Model: research-backup/roberta-large-conceptnet-average-prompt-e-nce
Author: research-backup
Pipeline: feature-extraction
Library: transformers
Tags: transformers, pytorch, roberta, feature-extraction, dataset:relbert/conceptnet_high_confidence, model-index, text-embeddings-inference, endpoints_compatible, region:us
Card metadata: datasets: relbert/conceptnet_high_confidence
model-index: name: relbert/roberta-large-conceptnet-average-prompt-e-nce
results: task: name: Relation Mapping
type: sorting-task
dataset: name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics: name: Accuracy
type: accuracy
value: 0.8862103174603174
verified: no, task: name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset: name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics: name: Accuracy
type: accuracy
value: 0.5
verified: no, task: name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset: name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics: name: Accuracy
type: accuracy
value: 0.49258160237388726
verified: no, task: name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset: name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics: name: Accuracy
type: accuracy
value: 0.7443023902167871
verified: no, task: name: Analogy Questions (Google)
type: multiple-choice-qa
dataset: name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics: name: Accuracy
type: accuracy
value: 0.886
verified: no, task: name: Analogy Questions (U2)
type: multiple-choice-qa
dataset: name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics: name: Accuracy
type: accuracy
value: 0.5526315789473685
verified: no, task: name: Analogy Questions (U4)
type: multiple-choice-qa
dataset: name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics: name: Accuracy
type: accuracy
value: 0.5439814814814815
verified: no, task: name: Lexical Relation Classification (BLESS)
type: classification
dataset: name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics: name: F1
type: f1
value: 0.9085430164230828
verified: no, name: F1 (macro)
type: f1_macro
value: 0.9007282568605484
verified: no, task: name: Lexical Relation Classification (CogALexV)
type: classification
dataset: name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics: name: F1
type: f1
value: 0.8380281690140845
verified: no, name: F1 (macro)
type: f1_macro
value: 0.656362704638303
verified: no, task: name: Lexical Relation Classification (EVALution)
type: classification
dataset: name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics: name: F1
type: f1
value: 0.6657638136511376
verified: no, name: F1 (macro)
type: f1_macro
value: 0.6498144246049421
verified: no, task: name: Lexical Relation Classification (K&H+N)
type: classification
dataset: name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics: name: F1
type: f1
value: 0.9565277874382695
verified: no, name: F1 (macro)
type: f1_macro
value: 0.8746667490411619
verified: no, task: name: Lexical Relation Classification (ROOT09)
type: classification
dataset: name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics: name: F1
type: f1
value: 0.8896897524287057
verified: no, name: F1 (macro)
type: f1_macro
value: 0.8862724322889753
verified: no
Likes: 0 | All-time downloads: 300
Created: 2022-07-30 19:48:25