Cohere Embed Multilingual v3
Supports 100+ languages with 512 token context length enabling within-language and cross-language retrieval. Requires input_type specification for search documents and queries to optimize multilingual search performance. If you want to compare the best embedding models for your data, try Agentset.
Model Information
- Provider
- Cohere
- License
- Proprietary
- Price per 1M tokens
- $0.100
- Dimensions
- 512
- Release Date
- 2024-02-07
- Model Name
- cohere-embed-multilingual-v3
- Total Evaluations
- 830
Performance Record
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Performance Overview
ELO ratings by dataset
Cohere Embed Multilingual v3's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Cohere Embed Multilingual v3 - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 8ms
- P50 (Median)
- 8ms
- P90
- 8ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.786
- nDCG@10
- 0.783
- Recall@5
- 0.061
- Recall@10
- 0.122
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.804
- nDCG@10
- 0.812
- Recall@5
- 0.624
- Recall@10
- 0.696
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.696
- nDCG@10
- 0.702
- Recall@5
- 0.804
- Recall@10
- 0.830
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.952
- nDCG@10
- 0.941
- Recall@5
- 0.121
- Recall@10
- 0.218
Latency Distribution
- Mean
- 8ms
- P50 (Median)
- 8ms
- P90
- 8ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.868
- nDCG@10
- 0.875
- Recall@5
- 0.940
- Recall@10
- 0.960
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with top-ranked embedding models and smart retrieval built in. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Compare Models
See how it stacks up
Compare Cohere Embed Multilingual v3 with other top embeddings to understand the differences in performance, accuracy, and latency.