Wolfram Computation Meets Knowledge

Task Type

Feature Extraction

15 items

Clinical Concept Embeddings Trained on Health Insurance Claims, Clinical Narratives from Stanford and PubMed Journal Articles

Represent a clinical concept as a vector

ConceptNet Numberbatch Word Vectors V17.06

Represent words as vectors

ConceptNet Numberbatch Word Vectors V17.06 (Raw Model)

Represent words as vectors

ELMo Contextual Word Representations Trained on 1B Word Benchmark

Represent words as contextual word-embedding vectors

GloVe 100-Dimensional Word Vectors Trained on Tweets

Represent words as vectors

GloVe 100-Dimensional Word Vectors Trained on Wikipedia and Gigaword 5 Data

Represent words as vectors

GloVe 200-Dimensional Word Vectors Trained on Tweets

Represent words as vectors

GloVe 25-Dimensional Word Vectors Trained on Tweets

Represent words as vectors

GloVe 300-Dimensional Word Vectors Trained on Common Crawl 42B

Represent words as vectors

GloVe 300-Dimensional Word Vectors Trained on Common Crawl 840B

Represent words as vectors

GloVe 300-Dimensional Word Vectors Trained on Wikipedia and Gigaword 5 Data

Represent words as vectors

GloVe 50-Dimensional Word Vectors Trained on Tweets

Represent words as vectors

GloVe 50-Dimensional Word Vectors Trained on Wikipedia and Gigaword 5 Data

Represent words as vectors

OpenFace Face Recognition Net Trained on CASIA-WebFace and FaceScrub Data

Represent a facial image as a vector

ResNet-101 Trained on Augmented CASIA-WebFace Data

Represent a facial image as a vector