Model: research-backup/roberta-large-semeval2012-v2-average-no-mask-prompt-c-nce
Author: research-backup
Pipeline: feature-extraction
Library: transformers
Tags: transformers, pytorch, roberta, feature-extraction, dataset:relbert/semeval2012_relational_similarity_v2, model-index, text-embeddings-inference, endpoints_compatible, region:us
Card metadata: datasets: relbert/semeval2012_relational_similarity_v2
model-index: name: relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-c-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: 0.8667460317460317
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: 0.6283422459893048
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: 0.6320474777448071
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: 0.7726514730405781
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: 0.92
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: 0.631578947368421
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: 0.6365740740740741
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: 0.9218020189844809
verified: no, name: F1 (macro)
type: f1_macro
value: 0.9177802775241372
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: 0.8694835680751174
verified: no, name: F1 (macro)
type: f1_macro
value: 0.7143050763634161
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: 0.6852654387865655
verified: no, name: F1 (macro)
type: f1_macro
value: 0.6719168721915184
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: 0.9568755651387633
verified: no, name: F1 (macro)
type: f1_macro
value: 0.8813368476884477
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: 0.911939830774052
verified: no, name: F1 (macro)
type: f1_macro
value: 0.9100147353897433
verified: no
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Created: 2022-08-20 04:15:11