shibing624/text2vec
Text2vec
text2vec este un toolkit de vectorizare a textului și un framework de similaritate semantică utilizat pentru a converti cuvinte și propoziții în vectori numerici. Oferă seturi de instrumente integrate pentru generarea de embedding-uri, calcularea proximității semantice și implementarea căutării lexicale și semantice.
Proiectul include un pipeline de fine-tuning al modelelor pentru optimizarea modelelor de embedding și potrivire folosind seturi de date supervizate sau nesupervizate. Se distinge, de asemenea, prin furnizarea unui API de embedding de text care permite modelelor de vectorizare să fie implementate ca servicii de rețea prin protocoale gRPC sau HTTP.
Framework-ul acoperă o gamă largă de capabilități, inclusiv analiza semantică, codificarea neuronală a textului și regăsirea prin căutare vectorială. Suportă atât embedding-uri vectoriale dense, cât și algoritmi de suprapunere a termenilor precum BM25 pentru a determina similaritatea și a identifica termeni înrudiți.
Optimizările suplimentare de performanță includ distilarea modelelor, reducerea dimensionalității bazată pe PCA și inferența distribuită multi-GPU pentru seturi de date la scară largă.
Features
- Semantic Similarity Calculation - Calculates conceptual similarity between text segments using cosine similarity and embedding vectors.
- Text Embeddings - Converts text into dense numerical vectors using transformer-based models to represent semantic meaning.
- Text Vectorizations - Transforms words and sentences into numerical vectors to represent semantic meaning for ML tasks.
- Text Vectorization Tools - Provides a toolkit for converting text into vectors using models like Word2Vec and Sentence-BERT.
- Hybrid Semantic Search Tools - Provides a system for retrieving documents using both BM25 lexical overlap and dense vector embeddings.
- Vector Similarity Search - Implements vector similarity search using cosine distance to measure semantic closeness between text embeddings.
- Model Fine-Tuning - Provides a dedicated pipeline for optimizing embedding and matching models using supervised or unsupervised datasets.
- Transformer Text Encoders - Uses transformer-based encoders to generate dense numerical vectors that represent the conceptual meaning of text.
- Semantic Search Engines - Implements a semantic search engine that retrieves relevant documents based on conceptual query meaning.
- Embedding Model Fine-Tuning - Implements processes for fine-tuning embedding models to optimize vector representations for specific domains.
- Model Distillation - Implements model distillation to create smaller, faster versions of embedding models without significant accuracy loss.
- Cross-Encoders - Provides cross-encoders that process text pairs simultaneously for high-precision semantic similarity scoring.
- Model-as-a-Service Platforms - Packages vectorization models into scalable inference services as a Model-as-a-Service implementation.
- Contrastive Fine-Tuning - Provides a pipeline for supervised contrastive fine-tuning to improve domain-specific embedding accuracy.
- Semantic Vector Search - Retrieves the most relevant documents from a dataset based on the mathematical distance between query and document embeddings.
- Inference Service Hosting - Wraps vectorization functions into high-performance API services for cloud-native deployment.
- Model Inference APIs - Exposes text vectorization models as high-performance gRPC and HTTP network services.
- BM25+ Scoring - Supports lexical-overlap scoring using the BM25 algorithm for non-semantic document retrieval.
