This repository contains hikka-forge-anime2vec, a sophisticated semantic vector space model for anime, created by Lorg0n. This repository is also a directly installable Python package, allowing you to integrate powerful anime vectorization capabilities into your own projects with ease.

Model Details

Model Version: v13
Architecture: A multi-input neural network with separate processing streams for text, genres, studios, and other categorical/numerical features. It uses attention mechanisms to weigh the importance of different text fields and genres, creating a rich, context-aware representation.
Embedding Size: The model outputs a 512-dimensional vector for each anime.

Data & Training

The model's understanding of conceptual relationships comes from a unique blend of real-world and synthetic data.

Base Data: The foundational metadata (titles, genres, studios, etc.) was sourced from the hikka.io database. This provided a solid, real-world grounding for the model.
Synthetic Data Augmentation: To teach the model complex conceptual and arithmetic relationships (e.g., "Anime A" - "Anime X" + "Anime Y" = "Anime B"), the training set was heavily augmented with data generated by a Large Language Model (LLM). This synthetic data was crucial for enabling the model's advanced vector arithmetic capabilities.
Training Procedure: The model was trained using a combination of three loss functions:
1. Triplet Loss: Based on user recommendations to learn similarity.
2. Cosine Similarity Loss: For the LLM-generated vector arithmetic examples.
3. Diversity Loss: To ensure a well-distributed embedding space and prevent model collapse.

Installation

Install the library directly from this Hugging Face repository:

pip install git+https://huggingface.co/Lorg0n/hikka-forge-anime2vec

Usage

The library provides a simple, high-level Anime2Vec class that handles all the complexity of downloading models, preprocessing data, and generating embeddings.

python

from hikka_forge import Anime2Vec

# Initialize the model. All required artifacts will be downloaded
# and cached automatically on the first run.
anime2vec = Anime2Vec()

# 1. Prepare data for a target anime.
# The `encode` method expects a dictionary with specific keys.
frieren_data = {
    "ua_title": "Фрірен, що проводжає в останню путь",
    "en_title": "Frieren: Beyond Journey's End",
    "original_title": "Sousou no Frieren",
    "ua_description": "Ельфійка-чарівниця Фрірен перемогла Короля Демонів...",
    "en_description": "The elf mage Frieren and her courageous fellow adventurers...",
    "alternate_names": ["Sousou no Frieren"],
    "genres": ["Adventure", "Drama", "Fantasy"],
    "studio": "Madhouse",
    "type": "TV",
    "numerical_features": [8.9, 500000, 2023, 28, 24, 100] # Example data
}

# 2. Generate the 512-dimensional vector representation
frieren_vector = anime2vec.encode(frieren_data)

print(f"Resulting vector for '{frieren_data['en_title']}' has shape: {frieren_vector.shape}")
# Now you can use this vector for similarity search, clustering, or vector arithmetic.

Vector Arithmetic Example

The true power of this model lies in its ability to perform conceptual arithmetic, a skill honed by the LLM-generated training data.

python

# This is a conceptual example. You would need to pre-compute vectors
# for all anime in your database to perform the final similarity search.

# Get vectors for two anime
aot_vector = anime2vec.encode(attack_on_titan_data)
code_geass_vector = anime2vec.encode(code_geass_data)

# Find the semantic average between them
# This should represent concepts like "military drama with sci-fi/mecha elements"
average_vector = (aot_vector + code_geass_vector) / 2.0

# You can now use `average_vector` to find anime that fit this
# hybrid description in your own vector database.
# Expected results: Aldnoah.Zero, Mobile Suit Gundam: Iron-Blooded Orphans, etc.

Limitations and Bias

Data Bias: The model's understanding is shaped by its training data. The base data from hikka.io and the synthetic data from the LLM both have inherent biases which will be reflected in the model's embeddings.
Textual Keyword Collision: The model relies heavily on text descriptions. Sometimes, an anime from a completely different genre might use specific keywords (e.g., "political intrigue," "strategy") in its synopsis, causing it to appear anomalously in search results for serious thrillers.
Encoder Vocabulary: While the categorical encoders (genre, studio) were trained on a comprehensive list, they are not exhaustive. A brand-new studio or a very niche genre tag not present in the original database will be treated as 'UNKNOWN'.

Citation

If you use this model in your work, please consider citing it:

bibtex

@misc{lorg0n2025anime2vec,
  author       = {Lorg0n},
  title        = {{hikka-forge-anime2vec: A Semantic Vector Space Model for Anime}},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Lorg0n/hikka-forge-anime2vec}},
  note         = {Hugging Face repository}
}