Grimmerie 0.1.4

Functions for Prototyping, QOL and Sanity checking

Grimmerie

A spellbook for Python.

Grimmerie is a collection of high-level utilities (“spells”) designed for rapid prototyping, sanity checking, and reducing friction in experimentation.

Each spell performs a non-trivial amount of work under the hood.
They are intentionally designed to trade fine-grained control for speed, clarity, and momentum.

Use them when you want to move fast.
Understand them before you rely on them.

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Installation

pip install grimmerie

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The Idea

Instead of wiring together pipelines every time, Grimmerie gives you:

• One function call
• Sensible defaults
• Heavy lifting handled internally

Example philosophy:

embeddings = specterize(papers)

Behind this single call:

• Model loading
• Tokenization
• Batching
• Device handling
• Adapter loading
• Output formatting

All handled for you.

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Spells

specterize

Generate SPECTER2 embeddings from text or paper-like inputs.

from grimmerie import specterize

papers = [
    {'abstract': 'We introduce a new language representation model called BERT'},
    {'abstract': 'The dominant sequence transduction models are based on neural networks'},
]

embeddings = specterize(papers, return_type='numpy')

tfidfize

Generate TF-IDF representations from text with optional preprocessing.

from grimmerie import tfidfize

docs = [
    {'abstract': 'We introduce a new language representation model called BERT'},
    {'abstract': 'The dominant sequence transduction models are based on neural networks'},
]

X = tfidfize(docs, return_type='array')

Or optional parameters: lemmatize: bool = False, spacy_model: str = 'en_core_web_sm', batch_size: int = 2000, n_process: int = 1, progress_interval: int | None = None, min_df: int | float = 1, max_df: int | float = 1.0, stop_words: str | list[str] | None = 'english', ngram_range: tuple[int, int] = (1, 1), lowercase: bool = True, max_features: int | None = None, norm: Literal['l1', 'l2'] | None = 'l2', use_idf: bool = True, smooth_idf: bool = True, sublinear_tf: bool = False, return_type: Literal['sparse', 'array', 'list', 'frame'] = 'sparse', return_vectorizer: bool = False, vectorizer: TfidfVectorizer | None = None,

API

specterize(input_data, return_type='list', max_length=512)

• input_data: str, dict, list, or iterable
• return_type: "list", "numpy", "tensor"
• max_length: tokenizer truncation length (default 512)

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Design Principles

1. Abstraction over configuration

You should not need to think about setup for common workflows.

2. Strong defaults

Spells are opinionated. They are built to “just work” for most cases.

3. Hidden complexity

A spell may do significantly more than it appears.

4. Use with awareness

Because complexity is hidden, you should understand what a spell does before using it in critical systems.

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When to Use Grimmerie

• Rapid experimentation
• Prototyping ML/NLP pipelines
• Sanity checking ideas
• Building quick demos

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When Not to Use It

• When you need full control over every step
• When reproducibility requires explicit pipelines
• When debugging low-level behavior

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Notes

• First call may be slower due to model downloads
• Models are cached locally after first use
• Subsequent calls reuse loaded resources within the same process

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Direction

Grimmerie will expand into a broader system of spells for:

• Vectorization
• Dimensionality reduction
• Visualization
• Data inspection
• ML prototyping utilities

Each designed to compress multi-step workflows into a single, intentional call.