Embed English v3: Complete Benchmark Performance Guide

cohere embed-english-v3 2023-11-01 CC-BY-NC-4.0

In-depth analysis of Embed English v3 performance on MMLU, HumanEval, GSM8K, MATH, BBH benchmarks, including pricing and use cases.

Key Specifications

Vendorcohere
Versionembed-english-v3
Release Date2023-11-01
Context Window512 tokens
Input Modalitiestext
Output Modalities—
LicenseCC-BY-NC-4.0
Documentationhttps://docs.cohere.com/docs

Benchmark Performance

BenchmarkScoreUnitEvaluated AtNotesSource
MMLU0%2023-11-01embed model - not applicableview
HUMANEVAL0pass@12023-11-01embed model - not applicableview
GSM8K0%2023-11-01embed model - not applicableview
MATH0%2023-11-01embed model - not applicableview
BBH0%2023-11-01embed model - not applicableview
GPQA0%2023-11-01embed model - not applicableview
IFEVAL0%2023-11-01embed model - not applicableview
ARC0%2023-11-01embed model - not applicableview
MUSR0%2023-11-01embed model - not applicableview
WINOGRANDE0%2023-11-01embed model - not applicableview

Pricing

TierPriceCurrency
Input$0.1 / MtokUSD
Output$0.1 / MtokUSD
Cache Read$0 / MtokUSD
Cache Write$0 / MtokUSD

Source: https://cohere.com/pricing · as of 2023-11-01

Compliance

  • Data Residency: US
  • SOC2: ✓
  • HIPAA: ✗
  • GDPR: ✓
  • ISO 27001: ✓

Embed English v3

모델 개요

Cohere Embed English v3 英文嵌入模型, 512 token 输入, 为检索/分类/聚类优化, 英文语义搜索领先。

핵심 사양

공급업체버전출시일컨텍스트 창입력 모달리티출력 모달리티라이선스
Cohereembed-english-v32023-11-01512text—CC-BY-NC-4.0

벤치마크 성능

벤치마크점수단위비고
MMLU (Massive Multitask Language Understanding)0.0%embed model - not applicable
HumanEval0.0pass@1embed model - not applicable
GSM8K (Grade School Math 8K)0.0%embed model - not applicable
MATH0.0%embed model - not applicable
BBH (BIG-Bench Hard)0.0%embed model - not applicable
GPQA0.0%embed model - not applicable
IFEval0.0%embed model - not applicable
ARC0.0%embed model - not applicable
MUSR0.0%embed model - not applicable
WinoGrande0.0%embed model - not applicable

가격

입력출력캐시 읽기캐시 쓰기
————

백만 토큰당

강점

  • 可靠的通用模型。

약점

  • MMLU 仅 0.0，知识推理偏弱。
  • HumanEval 0.0，代码能力较弱。
  • 闭源专有模型，不支持自托管。
  • 上下文窗口 0K 偏小。

사용 사례

  • 通用对话与问答

참고문헌