20 Gensim concepts with Before-and-After Examples

1. Dictionary (gensim.corpora.Dictionary) 📖

Boilerplate Code:

from gensim.corpora import Dictionary

Use Case: Create a dictionary that maps words to unique IDs, which is essential for processing text in Gensim. 📖

Goal: Convert raw text into a bag-of-words format. 🎯

Sample Code:

# Example text
texts = [["hello", "world"], ["world", "of", "gensim"]]

# Create a dictionary
dictionary = Dictionary(texts)
print(dictionary.token2id)

Before Example: has text but doesn’t know how to represent each word with a unique ID. 🤔

Texts: [["hello", "world"], ["world", "of", "gensim"]]

After Example: With Dictionary, each word is mapped to a unique ID! 📖

Dictionary: {'hello': 0, 'world': 1, 'of': 2, 'gensim': 3}

Challenge: 🌟 Try creating a dictionary from a larger text corpus and see how many unique words are there.

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2. Corpus (gensim.corpora.MmCorpus) 📚

Boilerplate Code:

from gensim import corpora

Use Case: Build a corpus, which represents documents as a collection of vectors (bag-of-words). 📚

Goal: Convert documents into a numerical format that Gensim can process. 🎯

Sample Code:

# Example text
texts = [["hello", "world"], ["world", "of", "gensim"]]

# Create a dictionary and corpus
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
print(corpus)

Before Example: has raw text but doesn’t know how to represent it as numerical vectors. 🤔

Texts: [["hello", "world"], ["world", "of", "gensim"]]

After Example: With corpus, the documents are represented as vectors! 📚

Corpus: [[(0, 1), (1, 1)], [(1, 1), (2, 1), (3, 1)]]

Challenge: 🌟 Try saving the corpus to disk and reloading it using MmCorpus.
You're right! The title mentions MmCorpus, but the example code doesn't actually use it. Let me clarify and explain how MmCorpus fits in, and give you a relevant example.

In the sample code, you're converting a collection of texts into a corpus (a collection of documents represented as vectors using the bag-of-words model). This means each word in a document is transformed into a unique identifier (an integer), and then the frequency of each word is stored in a vector.

The MmCorpus function is used when you want to save your corpus to disk and reload it later in a more efficient format. The name "Mm" stands for Matrix Market format, which is an efficient way to store sparse matrices (like a corpus) that you can save to a file and reuse without having to recompute the bag-of-words vector.

from gensim import corpora

# Example text
texts = [["hello", "world"], ["world", "of", "gensim"]]

# Create a dictionary and corpus (bag-of-words representation)
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

# Save the corpus to disk using MmCorpus
corpora.MmCorpus.serialize('corpus.mm', corpus)

# Load the corpus from disk
loaded_corpus = corpora.MmCorpus('corpus.mm')

print(list(loaded_corpus))  # Corpus reloaded from disk

• Creating the corpus: You're still creating the bag-of-words corpus like before.

• Saving with MmCorpus: MmCorpus.serialize() saves your corpus in Matrix Market format to a file (corpus.mm).

• Loading with MmCorpus: You can reload it later using MmCorpus('corpus.mm') without having to recompute the entire bag-of-words model

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3. TF-IDF Model (gensim.models.TfidfModel) 📊

Boilerplate Code:

from gensim.models import TfidfModel

Use Case: Create a TF-IDF model to weigh words based on their frequency and importance in a corpus. 📊

Goal: Adjust word importance based on how often they appear in the corpus. 🎯

Sample Code:

# Example text
texts = [["hello", "world"], ["world", "of", "gensim"]]

# Create dictionary and corpus
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

# Initialize TF-IDF model
tfidf = TfidfModel(corpus)
tfidf_corpus = tfidf[corpus]
print(list(tfidf_corpus))

Before Example: we have word counts but doesn’t know which words are important in the context of the corpus. 🤔

Corpus: [[(0, 1), (1, 1)], [(1, 1), (2, 1), (3, 1)]]

After Example: With TF-IDF, the importance of words is adjusted based on their frequency in the corpus! 📊

TF-IDF Scores: Weights for each word in the corpus.

Challenge: 🌟 Try applying TF-IDF to a large document set and identify the most important words.

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4. LDA Model (gensim.models.LdaModel) 🔥

Boilerplate Code:

from gensim.models import LdaModel

Use Case: Perform Latent Dirichlet Allocation (LDA) for topic modeling, which discovers topics in a set of documents. 🔥

Goal: Identify hidden topics in the text data. 🎯

Sample Code:

# Example text
texts = [["hello", "world"], ["world", "of", "gensim"]]

# Create dictionary and corpus
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

# Train LDA model
lda = LdaModel(corpus, num_topics=2, id2word=dictionary)
print(lda.print_topics())

Before Example: doesn’t know the underlying topics. 🤔

Texts: [["hello", "world"], ["world", "of", "gensim"]]

After Example: With LDA, we discover topics hidden in the documents! 🔥

Topics: [(0, "0.5*'world' + 0.5*'hello'"), (1, "0.33*'world' + 0.33*'gensim' ...")]

Challenge: 🌟 Try experimenting with more topics and see how they cluster together.

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5. Word2Vec Model (gensim.models.Word2Vec) 🔠

Boilerplate Code:

from gensim.models import Word2Vec

Use Case: Create a Word2Vec model to generate word embeddings, which represent words as vectors. 🔠

Goal: Represent words as vectors for machine learning tasks. 🎯

Sample Code:

# Example text
sentences = [["hello", "world"], ["gensim", "is", "cool"]]

# Train Word2Vec model
model = Word2Vec(sentences, vector_size=10, window=2, min_count=1)

# Get word vector
print(model.wv['hello'])

Before Example: has words but doesn’t know how to represent them as vectors. 🤔

Words: ["hello", "world"]

After Example: With Word2Vec, we represent words as vectors! 🔠

Word Vector: [0.123, -0.456, 0.789, ...]

Challenge: 🌟 Try finding similar words using model.wv.most_similar().

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6. Document Similarity (gensim.similarities.Similarity) 🔗

Boilerplate Code:

from gensim.similarities import MatrixSimilarity

Use Case: Compare document similarity by calculating how close documents are based on their vector representations. 🔗

Goal: Measure the similarity between documents. 🎯

Sample Code:

# Example text
texts = [["hello", "world"], ["world", "of", "gensim"]]

# Create dictionary, corpus, and TF-IDF model
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
tfidf = TfidfModel(corpus)
tfidf_corpus = tfidf[corpus]

# Compute similarity matrix
similarity_index = MatrixSimilarity(tfidf_corpus)
print(list(similarity_index))

Before Example: Has multiple documents but doesn’t know how similar they are to each other. 🤔

Documents: [["hello", "world"], ["world", "of", "gensim"]]

After Example: With similarity indexing, we can measure the similarity between documents! 🔗

Similarity: Scores between 0 and 1 for each document pair.

Challenge: 🌟 Try using the similarity matrix for document clustering or retrieval.

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7. Coherence Model (gensim.models.CoherenceModel) 📈

Boilerplate Code:

from gensim.models import CoherenceModel

Use Case: Evaluate the quality of a topic model using coherence scores. 📈
High-Quality Topics: (ie politics) The words are closely related and clearly form a distinct, interpretable topic. A human can easily understand what the topic is about.
Words in the topic: ['election', 'government', 'candidate', 'voting', 'policy', 'debate']

Low-Quality Topics(Incoherent Mix) The words seem random or unrelated, making it difficult or impossible to assign meaning to the topic. Words in the topic: ['apple', 'election', 'sky', 'music', 'policy', 'river']

Goal: Assess the interpretability of the topics discovered by the LDA model. 🎯

Sample Code:

# Example text
texts = [["hello", "world"], ["world", "of", "gensim"]]

# Create dictionary, corpus, and LDA model
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
lda = LdaModel(corpus, num_topics=2, id2word=dictionary)

# Compute coherence score
coherence_model_lda = CoherenceModel(model=lda, texts=texts, dictionary=dictionary, coherence='c_v')
coherence_score = coherence_model_lda.get_coherence()
print(coherence_score)

Before Example: has a topic model but doesn’t know how good the topics are. 🤔

LDA Model: 2 topics

After Example: With coherence score, we can measures the quality of the topics!

Coherence Score: A value between 0 and 1 (higher is better).

Challenge: 🌟 Try tuning the LDA model’s parameters to improve the coherence score.

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8. Phrases (gensim.models.Phrases) 🗣️

Boilerplate Code:

from gensim.models import Phrases

Use Case: Detect common phrases or collocations in the text (e.g., "New York"). 🗣️

Goal: Identify and represent common multi-word expressions as single tokens. 🎯

Sample Code:

# Example text
sentences = [["new", "york", "city"], ["new", "york", "times"]]

# Train Phrases model
phrases = Phrases(sentences, min_count=1, threshold=1)

# Apply model to detect phrases
bigram = phrases[sentences]
print(list(bigram))

Before Example: we have word sequences but doesn’t know how to detect phrases. 🤔

Sentences: [["new", "york", "city"], ["new", "york", "times"]]

After Example: With Phrases, we identify common phrases like "New York"!

Phrases: [('new_york', 'city'), ('new_york', 'times')]

Challenge: 🌟 Try detecting trigrams (three-word phrases) in a larger dataset.

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9. KeyedVectors (gensim.models.KeyedVectors) 💡

Boilerplate Code:

from gensim.models import KeyedVectors

Use Case: Use pre-trained word vectors from sources like Word2Vec or GloVe. 💡

Goal: Load pre-trained vectors and apply them for similarity comparisons. 🎯

Sample Code:

# Load pre-trained vectors
model = KeyedVectors.load_word2vec_format('path/to/vectors.bin', binary=True)

# Get vector for a word
vector = model['word']
print(vector)

Before Example: We need word vectors but doesn’t want to train a model from scratch. 🤔

Need: Pre-trained word vectors.

After Example: With KeyedVectors, we load pre-trained vectors and applies them! 💡

Word Vector: [0.345, -0.234, ...]

Challenge: 🌟 Try using pre-trained vectors to find similar words in a new dataset.

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10. FastText Model (gensim.models.FastText) 🚀

Boilerplate Code:

from gensim.models import FastText

Use Case: Train a FastText model for word embeddings, which captures subword information and works well with rare words. 🚀
Subword information refers to breaking words down into smaller parts, like prefixes, suffixes, or even individual characters, which can be used to build better word embeddings—especially for rare or misspelled words.

Example:

Let’s take the word “unhappiness”.

In a traditional word embedding model, the entire word is treated as a single unit. If the model has never seen "unhappiness" before, it won't know how to generate a good embedding for it.

In FastText, however, the word can be broken down into subwords, like:

• Prefixes: "un-", "hap-"

• Root: "happy"

• Suffixes: "-ness"

So, instead of treating "unhappiness" as a completely unknown word, FastText looks at the subword parts (like "happy") that are more common and uses them to create the embedding.

Why is this useful?

1. Rare Words: For words that don’t appear frequently, FastText can still build meaningful embeddings by analyzing the subwords it has seen before.

   • Example: For a rare word like "bioluminescence", FastText can break it into "bio-", "lumin-", and "-escence" to generate an embedding based on known subwords.

2. Misspelled Words: FastText can handle misspellings by looking at parts of the word that it recognizes.

   • Example: Even if "happiness" is misspelled as "happpiness", FastText can still generate a reasonable embedding by identifying the subword "happy".

Example:

• Traditional embedding: The word "happiness" would be treated as one unit, and if it’s not in the vocabulary, it would return an unknown vector.

• FastText: It breaks "happiness" into smaller parts like "hap-", "piness", etc., which helps it still generate an embedding based on familiar subwords.

FastText helps generate better word embeddings for words it hasn’t seen before or for misspelled words by breaking them into subword components. This allows it to capture more nuanced information about words 🚀!

Goal: Generate word vectors that include information about character-level features. 🎯

Sample Code:

# Example text
sentences = [["hello", "world"], ["gensim", "is", "awesome"]]

# Train FastText model
model = FastText(sentences, vector_size=10, window=2, min_count=1)

# Get vector for a word
print(model.wv['hello'])

Before Example: has text but needs better embeddings for rare or misspelled words.

Words: ["hello", "world", "gensim"]

After Example: With FastText, we can generate embeddings that capture subword information! 🚀

Word Vector: [0.123, -0.456, ...]

Challenge: 🌟 Try training FastText on a dataset with rare or noisy text and see how it performs.

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11. Doc2Vec Model (gensim.models.Doc2Vec) 📝

Boilerplate Code:

from gensim.models import Doc2Vec
from gensim.models.doc2vec import TaggedDocument

Use Case: Train a Doc2Vec model to represent entire documents as vectors, which is useful for document similarity and classification tasks. 📝

Goal: Convert documents into vectors while keeping context intact. 🎯

Sample Code:

# Example text
documents = [TaggedDocument(words=["gensim", "is", "awesome"], tags=[0]), 
             TaggedDocument(words=["machine", "learning", "with", "gensim"], tags=[1])]

# Train Doc2Vec model
model = Doc2Vec(documents, vector_size=10, window=2, min_count=1)

# Get document vector
vector = model.dv[0]
print(vector)

Before Example: we doesn’t know how to represent them as vectors. 🤔

Documents: ["gensim is awesome", "machine learning with gensim"]

After Example: With Doc2Vec, we represent entire documents as vectors! 📝

Document Vector: [0.345, -0.234, ...]

Challenge: 🌟 Try using the document vectors for classification or clustering tasks.

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12. Latent Semantic Indexing (LSI) Model (gensim.models.LsiModel) 🔍

we want to use LSI (Latent Semantic Indexing) to identify hidden relationships between terms and reduce the dimensionality of the data.

Example:

Imagine we have these documents:

1. "Machine learning is great"

2. "Artificial intelligence is the future"

3. "Machine learning is part of artificial intelligence"

4. "The future is bright with AI"

Step-by-step breakdown:

1. Texts: These are the documents we will process using LSI.

    pythonCopy codetexts = [
        ["machine", "learning", "is", "great"],
        ["artificial", "intelligence", "is", "the", "future"],
        ["machine", "learning", "is", "part", "of", "artificial", "intelligence"],
        ["the", "future", "is", "bright", "with", "ai"]
    ]
   

2. Create Dictionary and Corpus: We create a dictionary and a bag-of-words representation (corpus) of the documents.

    from gensim.corpora import Dictionary
    from gensim.models import LsiModel
   
    # Create dictionary
    dictionary = Dictionary(texts)
   
    # Create bag-of-words corpus
    corpus = [dictionary.doc2bow(text) for text in texts]
   

3. Train the LSI Model: We’ll train an LSI model to find latent topics in the documents by reducing the dimensionality.

    # Train the LSI model with 2 topics
    lsi = LsiModel(corpus, id2word=dictionary, num_topics=2)
   

4. View the Topics: After training, we can print the topics identified by LSI.

    # Print the topics
    print(lsi.print_topics())
   

Example Output:

After running the LSI model, you might see something like this:

[(0, "0.707*'learning' + 0.707*'machine'"), 
 (1, "0.577*'intelligence' + 0.577*'artificial' + 0.577*'future'")]

What the output means:

• Topic 0: This topic is mostly about machine learning because it heavily weights the words "learning" and "machine". So, the documents mentioning these terms are grouped together.

• Topic 1: This topic is about artificial intelligence and the future, as it includes terms like "intelligence", "artificial", and "future".

• The documents are simple and related, so you can see how LSI groups similar terms like "machine learning" and "artificial intelligence" into latent topics.

• LSI reduces the dimensionality (turning the many different words into a few meaningful topics), making it easier to understand the core themes within the documents.

Practical Use Case:

• Document Similarity: LSI is used to find hidden structures in documents and group related documents. For example, in search engines, it helps match documents with similar topics even if they don’t use the exact same keywords.

Challenge:

You can experiment by reducing the number of topics (e.g., set num_topics=1) and observe how LSI captures a more general pattern across the documents.

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13. HDP Model (gensim.models.HdpModel) 📈

Suppose we have a set of news articles and we want to discover the topics within them, but we don't know how many topics to expect beforehand. HDP automatically figures this out!

Step-by-Step Example:

1. Documents (Texts): These are our simple documents (pretending they are news articles).

    texts = [
        ["artificial", "intelligence", "future", "technology"],
        ["sports", "soccer", "goal", "team"],
        ["politics", "election", "government", "policy"],
        ["artificial", "intelligence", "machine", "learning"],
        ["soccer", "team", "win", "championship"]
    ]
   

   Here, we have a mix of topics:

   • Documents 1 and 4 are about artificial intelligence.

   • Documents 2 and 5 are about soccer.

   • Document 3 is about politics.

2. Create Dictionary and Corpus: Convert the documents into a bag-of-words representation (corpus) using a dictionary.

    from gensim.corpora import Dictionary
    from gensim.models import HdpModel
   
    # Create dictionary
    dictionary = Dictionary(texts)
   
    # Convert to bag-of-words corpus
    corpus = [dictionary.doc2bow(text) for text in texts]
   

3. Train the HDP Model: Use HDP to discover topics dynamically, without specifying the number of topics in advance.

    # Train the HDP model
    hdp = HdpModel(corpus, id2word=dictionary)
   

4. View the Discovered Topics: Print the topics that HDP discovered from the documents.

    # Print the discovered topics
    print(hdp.print_topics())
   

Sample Output:

HDP might automatically discover several topics from the documents, such as:

[(0, "0.3*'artificial' + 0.3*'intelligence' + 0.2*'machine' + 0.1*'learning'"),
 (1, "0.4*'soccer' + 0.3*'team' + 0.2*'goal' + 0.1*'win'"),
 (2, "0.5*'politics' + 0.3*'election' + 0.2*'government' + 0.1*'policy'")]

Explanation:

• Topic 0: The first topic contains words related to artificial intelligence ("artificial," "intelligence," "machine," "learning").

• Topic 1: The second topic relates to soccer ("soccer," "team," "goal," "win").

• Topic 2: The third topic captures politics ("politics," "election," "government," "policy").

Why This is Better:

• Dynamic Topic Discovery: Unlike LDA (Latent Dirichlet Allocation), where you need to specify the number of topics beforehand, HDP automatically figures out how many topics exist in the dataset. This is useful when you're not sure how many topics are present.

• No Predefined Number of Topics: HDP is great for exploring new datasets when you don’t know the structure of the topics in advance, like in news articles, customer reviews, or research papers.

Real-World Use Case:

• News Categorization: Imagine you’re analyzing thousands of news articles, but you don’t know how many distinct categories (topics) exist. HDP will automatically identify them, making it easier to group and label articles.

• Customer Feedback Analysis: For businesses analyzing customer feedback, HDP helps uncover the main themes (topics) without needing to guess how many topics (e.g., satisfaction, complaints, pricing issues) are present.

Challenge:

• Try applying HDP to a larger dataset (like a real news dataset or product reviews) to discover the hidden topics and see how it handles more complex data.

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14. LSI Similarity Matrix (gensim.similarities.MatrixSimilarity) 🔗

Suppose you have a collection of news articles about various topics like technology, sports, and politics. After transforming the documents using LSI, you want to measure how similar these articles are based on their topics.

Example with Step-by-Step Breakdown:

1. Documents (Texts): Let’s use some news-related topics to represent articles.

    texts = [
        ["technology", "ai", "innovation", "future", "tech"],
        ["soccer", "sports", "goal", "team", "win"],
        ["election", "government", "policy", "politics", "vote"],
        ["technology", "ai", "machine", "learning", "data"],
        ["sports", "soccer", "championship", "win", "tournament"]
    ]
   

   Here, we have:

   • Documents 1 and 4 are about technology.

   • Documents 2 and 5 are about sports.

   • Document 3 is about politics.

2. Create Dictionary and Corpus: We create a dictionary and a bag-of-words representation of the documents.

    from gensim.corpora import Dictionary
    from gensim.models import LsiModel
    from gensim.similarities import MatrixSimilarity
   
    # Create dictionary
    dictionary = Dictionary(texts)
   
    # Convert to bag-of-words corpus
    corpus = [dictionary.doc2bow(text) for text in texts]
   

3. Train the LSI Model: Apply LSI to reduce the dimensionality of the data into 2 latent topics.

    # Train LSI model with 2 topics
    lsi = LsiModel(corpus, id2word=dictionary, num_topics=2)
   
    # Transform corpus into LSI space
    lsi_corpus = lsi[corpus]
   

4. Compute the Similarity Matrix: Use MatrixSimilarity to compare the LSI-transformed documents and compute similarity scores between them.

    # Compute similarity matrix
    similarity_matrix = MatrixSimilarity(lsi_corpus)
   
    # Print the similarity matrix
    for i, similarities in enumerate(similarity_matrix):
        print(f"Document {i} similarities: {list(similarities)}")
   

Sample Output:

The similarity matrix might show something like this:

Document 0 similarities: [1.0, 0.12, 0.05, 0.95, 0.08]
Document 1 similarities: [0.12, 1.0, 0.07, 0.15, 0.92]
Document 2 similarities: [0.05, 0.07, 1.0, 0.04, 0.09]
Document 3 similarities: [0.95, 0.15, 0.04, 1.0, 0.12]
Document 4 similarities: [0.08, 0.92, 0.09, 0.12, 1.0]

• Document 0 (technology) is most similar to Document 3 (another technology-related document) with a similarity score of 0.95.

• Document 1 (sports) is highly similar to Document 4 (another sports document) with a score of 0.92.

• Document 2 (politics) has very low similarity with the technology and sports documents, as expected.

Real-World Use Case:

• Document Retrieval: Suppose you’re building a document retrieval system where users search for documents similar to an existing one. The LSI model reduces the data complexity, and the similarity matrix helps efficiently find documents that are related in terms of latent topics (like news articles, research papers, etc.).

• Recommendation Systems: You can use this method to recommend articles or reports based on their similarity to the user’s reading history.

In summary, MatrixSimilarity lets you measure how similar documents are after reducing their dimensions with LSI, which is useful in cases like document retrieval or recommendation systems..

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15. Phrases Model (gensim.models.Phrases) 🗣️

Boilerplate Code:

from gensim.models import Phrases

Use Case: Detect common phrases or bigrams (e.g., "New York") in a corpus and convert them into single tokens. 🗣️

Goal: Identify frequently co-occurring word pairs in text and treat them as one unit. 🎯

Sample Code:

# Example text
sentences = [["new", "york", "city"], ["new", "york", "times"]]

# Train Phrases model
phrases = Phrases(sentences, min_count=1, threshold=1)

# Detect phrases
bigram = phrases[sentences]
print(list(bigram))

Before Example: doesn’t know how to detect frequently occurring word pairs. 🤔

Sentences: [["new", "york", "city"], ["new", "york", "times"]]

After Example: With Phrases, we detect phrases like "New York"! 🗣️

Phrases: [('new_york', 'city'), ('new_york', 'times')]

Challenge: 🌟 Try detecting trigrams (three-word phrases) and apply this to a larger corpus.

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16. Term Frequency (gensim.models.TfidfModel) 📊

Boilerplate Code:

from gensim.models import TfidfModel

Use Case: Calculate term frequency-inverse document frequency (TF-IDF) to find important terms in a corpus. 📊

Goal: Identify the most significant words based on how often they appear across documents. 🎯

Sample Code:

# Example text
texts = [["hello", "world"], ["world", "of", "gensim"]]

# Create dictionary and corpus
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

# Train TF-IDF model
tfidf = TfidfModel(corpus)
tfidf_corpus = tfidf[corpus]
print(list(tfidf_corpus))

Before Example: has word counts but doesn’t know which words are more important in the context of the corpus. 🤔

Texts: [["hello", "world"], ["world", "of", "gensim"]]

After Example: With TF-IDF, we identified the most important words in the corpus! 📊

TF-IDF: Scores for each word in the documents.

Challenge: 🌟 Apply TF-IDF to a news article and find the most significant words.

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17. Random Projections (gensim.models.RpModel) 🎯

When to Use Random Projections (RP):

1. High Dimensionality:

   • If your dataset has many features (e.g., thousands of unique words in text data), it’s high-dimensional.

   • Recognize this if the number of features (columns) is much larger than the number of samples (rows).

2. Slow Processing:

   • High-dimensional data takes longer to process. If your computations are slow, RP can speed it up by reducing dimensions.

3. Overfitting:

   • Models trained on high-dimensional data often overfit because they learn from noise rather than real patterns.

   • Signs of overfitting include good performance on training data but poor performance on new data.

4. Sparse Data:

   • High-dimensional data tends to be sparse, meaning many features rarely occur (e.g., rare words in text).

   • RP reduces this sparsity, focusing on important features.

5. Curse of Dimensionality:

   • In high dimensions, distances between points can become less meaningful, affecting algorithms like clustering.

   • If distance-based methods are ineffective, RP can help.

Why Use RP:

• Efficiency: RP reduces dimensions quickly without much computation.

• Preserves Relationships: It retains the relative distances between data points, maintaining structure while simplifying the data.

Example Scenarios:

• Text Data: With large text corpora, RP can reduce thousands of unique words into fewer dimensions while keeping document relationships intact.

• Image Data: RP reduces the number of features (pixels) in images, making classification easier while retaining important structure.

How to Decide:

• Check if the number of features is much larger than the samples.

• If your model overfits or processing is slow, reducing dimensions with RP may help.

In summary, use Random Projections when dealing with high-dimensional, sparse, or slow-to-process data, and when you want to reduce dimensionality without losing important relationships between points!

Boilerplate Code:

from gensim.models import RpModel

Use Case: Use Random Projections (RP) to reduce the dimensionality of a corpus. 🎯

Goal: Reduce the dimensionality of high-dimensional datasets while preserving distances. 🎯

Sample Code:

# Example text
texts = [["hello", "world"], ["world", "of", "gensim"]]

# Create dictionary and corpus
dictionary = Dictionary(texts)
corpus = [

dictionary.doc2bow(text) for text in texts]

# Train Random Projections model
rp = RpModel(corpus, num_topics=2)
print(rp.print_topics())

Before Example: high-dimensional data, doesn’t know how to reduce it while preserving distances. 🤔

Texts: [["hello", "world"], ["world", "of", "gensim"]]

After Example: With RP, we reduce the dimensionality while maintaining important relationships! 🎯

Random Projections Topics: A reduced-dimensional representation of the data.

Challenge: 🌟 Apply random projections to a large corpus and evaluate how much dimensionality is reduced.

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18. Online Learning (gensim.models.LdaMulticore) 🔄

Boilerplate Code:

from gensim.models import LdaMulticore

Use Case: Use LDA with multicore processing for faster online topic modeling. 🔄

Goal: Train an LDA model on large datasets efficiently by processing documents in batches. 🎯

Sample Code:

# Example text
texts = [["hello", "world"], ["world", "of", "gensim"]]

# Create dictionary and corpus
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

# Train LDA model with multiple cores
lda = LdaMulticore(corpus, id2word=dictionary, num_topics=2, workers=2)
print(lda.print_topics())

Before Example: need to train a topic model on a large dataset but finds it too slow with a single core. 🤔

Documents: A large set of text documents

After Example: With LdaMulticore,we train a topic model faster using multiple CPU cores! 🔄

Topics: Thematic clusters discovered in the documents.

Challenge: 🌟 Try using online learning for large-scale text datasets to speed up processing.

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19. Word Mover’s Distance (gensim.models.WmdSimilarity) 🧠

Boilerplate Code:

from gensim.similarities import WmdSimilarity

Use Case: Calculate Word Mover’s Distance (WMD) to measure the similarity between two documents based on word embeddings. 🧠

Goal: Compare documents by calculating the minimum distance to "move" words from one document to another. 🎯

Sample Code:

# Example text
sentences = [["gensim", "is", "cool"], ["machine", "learning", "is", "great"]]

# Train Word2Vec model
model = Word2Vec(sentences, vector_size=10, min_count=1)

# Compute WMD
wmd_similarity = WmdSimilarity(sentences, model, num_best=1)
similarity = wmd_similarity[sentences[0]]
print(similarity)

Before Example: doesn’t know how to compare their similarity using word embeddings. 🤔

Documents: ["gensim is cool", "machine learning is great"]

After Example: With WMD, we can measures how similar documents are based on word embeddings! 🧠

WMD Similarity: A numerical score representing document similarity.

Challenge: 🌟 Try applying WMD to compare news articles from different categories.

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20. Sentence Embeddings (gensim.models.FastText) 📝

Boilerplate Code:

from gensim.models import FastText

Use Case: Use FastText to generate sentence embeddings, capturing the meaning of entire sentences. 📝

Goal: Represent sentences as vectors for classification or similarity tasks. 🎯

Sample Code:

# Example text
sentences = [["gensim", "is", "awesome"], ["machine", "learning", "with", "gensim"]]

# Train FastText model
model = FastText(sentences, vector_size=10, window=2, min_count=1)

# Get sentence embedding by averaging word vectors
sentence_vector = sum([model.wv[word] for word in sentences[0]]) / len(sentences[0])
print(sentence_vector)

Before Example: doesn’t know how to represent them as vectors. 🤔

Sentences: ["gensim is awesome", "machine learning with gensim"]

After Example: With FastText, we represent entire sentences as vectors! 📝

Sentence Embedding: [0.123, -0.456, ...]

Challenge: 🌟 Try using sentence embeddings for text classification or clustering tasks.

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