Semhash: Multimodal Semantic Deduplication for Text and Image Cleaning

MinishLab released Semhash in January 2026 to provide a framework for fast multimodal semantic deduplication and filtering. The project utilizes model2vec and vicinity-based hashing to identify near-duplicate records across text and image datasets.

Use Cases

• Deduplication of text datasets using model2vec semantic embeddings
• Filtering redundant image data in computer vision pipelines via vicinity hashing
• Preprocessing large-scale multimodal corpora to reduce training compute requirements

Strengths

• MIT license allows for permissive commercial and research application
• Supports multimodal deduplication across both text and image formats
• Utilizes model2vec for high-speed embedding generation

Limitations

• Lack of public documentation regarding specific row counts or dataset scale
• Potential for false positives inherent in semantic hashing algorithms

Provenance

Source

MinishLab GitHub repository

Collection Method

algorithmic generation

Freshness

Last updated January 20, 2026.