Mmlw Roberta Base vs Mmlw Roberta Large
MTEB multilingual v2 · Mean over tasks · multilingual (1037 languages) Embeddings assign examples to categories, group similar examples, estimate semantic similarity between pairs, classify relationships between pairs, rank relevant documents for queries, find translated sentence pairs, rerank documents according to instructions, assign multiple labels to each example, reorder candidate documents by relevance on MTEB Multilingual v2 · mean over tasks in 1037 languages including 'Auhelawa, Abau, Achi, Achinese, Achuar-Shiwiar, Adzera, Afrikaans, Agarabi; tasks receive equal weight
Mmlw Roberta Base41.5
Mmlw Roberta Large42.5
| Benchmark | Mmlw Roberta Base | Mmlw Roberta Large |
|---|---|---|
| MTEB multilingual v2 · Mean over tasks · multilingual (1037 languages) | 41.5 | 42.5 |
Specs
| Spec | Mmlw Roberta Base | Mmlw Roberta Large |
|---|---|---|
| Context | 1K | 1K |
| Weights | Open | Open |
| Released | Nov 2023 | Nov 2023 |
| Dimensions | 768 | 1,024 |
| Max input | 1K tokens | 1K tokens |
| Languages | Polish | Polish |
| Memory (4-bit) | 0.1 GB | 0.3 GB |
| Licence | Apache-2.0 | Apache-2.0 |
| Commercial use | Allowed | Allowed |
What breaks when you switch
Mmlw Roberta Base → Mmlw Roberta Large
Re-embed your whole corpus›
Vectors from different models can't be mixed; dimensions change from 768 to 1,024.
Memory (4-bit): 0.1 GB → 0.3 GB
Mmlw Roberta Large → Mmlw Roberta Base
Re-embed your whole corpus›
Vectors from different models can't be mixed; dimensions change from 1,024 to 768.