facebookresearch/StarspaceArchived
Starspace
Starspace is a vector embedding framework designed for training high-dimensional representations of text and images. It functions as a machine learning system for neural ranking, text classification, and knowledge graph embedding, mapping different object types into a shared numerical space to facilitate retrieval and prediction tasks.
The system includes specialized tools for knowledge graph completion and link prediction by representing entities and their relationships within a multi-relational vector space. It further provides capabilities for semantic content recommendation and large-scale text classification by mapping inputs to target labels or candidate items.
The framework covers broad capability areas including similarity-based entity ranking, vector embedding extraction from documents or n-grams, and the use of random-walk based training. To manage large datasets, it incorporates disk-based compressed data loading and negative sampling optimization.
Features
- Vector Embeddings - Provides a framework for learning high-dimensional vector representations of text and images.
- Distance-Based Ranking - Ranks candidates by calculating the geometric proximity between query and target entity vectors.
- Neural Ranking - Calculates similarity between query objects and candidates using neural models to provide ranked results.
- Relational Graph Embeddings - Represents entities and their various relationship types as vectors for link prediction in multi-relational spaces.
- Knowledge Graph Embeddings - Implements specialized tools for learning representations of entities and relations in knowledge graphs.
- Knowledge Graph Completion - Predicts missing links and relationships between entities by mapping them into a multi-relational vector space.
- Link Prediction - Analyzes entities and relations to identify missing links and complete triples within multi-relational graphs.
- Vector Similarity Search - Implements vector similarity search to retrieve the most relevant entities based on distance metrics.
- AI-Based Relevance Ranking - Uses AI-based relevance ranking to calculate distance between query objects and candidates for retrieval.
- Vector Space Models - Implements vector space models to map diverse object types into a shared numerical space for retrieval and classification.
- Text Document Classification - Provides large-scale text document classification by mapping inputs to target labels in an embedding space.
- Recommendation Engines - Ships a recommendation engine that predicts and ranks items using embedding-based similarity.
- Negative Sampling Techniques - Employs negative sampling techniques to reduce computational load by updating only a subset of weights during training.
- Multimodal Classification - Assigns labels to both text and image inputs by mapping them into a common embedding space.
- Random Walk Analysis - Uses random-walk based training to capture structural relationships between entities within a graph.
- Content Recommendations - Suggests relevant content by analyzing the embedding similarity between user interactions and candidates.
- Embedding Algorithms - General-purpose embedding framework for various data types.
- Machine Learning Frameworks - General-purpose tool for learning embeddings for various tasks.
- Natural Language Processing - General-purpose embedding tool for text and other data.
- C++ NLP Libraries - Library for creating embeddings for various text classification tasks.
Star history
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