Gensim

Warnings

• breaking Gensim 4.0 introduced significant breaking API changes from 3.x. Key attributes and methods were renamed or moved. For example, `model.vocab` was replaced by `model.wv.key_to_index` or `model.wv.index_to_key`, and many vector-related methods like `most_similar()` moved from the model object to `model.wv` (KeyedVectors). The `size` parameter was renamed to `vector_size`, and `iter` to `epochs`.
  Fix: Consult the official Gensim 3.x to 4.x migration guide on GitHub. Update attribute access (e.g., `model.wv.index_to_key` instead of `model.wv.vocab.keys()`) and method calls (e.g., `model.wv.most_similar()` instead of `model.most_similar()`). Adjust parameter names for model initialization (e.g., `vector_size` instead of `size`, `epochs` instead of `iter`).
• breaking Gensim 4.0.0 and later versions officially dropped support for Python 2.7. Users requiring Python 2.7 must use Gensim 3.8.3 or an earlier version.
  Fix: Upgrade to Python 3.9+ (Gensim 4.4.0 requires >=3.9). If Python 2.7 is strictly necessary, pin Gensim to version 3.8.3: `pip install gensim==3.8.3`.
• gotcha While Gensim is designed for memory-independent processing, training models like LDA or Word2Vec on extremely large corpora can still lead to high memory consumption, especially if not preprocessed efficiently or if the entire corpus is loaded into RAM.
  Fix: Employ memory-efficient practices: filter stopwords, remove rare words, and use Gensim's `MmCorpus` or custom iterators that stream data from disk. Reduce dictionary size by filtering `no_below` and `no_above` extremes. Consider using `LdaMulticore` for LDA models with multiple cores.
• gotcha Ensure your NumPy version is compatible with your Gensim installation. While Gensim 4.4.0 officially added support for NumPy 2.0, older Gensim 4.x releases (e.g., 4.0.1) had specific compatibility issues with NumPy binary packages on Windows.
  Fix: Always install the latest Gensim version (`pip install --upgrade gensim`) which includes the most recent compatibility fixes. If encountering issues, try pinning NumPy to a known compatible version or ensuring your BLAS libraries are correctly configured if building NumPy from source.

Install

• pip install gensim Install latest stable version

Imports

• Word2Vec

  from gensim.models import Word2Vec

• Doc2Vec

  from gensim.models import Doc2Vec

• LdaModel

  from gensim.models import LdaModel

• Dictionary

  from gensim.corpora import Dictionary

• simple_preprocess

  from gensim.utils import simple_preprocess

• model.wv.index_to_key

  model.wv.index_to_key

  In Gensim 4.0+, the `vocab` attribute was removed from `KeyedVectors`. Use `model.wv.index_to_key` for a list of words or `model.wv.key_to_index` for a word-to-index mapping.
• model.wv.most_similar()

  model.wv.most_similar(word)

  Many vector-related methods (like `most_similar`, `wmdistance`, `doesnt_match`, `similarity`) were moved from the top-level model object to the `KeyedVectors` object (`.wv`) in Gensim 4.0+.

Quickstart

This quickstart demonstrates how to preprocess text, create a dictionary, train a Word2Vec model, and then save, load, and use the model to find similar words. It highlights best practices for corpus preparation and basic model interaction.

import logging
from gensim.models import Word2Vec
from gensim.corpora import Dictionary
from gensim.utils import simple_preprocess

logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)

# Sample corpus
corpus = [
    "Human machine interface for lab abc computer applications",
    "A survey of user opinion of computer system response time",
    "The EPS user interface management system",
    "System and human system engineering testing of EPS",
    "Relation of user perceived response time to error measurement",
    "The generation of random binary non-linear sequences of great length",
    "The interaction of user and computer in an easy way",
    "Doctors use computers for medical diagnosis",
    "A quick brown fox jumps over the lazy dog"
]

# Preprocess the corpus: tokenize, lowercase, and filter
tokenized_corpus = [simple_preprocess(doc) for doc in corpus]

# Create a dictionary from the tokenized corpus
dictionary = Dictionary(tokenized_corpus)

# Filter out words that appear in less than 2 documents or more than 50% of documents
dictionary.filter_extremes(no_below=2, no_above=0.5)

# Prepare corpus for Word2Vec training (list of lists of words)
# Word2Vec expects an iterable of sentences, where each sentence is a list of words.

# Train a Word2Vec model
model = Word2Vec(
    sentences=tokenized_corpus,  # Your list of tokenized sentences
    vector_size=100,             # Dimensionality of the word vectors
    window=5,                    # Maximum distance between the current and predicted word within a sentence
    min_count=1,                 # Ignores all words with total frequency lower than this
    workers=4,                   # Use 4 worker threads to train the model
    epochs=10                    # Number of iterations (epochs) over the corpus
)

# Save the model
model.save("word2vec_model.model")

# Load the model
loaded_model = Word2Vec.load("word2vec_model.model")

# Find the most similar words to 'computer'
if 'computer' in loaded_model.wv:
    similar_words = loaded_model.wv.most_similar('computer')
    print("\nWords similar to 'computer':")
    for word, score in similar_words:
        print(f"{word}: {score:.4f}")
else:
    print("\n'computer' not in vocabulary.")