updates

Backend/venv/lib/python3.12/site-packages/nltk/test/gensim.doctest

@@ -0,0 +1,141 @@
+.. Copyright (C) 2001-2025 NLTK Project
+.. For license information, see LICENSE.TXT
+
+=======================================
+Demonstrate word embedding using Gensim
+=======================================
+
+    >>> from nltk.test.gensim_fixt import setup_module
+    >>> setup_module()
+
+We demonstrate three functions:
+- Train the word embeddings using brown corpus;
+- Load the pre-trained model and perform simple tasks; and
+- Pruning the pre-trained binary model.
+
+    >>> import gensim
+
+---------------
+Train the model
+---------------
+
+Here we train a word embedding using the Brown Corpus:
+
+    >>> from nltk.corpus import brown
+    >>> train_set = brown.sents()[:10000]
+    >>> model = gensim.models.Word2Vec(train_set)
+
+It might take some time to train the model. So, after it is trained, it can be saved as follows:
+
+    >>> model.save('brown.embedding')
+    >>> new_model = gensim.models.Word2Vec.load('brown.embedding')
+
+The model will be the list of words with their embedding. We can easily get the vector representation of a word.
+
+    >>> len(new_model.wv['university'])
+    100
+
+There are some supporting functions already implemented in Gensim to manipulate with word embeddings.
+For example, to compute the cosine similarity between 2 words:
+
+    >>> new_model.wv.similarity('university','school') > 0.3
+    True
+
+---------------------------
+Using the pre-trained model
+---------------------------
+
+NLTK includes a pre-trained model which is part of a model that is trained on 100 billion words from the Google News Dataset.
+The full model is from https://code.google.com/p/word2vec/ (about 3 GB).
+
+    >>> from nltk.data import find
+    >>> word2vec_sample = str(find('models/word2vec_sample/pruned.word2vec.txt'))
+    >>> model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_sample, binary=False)
+
+We pruned the model to only include the most common words (~44k words).
+
+    >>> len(model)
+    43981
+
+Each word is represented in the space of 300 dimensions:
+
+    >>> len(model['university'])
+    300
+
+Finding the top n words that are similar to a target word is simple. The result is the list of n words with the score.
+
+    >>> model.most_similar(positive=['university'], topn = 3)
+    [('universities', 0.70039...), ('faculty', 0.67809...), ('undergraduate', 0.65870...)]
+
+Finding a word that is not in a list is also supported, although, implementing this by yourself is simple.
+
+    >>> model.doesnt_match('breakfast cereal dinner lunch'.split())
+    'cereal'
+
+Mikolov et al. (2013) figured out that word embedding captures much of syntactic and semantic regularities. For example,
+the vector 'King - Man + Woman' is close to 'Queen' and 'Germany - Berlin + Paris' is close to 'France'.
+
+    >>> model.most_similar(positive=['woman','king'], negative=['man'], topn = 1)
+    [('queen', 0.71181...)]
+
+    >>> model.most_similar(positive=['Paris','Germany'], negative=['Berlin'], topn = 1)
+    [('France', 0.78840...)]
+
+We can visualize the word embeddings using t-SNE (https://lvdmaaten.github.io/tsne/). For this demonstration, we visualize the first 1000 words.
+
+|    import numpy as np
+|    labels = []
+|    count = 0
+|    max_count = 1000
+|    X = np.zeros(shape=(max_count,len(model['university'])))
+|
+|    for term in model.index_to_key:
+|        X[count] = model[term]
+|        labels.append(term)
+|        count+= 1
+|        if count >= max_count: break
+|
+|    # It is recommended to use PCA first to reduce to ~50 dimensions
+|    from sklearn.decomposition import PCA
+|    pca = PCA(n_components=50)
+|    X_50 = pca.fit_transform(X)
+|
+|    # Using TSNE to further reduce to 2 dimensions
+|    from sklearn.manifold import TSNE
+|    model_tsne = TSNE(n_components=2, random_state=0)
+|    Y = model_tsne.fit_transform(X_50)
+|
+|    # Show the scatter plot
+|    import matplotlib.pyplot as plt
+|    plt.scatter(Y[:,0], Y[:,1], 20)
+|
+|    # Add labels
+|    for label, x, y in zip(labels, Y[:, 0], Y[:, 1]):
+|        plt.annotate(label, xy = (x,y), xytext = (0, 0), textcoords = 'offset points', size = 10)
+|
+|    plt.show()
+
+------------------------------
+Prune the trained binary model
+------------------------------
+
+Here is the supporting code to extract part of the binary model (GoogleNews-vectors-negative300.bin.gz) from https://code.google.com/p/word2vec/
+We use this code to get the `word2vec_sample` model.
+
+|    import gensim
+|    # Load the binary model
+|    model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary = True)
+|
+|    # Only output word that appear in the Brown corpus
+|    from nltk.corpus import brown
+|    words = set(brown.words())
+|    print(len(words))
+|
+|    # Output presented word to a temporary file
+|    out_file = 'pruned.word2vec.txt'
+|    with open(out_file,'w') as f:
+|        word_presented = words.intersection(model.index_to_key)
+|        f.write('{} {}\n'.format(len(word_presented),len(model['word'])))
+|
+|        for word in word_presented:
+|            f.write('{} {}\n'.format(word, ' '.join(str(value) for value in model[word])))