Key Specifications
| Vendor | cohere |
|---|
| Version | embed-english-v3 |
|---|
| Release Date | 2023-11-01 |
|---|
| Context Window | 512 tokens |
|---|
| Input Modalities | text |
|---|
| Output Modalities | — |
|---|
| License | CC-BY-NC-4.0 |
|---|
| Documentation | https://docs.cohere.com/docs |
|---|
Benchmark Performance
| Benchmark | Score | Unit | Evaluated At | Notes | Source |
|---|
| MMLU | 0 | % | 2023-11-01 | embed model - not applicable | view |
| HUMANEVAL | 0 | pass@1 | 2023-11-01 | embed model - not applicable | view |
| GSM8K | 0 | % | 2023-11-01 | embed model - not applicable | view |
| MATH | 0 | % | 2023-11-01 | embed model - not applicable | view |
| BBH | 0 | % | 2023-11-01 | embed model - not applicable | view |
| GPQA | 0 | % | 2023-11-01 | embed model - not applicable | view |
| IFEVAL | 0 | % | 2023-11-01 | embed model - not applicable | view |
| ARC | 0 | % | 2023-11-01 | embed model - not applicable | view |
| MUSR | 0 | % | 2023-11-01 | embed model - not applicable | view |
| WINOGRANDE | 0 | % | 2023-11-01 | embed model - not applicable | view |
Pricing
| Tier | Price | Currency |
|---|
| Input | $0.1 / Mtok | USD |
| Output | $0.1 / Mtok | USD |
| Cache Read | $0 / Mtok | USD |
| Cache Write | $0 / Mtok | USD |
Source:
https://cohere.com/pricing
· as of 2023-11-01
Compliance
- Data Residency: US
- SOC2: ✓
- HIPAA: ✗
- GDPR: ✓
- ISO 27001: ✓
Embed English v3
モデル概要
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。
コア仕様
| ベンダー | バージョン | リリース日 | コンテキストウィンドウ | 入力モダリティ | 出力モダリティ | ライセンス |
|---|
| Cohere | embed-english-v3 | 2023-11-01 | 512 | text | — | CC-BY-NC-4.0 |
ベンチマークパフォーマンス
| ベンチマーク | スコア | 単位 | 備考 |
|---|
| MMLU (Massive Multitask Language Understanding) | 0.0 | % | embed model - not applicable |
| HumanEval | 0.0 | pass@1 | embed model - not applicable |
| GSM8K (Grade School Math 8K) | 0.0 | % | embed model - not applicable |
| MATH | 0.0 | % | embed model - not applicable |
| BBH (BIG-Bench Hard) | 0.0 | % | embed model - not applicable |
| GPQA | 0.0 | % | embed model - not applicable |
| IFEval | 0.0 | % | embed model - not applicable |
| ARC | 0.0 | % | embed model - not applicable |
| MUSR | 0.0 | % | embed model - not applicable |
| WinoGrande | 0.0 | % | embed model - not applicable |
料金
| 入力 | 出力 | キャッシュ読み取り | キャッシュ書き込み |
|---|
| — | — | — | — |
100万トークンあたり
強み
弱み
- 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。
ユースケース
参考文献