Gensim Download Word2vec-google-news-300

it is much easier to use the gensim api for dowloading the word2vec compressed model by google, it will be stored in /home/"your_username"/gensim-data/word2vec-google-news-300/ . Load the vectors and play ball. I have 16GB of RAM which is more than enough to handle the model

I tried to use gensim.downloader to download word2vec-google-news-300, but my network isn't very reliable, so I downloaded word2vec-google-news-300.gz and __init__.py from github and put them into ~/gensim-data/word2vec-google-news-300/.

Download File 🔥 https://urlin.us/2y4NLG 🔥

The trained word vectors can also be stored/loaded from a format compatible with theoriginal word2vec implementation via self.wv.save_word2vec_formatand gensim.models.keyedvectors.KeyedVectors.load_word2vec_format().

I use a simple python function load_bin_vec shown as follows to load the Google pretrained .bin model. But I find that the outputs are different from the results using the load_word2vec_format function in gensim.models.Word2Vec.

Step 9: Summarize Text Documents

The summarize( ) function implements the text rank summarization.

You do not have to generate a tokenized list by splitting the sentences as that is already handled by the gensim.summarization.textcleaner module.

Code:

The FastText project provides word-embeddings for 157 different languages, trained on Common Crawl and Wikipedia. These word embeddings can easily be downloaded and imported to Python. The KeyedVectors-class of gensim can be applied for the import. This class also provides many useful tools, e.g. an index to fastly find the vector of an arbitrary word or function to calculate similarities between word-vectors. Some of these tools will be demonstrated below:

As described before GloVe constitutes another method for calculating Word-Embbedings. Pre-trained GloVe vectors can be downloaded fromGlove and imported into Python. However, gensim already provides a downloader for several word-embeddings, including GloVe embeddings of different length and different training-data.

In this section it is demonstrated how gensim can be applied to train a Word2Vec (either CBOW or Skipgram) embedding from an arbitrary corpus. In this demo the applied training corpus is the complete English Wikipedia dump.

In the following code cell a name for the word2vec-model is specified. If the specified directory already contains a model with the specified name, it is loaded. Otherwise, it is generated and saved under the specified name. A skipgram-model can be generated in the same way. In this case model = word2vec.Word2Vec(sentences,size=200,sorted_vocab=1) has to be replaced by model = word2vec.Word2Vec(sentences,size=200,sorted_vocab=1,sg=1).See gensim model.Word2Vec documentation for the configuration of more parameters.

The technical context of this article is Python v3.11 and several additional libraries: gensim v4.3.1, pandas v2.0.1, numpy v1.26.1, nltk v3.8.1 and scikit-learn v1.2.2. All examples should work with newer library versions too.

In the following implementation, the Gensim library will be used to load pretrained Word2Vec vectors and apply them to the corpus. To use one of the pretrained models, you need to download its model with a Gensim helper. Note that the models can be very large. For example, the word2vec-google-news-300 models is 1.6GB and provides 300-dimensional vectors for each word.

word2vec model trained on the concatenation of all the individual universities corpora. To generate the word embeddings of the corpus, the gensim implementation of word2vec (CBOW) was used. For training the word embeddings model, the following parameters were used: vector dimensions=300, window size=10, negative sampling=10, down sampling frequent words = 0.00008 (downsamples 612 most-common words), number of iterations (epochs) through the corpus=10, maximum final vocabulary= 3 million. The maximum final vocabulary resulted in an effective minimum frequency count of 20. That is, only terms that appear more than 20 times in the corpus were included into the word embedding model vocabulary. The exponent used to shape the negative sampling distribution was 0.5.

It is a great package for processing texts, working with word vector models (such as Word2Vec, FastText etc) and for building topic models.Also, another significant advantage with gensim is: it lets you handle large text files without having to load the entire file in memory.

In gensim, the dictionary contains a map of all words (tokens) to its unique id.You can create a dictionary from a paragraph of sentences, from a text file that contains multiple lines of text and from multiple such text files contained in a directory. For the second and third cases, we will do it without loading the entire file into memory so that the dictionary gets updated as you read the text line by line.

How is TFIDF computed?Tf-Idf is computed by multiplying a local component like term frequency (TF) with a global component, that is, inverse document frequency (IDF) and optionally normalizing the result to unit length.As a result of this, the words that occur frequently across documents will get downweighted.There are multiple variations of formulas for TF and IDF existing. Gensim uses the SMART Information retrieval system that can be used to implement these variations. You can specify what formula to use specifying the smartirs parameter in the TfidfModel. See help(models.TfidfModel) for more details.So, how to get the TFIDF weights?By training the corpus with models.TfidfModel(). Then, apply the corpus within the square brackets of the trained tfidf model. See example below.from gensim import modelsimport numpy as npdocuments = ["This is the first line", "This is the second sentence", "This third document"]# Create the Dictionary and Corpusmydict = corpora.Dictionary([simple_preprocess(line) for line in documents])corpus = [mydict.doc2bow(simple_preprocess(line)) for line in documents]# Show the Word Weights in Corpusfor doc in corpus: print([[mydict[id], freq] for id, freq in doc])# [['first', 1], ['is', 1], ['line', 1], ['the', 1], ['this', 1]]# [['is', 1], ['the', 1], ['this', 1], ['second', 1], ['sentence', 1]]# [['this', 1], ['document', 1], ['third', 1]]# Create the TF-IDF modeltfidf = models.TfidfModel(corpus, smartirs='ntc')# Show the TF-IDF weightsfor doc in tfidf[corpus]: print([[mydict[id], np.around(freq, decimals=2)] for id, freq in doc])# [['first', 0.66], ['is', 0.24], ['line', 0.66], ['the', 0.24]]# [['is', 0.24], ['the', 0.24], ['second', 0.66], ['sentence', 0.66]]# [['document', 0.71], ['third', 0.71]]Notice the difference in weights of the words between the original corpus and the tfidf weighted corpus.

To get the document vector of a sentence, pass it as a list of words to the infer_vector() method.print(model.infer_vector(['australian', 'captain', 'elected', 'to', 'bowl']))#> array([-0.11043505, 0.21719663, -0.21167697, -0.10790558, 0.5607173 ,#> ...#> 0.16428669, -0.31307793, -0.28575218, -0.0113026 , 0.08981086],#> dtype=float32)18. How to compute similarity metrics like cosine similarity and soft cosine similarity?Soft cosine similarity is similar to cosine similarity but in addition considers the semantic relationship between the words through its vector representation.To compute soft cosines, you will need a word embedding model like Word2Vec or FastText. First, compute the similarity_matrix. Then convert the input sentences to bag-of-words corpus and pass them to the softcossim() along with the similarity matrix.from gensim.matutils import softcossimfrom gensim import corporasent_1 = 'Sachin is a cricket player and a opening batsman'.split()sent_2 = 'Dhoni is a cricket player too He is a batsman and keeper'.split()sent_3 = 'Anand is a chess player'.split()# Prepare the similarity matrixsimilarity_matrix = fasttext_model300.similarity_matrix(dictionary, tfidf=None, threshold=0.0, exponent=2.0, nonzero_limit=100)# Prepare a dictionary and a corpus.documents = [sent_1, sent_2, sent_3]dictionary = corpora.Dictionary(documents)# Convert the sentences into bag-of-words vectors.sent_1 = dictionary.doc2bow(sent_1)sent_2 = dictionary.doc2bow(sent_2)sent_3 = dictionary.doc2bow(sent_3)# Compute soft cosine similarityprint(softcossim(sent_1, sent_2, similarity_matrix))#> 0.7868705819999783print(softcossim(sent_1, sent_3, similarity_matrix))#> 0.6036445529268666print(softcossim(sent_2, sent_3, similarity_matrix))#> 0.60965453519611Below are some useful similarity and distance metrics based on the word embedding models like fasttext and GloVe. We have already downloaded these models using the downloader API.

We have covered a lot of ground about the various features of gensim and get a good grasp on how to work with and manipulate texts. The above examples should serve as nice templates to get you started and build upon for various NLP tasks. Hope you will find it helpful and feel comfortable to use gensim more often in your NLP projects. e24fc04721