Alibaba-NLP/gte-multilingual-base
Primitive: /encode · Encode ·
ModernBERT
The gte-multilingual-base model is the latest in the GTE (General Text Embedding) family of models, featuring several key attributes:
Overview
Hardware: — drives latency, throughput & cost
| Size | 305M params |
|---|---|
| Tasks | /encode |
| License | apache-2.0 |
| Languages | af, ar, az, be, bg, bn, ca, ceb, cs, cy, da, de, el, en, es, et, eu, fa, fi, fr, gl, gu, he, hi, hr, ht, hu, hy, id, is, it, ja, jv, ka, kk, km, kn, ko, ky, lo, lt, lv, mk, ml, mn, mr, ms, my, ne, nl, no, pa, pl, pt, qu, ro, ru, si, sk, sl, so, sq, sr, sv, sw, ta, te, th, tl, tr, uk, ur, vi, yo, zh |
| Latency | 57 ms |
| Throughput | 55.1K tok/s |
| Cost | $0.0040 /1M tok |
Cost is approximate — computed from list GPU prices; your actual price depends on the provider you deploy SIE with.
Embedding
| Output types | Dense |
|---|---|
| Dimensions | dense: 768 |
| Max sequence length | 8,192 |
| Inputs | text |
Benchmarks
CQADupstackPhysicsRetrieval
Duplicate question retrieval from StackExchange Physics
CosQA
Code search with natural language queries
FiQA2018
Financial opinion mining and question answering
LegalBenchConsumerContractsQA
Question answering on consumer contracts
NFCorpus
Biomedical literature search from NutritionFacts.org
NanoFiQA2018Retrieval
Smaller subset of the FiQA financial QA dataset
SCIDOCS
Citation prediction, document classification, and recommendation for scientific papers
SciFact
Scientific claim verification using research literature
StackOverflowQA
Programming question answering from Stack Overflow