# gensim

This rule covers reproducible gensim model runs and text-corpus preparation for topic modeling. Use it when building or maintaining a Python NLP pipeline.

- rule · `type=rule` · Cursor · ★ 3580
- Author: sanjeed5
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/gensim.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/gensim

## What it does

The rule recommends configuring logging and setting a random seed for both `gensim` and `numpy`, then passing that seed through a model’s `random_state` parameter. The sample uses `42`; treat it as an illustration, not a required setting.

For text preparation, it pairs `gensim.utils.simple_preprocess` with `spaCy`: the former tokenizes and lowercases text, while the latter supports lemmatization and stop-word filtering. It also explains how to prune a dictionary with `filter_extremes`, where `no_below` is a document count and `no_above` is a fraction. For larger collections, the examples process documents as a stream and show `MmCorpus`. The `spaCy` example loads `en_core_web_sm`, so that model must be available for the shown `spacy.load` call to work.

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/gensim.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/gensim.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/gensim.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/gensim.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/gensim.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/gensim.mdc

## When to use it
- make model runs reproducible with a random seed
- tokenize and lemmatize text before modeling
- prune rare and overly common dictionary entries
- feed a large text collection to a model as a stream

## Example prompts
- Set up logging and a random seed for the `gensim` model in this Python script.
- Prepare this corpus with `simple_preprocess` and `spaCy`, then prune its dictionary with `filter_extremes`.
- Change the processing of `my_large_corpus.txt` so documents are streamed into the model.

## Source text · gensim.mdc · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: bbcb788b7f9647db.

BEGIN THIRD-PARTY FILE bbcb788b7f9647db
````text
---
description: This guide outlines definitive best practices for using the gensim library, focusing on reproducibility, efficient corpus construction, robust model training, and maintainable code for NLP topic modeling tasks.
globs: **/*.py
---
# gensim Best Practices

This document is your definitive guide for using `gensim` effectively and correctly within our team. We prioritize reproducibility, performance, and maintainability. Follow these rules to ensure consistent, high-quality NLP pipelines.

## 1. Ensure Reproducibility

Always configure logging and set a random seed at the entry point of any script using `gensim` models. This is non-negotiable for debugging and consistent results.

### ❌ BAD: Unpredictable Runs

```python
import gensim
from gensim import models, corpora

# No logging, no seed
# ... model training ...
lda_model = models.LdaModel(corpus, num_topics=10)
```

### ✅ GOOD: Reproducible and Observable Runs

```python
import logging
import numpy as np
import gensim
from gensim import models, corpora
from gensim.utils import randseed

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

# 2. Set a global random seed for gensim and numpy
# Gensim's randseed sets numpy's seed internally.
randseed = 42
np.random.seed(randseed)

# ... rest of your script ...
# Ensure any model that takes a random_state or seed parameter uses it
lda_model = models.LdaModel(corpus, num_topics=10, random_state=randseed)
```

## 2. Construct Clean and Efficient Corpora

A well-prepared corpus is fundamental to effective topic modeling. Prioritize memory efficiency and intelligent vocabulary pruning.

### 2.1. Preprocessing with `gensim.utils.simple_preprocess` and `spaCy`

Combine `gensim`'s simple preprocessing with `spaCy` for robust tokenization and lemmatization. `simple_preprocess` handles basic tokenization and lowercasing efficiently.

### ❌ BAD: Manual, Inconsistent Preprocessing

```python
import re

documents = ["This is a document.", "Another document here."]
stoplist = set('is a here'.split())

texts = []
for doc in documents:
    # Manual lowercasing, splitting, and stopword removal
    tokens = [word for word in re.findall(r'\b\w+\b', doc.lower()) if word not in stoplist]
    texts.append(tokens)
```

### ✅ GOOD: `gensim.utils.simple_preprocess` + `spaCy` for Production

```python
import spacy
from gensim.utils import simple_preprocess

# Load spaCy model once
nlp = spacy.load("en_core_web_sm", disable=['parser', 'ner'])

def preprocess_document(text: str) -> list[str]:
    """
    Tokenizes, lowercases, removes stopwords, and lemmatizes text using spaCy.
    """
    # Use gensim's simple_preprocess for initial tokenization and lowercasing
    tokens = simple_preprocess(text, deacc=True) # deacc=True removes accents

    # Process with spaCy for lemmatization and advanced stopword removal
    doc = nlp(" ".join(tokens))
    lemmas = [
        token.lemma_ for token in doc
        if not token.is_stop and not token.is_punct and not token.is_space
    ]
    return lemmas

documents = ["Human machine interface for lab abc computer applications",
             "A survey of user opinion of computer system response time"]

processed_texts = [preprocess_document(doc) for doc in documents]
```

### 2.2. Vocabulary Pruning with `filter_extremes`

Always prune your dictionary using `filter_extremes` to remove very rare and very common tokens. This significantly improves model quality and reduces noise.

**Crucial**: `no_below` is an absolute count (int), `no_above` is a fraction (float).

### ❌ BAD: Unfiltered Dictionary or Misunderstood Parameters

```python
from gensim import corpora

# Dictionary with all tokens, including noise
dictionary = corpora.Dictionary(processed_texts)

# Incorrectly using no_below as a fraction or no_above as an absolute count
# This will lead to unexpected vocabulary sizes.
````
END THIRD-PARTY FILE bbcb788b7f9647db

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