word2vec | Python interface to Google word2vec | Machine Learning library

 by   danielfrg C Version: 0.11.1 License: Apache-2.0

kandi X-RAY | word2vec Summary

kandi X-RAY | word2vec Summary

word2vec is a C library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Numpy applications. word2vec has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Python interface to Google word2vec. Training is done using the original C code, other functionality is pure Python with numpy.
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            kandi-support Support

              word2vec has a medium active ecosystem.
              It has 2505 star(s) with 626 fork(s). There are 107 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 5 open issues and 46 have been closed. On average issues are closed in 389 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of word2vec is 0.11.1

            kandi-Quality Quality

              word2vec has 0 bugs and 0 code smells.

            kandi-Security Security

              word2vec has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              word2vec code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              word2vec is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

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              word2vec releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.
              It has 659 lines of code, 51 functions and 11 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

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            word2vec Key Features

            No Key Features are available at this moment for word2vec.

            word2vec Examples and Code Snippets

            Define a word2vec .
            pythondot img1Lines of Code : 59dot img1License : Permissive (MIT License)
            copy iconCopy
            def word2vec(batch_gen):
                """ Build the graph for word2vec model and train it """
                # Step 1: define the placeholders for input and output
                # center_words have to be int to work on embedding lookup
            
                # TO DO
            
            
                # Step 2: define weights.  
            Embed word2vec .
            pythondot img2Lines of Code : 53dot img2License : Permissive (MIT License)
            copy iconCopy
            def word2vec(dataset):
                """ Build the graph for word2vec model and train it """
                # Step 1: get input, output from the dataset
                with tf.name_scope('data'):
                    iterator = dataset.make_initializable_iterator()
                    center_words, target_  
            Create a word2vec .
            pythondot img3Lines of Code : 49dot img3License : Permissive (MIT License)
            copy iconCopy
            def word2vec(batch_gen):
                """ Build the graph for word2vec model and train it """
                # Step 1: define the placeholders for input and output
                with tf.name_scope('data'):
                    center_words = tf.placeholder(tf.int32, shape=[BATCH_SIZE], name='  
            How to get the dimensions of a word2vec object in python?
            Pythondot img4Lines of Code : 13dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import gensim
            gensim.__version__
            # 3.6.0
            
            from gensim.test.utils import common_texts
            from gensim.models import Word2Vec
            
            model = Word2Vec(sentences=common_texts, window=5, min_count=1, workers=4) # do not specify size, leave the default 10
            Can't see why this variable is not defined
            Pythondot img5Lines of Code : 13dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # total vocabulary size plus 0 for unknown words
            vocab_size =   25768
            
            def get_weight_matrix(embedding, vocab):
                #len(vocab) + 1
                # define weight matrix dimensions with all 0
                weight_matrix = np.zeros((vocab_size, 100))
                # step
            How to get average pairwise cosine similarity per group in Pandas
            Pythondot img6Lines of Code : 31dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # get average if more than 1 word is included in the "text" column
            def document_vector(items):
                # remove out-of-vocabulary words
                doc = [word for word in items.split() if word in model_glove]
                if doc:
                    doc_vector = model_gl
            Word2Vec dimensions incorrect
            Pythondot img7Lines of Code : 12dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            X = [[word2idx[w[0]] for w in s] for s in sentences]
            X = np.array(X)
            print(X.shape)
            
            wrd2vec_size = 30
            input_word = Input(shape=(max_len, wrd2vec_size))
            x = SpatialDropout1D(0.1)(input_word)
            x = Bidirectional(LSTM(u
            Unpickle instance from Jupyter Notebook in Flask App
            Pythondot img8Lines of Code : 16dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            ├── WebApp/
            │  └── app.py
            └── Untitled.ipynb
            
            from WebApp.app import GensimWord2VecVectorizer
            GensimWord2VecVectorizer.__module__ = 'app'
            
            import sys
            sys.modules['app'] = sys.modules['WebApp.app']
            
            Using gensim most_similar function on a subset of total vocab
            Pythondot img9Lines of Code : 5dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            finite_set = set(['word_d', 'word_e', 'word_f'])  # set for efficient 'in'
            all_candidates = wv_from_bin.most_similar(positive=["word_a", "word_b"],
                                                      topn=len(vw_from_bin))
            filtered_results = [word_s
            Word2Vec returning vectors for individual character and not words
            Pythondot img10Lines of Code : 18dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from gensim.models import Word2Vec
            
            words= [['S.B.MILS','Siblings','DEVASTHALY','KOTRESHA'],
            ['unimodal','7','regarding','random','59','intimating'],
            ['COMPETITION','prospects','2K15','gather','Mega'],
            ['SENSOR','NCTT','NETWORKING','orgain

            Community Discussions

            QUESTION

            How do I adapt code to make CNN model compatible with a higher dimension word embedding?
            Asked 2022-Apr-09 at 10:09

            I have been following an online tutorial on 1D CNN for text classification. I have got the model to work with a self trained word2vec embedding of 100 dimensions, but I want to see how the model would preform when given a higher dimensional word embedding.

            I have tried downloading a 300 dimension word2vec model and adding the .txt file in the CNN model and changing any dimensions from a 100 to 300. The model runs but produces bad results, the accuracy is 'nan' and the loss is 0.000 for all epochs.

            What would i have to change for the model to work with the 300 dimension word2vec model? Thanks i have added the code below:

            ...

            ANSWER

            Answered 2022-Apr-08 at 15:49

            If you are using 300-dimensional vectors you need to change two things in your code. This line:

            Source https://stackoverflow.com/questions/71789971

            QUESTION

            How to get average pairwise cosine similarity per group in Pandas
            Asked 2022-Mar-29 at 20:51

            I have a sample dataframe as below

            ...

            ANSWER

            Answered 2022-Mar-29 at 18:47

            Remove the .vocab here in model_glove.vocab, this is not supported in the current version of gensim any more: Edit: also needs split() to iterate over words and not characters here.

            Source https://stackoverflow.com/questions/71666450

            QUESTION

            Unpickle instance from Jupyter Notebook in Flask App
            Asked 2022-Feb-28 at 18:03

            I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:

            AttributeError: module '__main__' has no attribute 'GensimWord2VecVectorizer'

            I am creating the model on Google Colab.

            Code in Jupyter Notebook:

            ...

            ANSWER

            Answered 2022-Feb-24 at 11:48

            Import GensimWord2VecVectorizer in your Flask Web app python file.

            Source https://stackoverflow.com/questions/71231611

            QUESTION

            Word2Vec returning vectors for individual character and not words
            Asked 2022-Feb-12 at 13:11

            For the following list:

            ...

            ANSWER

            Answered 2022-Feb-12 at 13:11

            Word2Vec expects a list of lists as input, where the corpus (main list) is composed of individual documents. The individual documents are composed of individual words (tokens). Word2Vec iterates over all documents and all tokens. In your example you have passed a single list to Word2Vec, therefore Word2Vec interprets each word as an individual document and iterates over each word character which is interpreted as a token. Therefore you have built a vocabulary of characters not words. To build a vocabulary of words you can pass a nested list to Word2Vec as in the example below.

            Source https://stackoverflow.com/questions/71091209

            QUESTION

            Plotly - Highlight data point and nearest three points on hover
            Asked 2022-Feb-02 at 04:15

            I have made a scatter plot of the word2vec model using plotly.
            I want functionality of highlighting the specific data point on hover along with the top 3 nearest vectors to that. It would be of great help if anyone can guide me with this or suggest any other option

            model
            csv

            Code:

            ...

            ANSWER

            Answered 2022-Feb-02 at 04:15

            In plotly-python, I don't think there's an easy way of retrieving the location of the cursor. You can attempt to use go.FigureWidget to highlight a trace as described in this answer, but i think you're going to be limited with with plotly-python and i'm not sure if highlighting the closest n points will be possible.

            However, I believe that you can accomplish what you want in plotly-dash since callbacks are supported - meaning you would be able to retrieve location of your cursor and then calculate the n closest data points to your cursor and highlight the data points as needed.

            Below is an example of such a solution. If you haven't seen it before, it looks complicated, but what is happening is that I am taking the point where you clicked as an input. plotly is plotly.js under the hood so it comes us in the form of a dictionary (and not some kind of plotly-python object). Then I calculate the closest three data points to the clicked input point by comparing the coordinates of every other point in the dataframe, add the information from the three closest points as traces to the input with the color teal (or any color of your choosing), and send this modified input back as the output, and update the figure.

            I am using click instead of hover because hover would cause the highlighted points to flicker too much as you drag your mouse through the points.

            Also the dash app doesn't work perfectly as I believe there is some issue when you double click on points (you can see me click once in the gif below before getting it to start working), but this basic framework is hopefully close enough to what you want. Cheers!

            Source https://stackoverflow.com/questions/70944316

            QUESTION

            gensim w2k - additional file
            Asked 2022-Feb-01 at 14:52

            I trained w2v on rather big (> 200 million sentences) corpus, and got, in addition to file w2v_model.model, files: w2v_model.model.trainables.syn1neg.npy and w2v.model_model.wv.vectors.npy. Model file was successfully loaded and read all npy files without any exceptions. The obtained model performed OK.

            Now I retrained the model on much bigger corpus (> 1 billion sentences). The same 3 files were automatically saved, as expected.

            When I try to load my new retrained model:

            ...

            ANSWER

            Answered 2022-Jan-24 at 18:39

            If a .save() is creating any files with the word trainables in it, you're using a older version fo Gensim. Any new training should definitely prefer using a current version. As of now (January 2022), that's gensim-4.1.2, released 2021-09.

            If an attempt at a .load() generated that particular error, then there should've been that file, alongside the others you mention, created when the .save() had been done. (In fact, the only way that the main file you named with path_filename should be able to know that other filename is if that other file was written successfully, allowing the main file to complete writing.)

            Are you sure that file wasn't written, but then somehow left behind, perhaps getting deleted or not moving alongside the other few files to some new filesystem path?

            In general, I would suggest:

            • using latest Gensim for any new training
            • always enable Python logging at the INFO level, & watch the logging/console output of training/saving processes closely to see confirmation of expected activity/steps
            • keep all files from a .save() that begin with the same main filename (in your examples above, w2v_US.model) together - & keep in mind that for larger models it may be a larger roster of files than for a small test model

            You will probably have to re-train the model, but you might be able to re-generate a compatible lockf file via steps like the following:

            • save aside all files of any potential use
            • from the exact same configuration as your original .save() – including the same outdated Gensim version, exact same model parameters, & exact same training corpus – repeat all the model-building steps you did before up through the .build_vocab() step. (That is: no extra need to .train().) This will create an untrained dummy model that should exactly match the vocabulary 'shape' of your broken model.
            • use .save() to save that dummy model again - watching the logs/output for errors. There should be, alongside the other files, a file with a name like dummy.model.trainables.vectors_lockf.npy. If so, you might be able to copy that away, rename it to tbe the file expected by the original model whose load failed, then leave it alongside that original model - and the .load() might then succeed, or fail in a different way.

            (If there were other problems/corruption at the time of the original model creation, this might not work. In particular, I wonder if when you talk about retraining the model, you didn't start with a fresh Word2Vec instance, but somehow expanded the older one, which might've added other problems/complications. In that case, a full retraining, ideally in the latest Gensim, would be necessary, and also a better basis for going forward.)

            Source https://stackoverflow.com/questions/70693372

            QUESTION

            How to plot tsne on word2vec (created from gensim) for the most_similar 20 cases?
            Asked 2022-Jan-05 at 08:42

            I am using TSNE to plot a trained word2vec model (created from gensim):

            ...

            ANSWER

            Answered 2022-Jan-05 at 08:42

            Using an example from the package:

            Source https://stackoverflow.com/questions/70268270

            QUESTION

            disable logging for specific lines of code
            Asked 2021-Nov-20 at 11:45

            I am tuning the word2vec model hyper-parameters. Word2Vec has to many log in console that I cannot read Optuna or my custom log. Is there any trick to suppress logs generated by Word2Vec?

            ...

            ANSWER

            Answered 2021-Nov-19 at 22:09

            Gensim's classes generally only log if you specifically turn it on, in your code, by setting either a global or module/class-specific logging level.

            So, are you sure you didn't turn on more logging that you want?

            Search your code for anything that sets an INFO or DEBUG level of logging - and either delete or adjust/narrow that line to either not enable, or to set a more restrictie level, on the word2vec module or Word2Vec class.

            Source https://stackoverflow.com/questions/70039495

            QUESTION

            How to seek for bigram similarity in gensim word2vec model
            Asked 2021-Nov-10 at 23:39

            Here I have a word2vec model, suppose I use the google-news-300 model

            ...

            ANSWER

            Answered 2021-Nov-10 at 23:39

            At one level, when a word-token isn't in a fixed set of word-vectors, the creators of that set of word-vectors chose not to train/model that word. So, anything you do will only be a crude workaround for its absence.

            Note, though, that when Google prepared those vectors – based on a dataset of news articles from before 2012 – they also ran some statistical multigram-combinations on it, creating multigrams with connecting _ characters. So, first check if a vector for 'artificial_intelligence' might be present.

            If it isn't, you could try other rough workarounds like averaging together the vectors for 'artificial' and 'intelligence' – though of course that won't really be what people mean by the distinct combination of those words, just meanings suggested by the independent words.

            The Gensim .most_similar() method can take either a raw vectors you've created by operations such as averaging, or even a list of multiple words which it will average for you, as arguments via its explicit keyword positive parameter. For example:

            Source https://stackoverflow.com/questions/69909863

            QUESTION

            Confusion Matrix ValueError: Classification metrics can't handle a mix of binary and continuous targets
            Asked 2021-Nov-07 at 19:04

            I'm currently trying to make a confusion matrix for my neural network model, but keep getting this error:

            ...

            ANSWER

            Answered 2021-Nov-07 at 19:04

            The model outputs the predicted probabilities, you need to transform them back to class labels before calculating the classification metrics, see below.

            Source https://stackoverflow.com/questions/69875073

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

            Vulnerabilities

            No vulnerabilities reported

            Install word2vec

            You can download it from GitHub.

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            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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            pip install word2vec

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            https://github.com/danielfrg/word2vec.git

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            gh repo clone danielfrg/word2vec

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            git@github.com:danielfrg/word2vec.git

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