't iterate through the files
            Asked 2021-Jun-04 at 12:54

            In my simple for loop below, I iterated over 3600 texts, tokenized them and saved them into a list:

            ...

            ANSWER

            Answered 2021-Jun-04 at 12:54

            You collect document tokens into a list of lists, list (list.append(tokenize)), and then in a for tokens in list: loop you assign the most_common() value to the most_common variable thus getting only the most common terms for the last document only.

            I sugges to .extend the list of tokens to collect all tokens into a single flat list of words, and then getting the top 10 most common tokens by passing the list to the nltk.FreqDist() method:

            Source https://stackoverflow.com/questions/67756442

            QUESTION

            download nltk corpus as cmdclass in setup.py files not working
            Asked 2021-Jun-03 at 12:13

            There are some parts of the nltk corpus that I'd like to add to the setup.py file. I followed the response here by setting up a custom cmdclass. My setup file looks like this.

            ...

            ANSWER

            Answered 2021-Jun-03 at 12:13

            Pass the class, not its instance:

            Source https://stackoverflow.com/questions/67821111

            QUESTION

            Selecting certain tuple based on elements in a tuple filled list
            Asked 2021-Jun-03 at 08:24

            I am doing some NLP with NLTK and I have a Counter() sequences, for example

            ...

            ANSWER

            Answered 2021-Jun-03 at 08:24

            As metatoaster has elaborated in his comment, you would prob. have to restructure your data to perform the operation in the exact way you want (without O(n)).

            That being said, in the current state and with reference to your example, you could do:

            Source https://stackoverflow.com/questions/67815388

            QUESTION

            pipenv - Pipfile.lock is not being generated due to the 'Could not find a version that matches keras-nightly~=2.5.0.dev' error
            Asked 2021-Jun-03 at 06:29

            As the title clearly describes the issue I've been experiencing, no Pipfile.lock is being generated as I get the following error when I execute the recommended command pipenv lock --clear:

            ...

            ANSWER

            Answered 2021-Jun-03 at 06:29

            By looking at the pypi site for keras-nightly library, I could see that there are no versions named 2.5.0.dev. Check which package is generating the error and try downgrading that package.

            Source https://stackoverflow.com/questions/67806604

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install NLTK

            You can download it from GitHub.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/Slattz/NLTK.git

          • CLI

            gh repo clone Slattz/NLTK

          • sshUrl

            git@github.com:Slattz/NLTK.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Explore Related Topics

            Consider Popular Hacking Libraries

            wifiphisher

            by wifiphisher

            routersploit

            by threat9

            XSStrike

            by s0md3v

            pwntools

            by Gallopsled

            Atmosphere

            by Atmosphere-NX

            Try Top Libraries by Slattz

            ACNL_REditor

            by SlattzC++

            ACNL_Research

            by SlattzC

            ObfusC

            by SlattzC++