Embeddings Evaluation
9 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Embeddings Evaluation
CANNOT
Names pairs dataset
Most implemented papers
A Survey of Word Embeddings Evaluation Methods
Word embeddings are real-valued word representations able to capture lexical semantics and trained on natural language corpora.
Expert Concept-Modeling Ground Truth Construction for Word Embeddings Evaluation in Concept-Focused Domains
We present a novel, domain expert-controlled, replicable procedure for the construction of concept-modeling ground truths with the aim of evaluating the application of word embeddings.
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets
collection-space-navigator/csn
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We introduce the Collection Space Navigator (CSN), a browser-based visualization tool to explore, research, and curate large collections of visual digital artifacts that are associated with multidimensional data, such as vector embeddings or tables of metadata.
This is not correct! Negation-aware Evaluation of Language Generation Systems
MiriUll/negation_aware_evaluation
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Based on this dataset, we fine-tuned a sentence transformer and an evaluation metric to improve their negation sensitivity.
The Limitations of Cross-language Word Embeddings Evaluation
The aim of this work is to explore the possible limitations of existing methods of cross-language word embeddings evaluation, addressing the lack of correlation between intrinsic and extrinsic cross-language evaluation methods.
SART - Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings Evaluation
We evaluate state-of-the-art word embedding models for two languages using our proposed datasets for Tatar and the original datasets for English and report our findings on performance comparison.
Embeddings Evaluation Using a Novel Measure of Semantic Similarity
Then, we train several embedding models on a text corpus and select the best model, that is, the model that maximizes the correlation between the HSS and the cosine similarity of the pair of words that are in both the taxonomy and the corpus.
TWLV-I: Analysis and Insights from Holistic Evaluation on Video Foundation Models
twelvelabs-io/video-embeddings-evaluation-framework
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In this work, we discuss evaluating video foundation models in a fair and robust manner.
Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models
Vision-Language Models (VLMs) learn joint representations by mapping images and text into a shared latent space.
