ngrams | Project 2 : Language | Natural Language Processing library

 by   esbie Java Version: Current License: No License

kandi X-RAY | ngrams Summary

kandi X-RAY | ngrams Summary

ngrams is a Java library typically used in Artificial Intelligence, Natural Language Processing applications. ngrams has no bugs, it has no vulnerabilities and it has low support. However ngrams build file is not available. You can download it from GitHub.

Unsmoothed Unigrams create a word-indexed array (hashtable) from word to word count. e.g., { 'write' => 1, 'a' => 6, 'program' => 2 } An absence of the index e.g. counts['foobar'] => null indicates that that word did not occur in the corpus => count of 0. P(word1) = count[word1] / totalCount. Unsmoothed Bigrams create a double hashtable: hashtable (word => hashtable(word=>count)). e.g., { 'write' => {'a' => 3, 'some' => 2}, 'a' => {'program' => 1, 'report' => 2} } Here the outer word index is the first word and the inner word index is the second word. So counts['write']['some'] == 2 means the bigram 'write some' occurred twice. An absence of the index e.g. (1) counts['write']['foobar'] => null or (2) counts['foobar'] => null indicates that (1) that bigram did not occur or (2) that word did not occur at the start of any bigram, meaning that it is a count of 0. P(word1 word2) = count[word1][word2] / count[word1] Note that unigram counts needed for bigram Probabilities. Regular Expression for matching "words" including punctuation and stuff like 's: ('?\w+|\p{Punct}) Java-ized for use in a String literal: ('?\w+|\p{Punct}).

            kandi-support Support

              ngrams has a low active ecosystem.
              It has 25 star(s) with 16 fork(s). There are 8 watchers for this library.
              It had no major release in the last 6 months.
              ngrams has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of ngrams is current.

            kandi-Quality Quality

              ngrams has 0 bugs and 0 code smells.

            kandi-Security Security

              ngrams has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              ngrams code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              ngrams does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              ngrams releases are not available. You will need to build from source code and install.
              ngrams has no build file. You will be need to create the build yourself to build the component from source.
              It has 686 lines of code, 45 functions and 5 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed ngrams and discovered the below as its top functions. This is intended to give you an instant insight into ngrams implemented functionality, and help decide if they suit your requirements.
            • Main entry point for training
            • Calculates the probability of all words in the given set
            • Trains the words in the sample
            • Get the sentence
            • Generate a random word based on the ngram
            • Insert a new string into this tree
            • Calculates Good Turing Counts for each leaf node
            • Returns the good turing probability for the given words
            • Main method for testing
            • Computes the perplexity of all words in the given set
            • Performs training
            • Generate good turing counts
            • Returns the good turing probability for the given word
            • Generates a sentence
            • Generate a sentence
            • Test entry point
            Get all kandi verified functions for this library.

            ngrams Key Features

            No Key Features are available at this moment for ngrams.

            ngrams Examples and Code Snippets

            No Code Snippets are available at this moment for ngrams.

            Community Discussions


            CPU Bound Task - Multiprocessing Approach Performance Way Worse Than Synchronous Approach -Why?
            Asked 2022-Apr-01 at 00:56

            I just get start with asynchronous programming, and I have one questions regarding CPU bound task with multiprocessing. In short, why multiprocessing generated way worse time performance than Synchronous approach? Did I do anything wrong with my code in asynchronous version? Any suggestions are welcome!

            1: Task description

            I want use one of the Google's Ngram datasets as input, and create a huge dictionary includes each words and corresponding words count.

            Each Record in the dataset looks like follow :




            2: Hardware Information: Intel Core i5-5300U CPU @ 2.30 GHz 8GB RAM

            3: Synchronous Version - Time Spent 170.6280147 sec



            Answered 2022-Apr-01 at 00:56

            There's quite a bit I don't understand in your code. So instead I'll just give you code that works ;-)

            • I'm baffled by how your code can run at all. A .gz file is compressed binary data (gzip compression). You should need to open it with Python's As is, I expect it to die with an encoding exception, as it does when I try it.

            • temp[2] is not an integer. It's a string. You're not adding integers here, you're catenating strings with +. int() needs to be applied first.

            • I don't believe I've ever seen asyncio mixed with concurrent.futures before. There's no need for it. asyncio is aimed at fine-grained pseudo-concurrency in a single thread; concurrent.futures is aimed at coarse-grained genuine concurrency across processes. You want the latter here. The code is easier, simpler, and faster without asyncio.

            • While concurrent.futures is fine, I'm old enough that I invested a whole lot into learning the older multiprocessing first, and so I'm using that here.

            • These ngram files are big enough that I'm "chunking" the reads regardless of whether running the serial or parallel version.

            • collections.Counter is much better suited to your task than a plain dict.

            • While I'm on a faster machine than you, some of the changes alluded to above have a lot do with my faster times.

            • I do get a speedup using 3 worker processes, but, really, all 3 were hardly ever being utilized. There's very little computation being done per line of input, and I expect that it's more memory-bound than CPU-bound. All the processes are fighting for cache space too, and cache misses are expensive. An "ideal" candidate for coarse-grained parallelism does a whole lot of computation per byte that needs to be transferred between processes, and not need much inter-process communication at all. Neither are true of this problem.



            Convert bigrams to N-grams in Pyspark dataframe
            Asked 2022-Mar-28 at 19:16

            I have dataframe



            Answered 2022-Mar-28 at 19:16

            A self join can help, the second condition is implemented in the join condition. Then the n-grams are created by combining the arrays of the two sides. When combining the arrays the element that is common in both arrays is omitted:



            Pyspark udf doesn't work while Python function works
            Asked 2022-Mar-21 at 13:31

            I have a Python function:



            Answered 2022-Mar-21 at 13:31

            Hope that's the outcome you are looking for.



            How can I optimize my code to inverse transform the output of TextVectorization?
            Asked 2022-Mar-16 at 13:13

            I'm using a TextVectorization Layer in a TF Keras Sequential model. I need to convert the intermediate TextVectorization layer's output to plain text. I've found that there is no direct way to accomplish this. So I used the TextVectorization layer's vocabulary to inverse transform the vectors. The code is as follows:



            Answered 2022-Mar-16 at 12:37

            Maybe try np.vectorize:



            FailedPreconditionError: Table not initialized
            Asked 2022-Feb-13 at 11:58

            I am trying to create an NLP neural-network using the following code:




            Answered 2022-Feb-13 at 11:58

            The TextVectorization layer is a preprocessing layer that needs to be instantiated before being called. Also as the docs explain:

            The vocabulary for the layer must be either supplied on construction or learned via adapt().

            Another important information can be found here:

            Crucially, these layers are non-trainable. Their state is not set during training; it must be set before training, either by initializing them from a precomputed constant, or by "adapting" them on data

            Furthermore, it is important to note, that the TextVectorization layer uses an underlying StringLookup layer that also needs to be initialized beforehand. Otherwise, you will get the FailedPreconditionError: Table not initialized as you posted.



            How do I access the n-grams produced by FeaturizeText in Microsoft.ML?
            Asked 2022-Feb-10 at 13:35

            I managed to get a first text analyser running in Microsoft.ML. I would like to get to the list of ngrams determined by the model, but I can only get the numerical vectors "counting" occurrences without knowing what they refer to.

            Here is the core of my working code so far:



            Answered 2022-Feb-10 at 13:35

            Well, I figured it out, and wanted to share it here if anyone might bump into this same issue. First, you create your model as usual. Take notice of the name of the column where you put the output of the Ngrams step (in our case "ProduceNgrams").

            Then the combination of "Schema.GetSlotNames" and "slotNames.GetValues" does the trick of fetching the desired ngrams:



            How to tokenize a text using tensorflow?
            Asked 2022-Feb-06 at 12:57

            I am trying to use the following code to vectorize a sentence:



            Answered 2022-Feb-06 at 12:57

            You have to first compute the vocabulary of the TextVectorization layer using either the adapt method or by passing a vocabulary array to the vocabulary argument of the layer. Here is a working example:



            unigrams in SQL
            Asked 2022-Jan-24 at 10:52

            I have the following table (tbl)



            Answered 2022-Jan-24 at 10:37

            You can split text as needed into string array,unnest it and use distinct option for count for ids in group by:



            TensorFlow TextVectorization producing Ragged Tensor with no padding after loading it from pickle
            Asked 2022-Jan-18 at 14:20

            I have a TensorFlow TextVectorization layer named "eng_vectorization":



            Answered 2021-Dec-07 at 12:31

            The problem is related to a very recent bug, where the output_mode is not set correctly when it comes from a saved configuration.

            This works:



            How to use exception handling in pandas while using a function
            Asked 2022-Jan-17 at 22:04

            I have the following dataframe:



            Answered 2022-Jan-09 at 07:22

            So, given the following dataframe:


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


            No vulnerabilities reported

            Install ngrams

            You can download it from GitHub.
            You can use ngrams like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the ngrams component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer For Gradle installation, please refer .


            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 .
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