A monolingual and cross-lingual meta-embedding generation and evaluation framework
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Updated
Apr 29, 2022 - Python
A monolingual and cross-lingual meta-embedding generation and evaluation framework
Open Source Embeddings Optimisation and Eval Framework for RAG/LLM Applications. Documentations at https://docs.vectorboard.ai/introduction
Evaluate custom and HuggingFace text-to-image/zero-shot-image-classification models like CLIP, SigLIP, DFN5B, and EVA-CLIP. Metrics include Zero-shot accuracy, Linear Probe, Image retrieval, and KNN accuracy.
A scientific benchmark and comparison of the performance of sentiment analysis models in NLP on small to medium datasets
Interactive quality analysis for two-dimensional embeddings
Gold standard resource for evaluation of Danish word embedding models.
Graph (network) embeddings evaluation framework via classification, gram martix construction for links prediction
A framework for word embedding evaluation automation and visualization.
Repository for the Master Thesis "Encoding semantic information about skills in the domain of human resources" in the University of Koblenz-Landau in cooperation with talentsconnect AG.
Simple function that computes pairwaise cosine distance between several vectors at once, pytorch can only compute beween two vectors at a time, which is time consuming and inneficient when you have multiple vectors.
This is a repository for a Jupyter based tool to calculate Greedy Matching, Vector Extrema and Average Embedding evaluation metrics for generative AI chatbots
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