SAVRN Model Hub · Comparisons
bge-reranker-base vs xlm-roberta-base-language-detection
Bge-reranker-base has 278M parameters and xlm-roberta-base-language-detection has 278M parameters; both are released under MIT License; at 16-bit, bge-reranker-base needs about 0.7 GB (1x MI300X from $1.85 an hour) and xlm-roberta-base-language-detection about 0.7 GB (1x MI300X from $1.85 an hour).
| Field | bge-reranker-base BAAI/bge-reranker-base | xlm-roberta-base-language-detection papluca/xlm-roberta-base-language-detection |
|---|---|---|
| Publisher | Beijing Academy of Artificial Intelligence | Luca Papariello |
| Task | Text classification | Text classification |
| Modality | Text | Text |
| Parameters, as reported | 278M parameters | 278M parameters |
| Architecture | XLMRobertaForSequenceClassification | XLMRobertaForSequenceClassification |
| Library | sentence-transformers | transformers |
| Context length | 514 tokens | 514 tokens |
| Repository size | 3.4 GB | 3.4 GB |
| Artifact formats | safetensors, onnx, pytorch | safetensors, pytorch, tf |
| License | mit | mit |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 0.7 GB | 0.7 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0.2 GB | 0.2 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 2cfc18c9415c | 9865598389ca |
| Downloads reported by the hub | 4M | 579k |
| Last observed | 2026-09-18 | 2026-09-18 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
bge-reranker-base
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| MTEB CMedQAv1 | Configuration defaultTask RerankingMetric mapComparison conditions not established | 81.2721 | BAAI Publisher reported |
Evaluated revision not stated | — |
| MTEB CMedQAv1 | Configuration defaultTask RerankingMetric mrrComparison conditions not established | 84.1424 | BAAI Publisher reported |
Evaluated revision not stated | — |
| MTEB CMedQAv2 | Configuration defaultTask RerankingMetric mapComparison conditions not established | 84.1037 | BAAI Publisher reported |
Evaluated revision not stated | — |
| MTEB CMedQAv2 | Configuration defaultTask RerankingMetric mrrComparison conditions not established | 86.7938 | BAAI Publisher reported |
Evaluated revision not stated | — |
| MTEB MMarcoReranking | Configuration defaultTask RerankingMetric mapComparison conditions not established | 35.4601 | BAAI Publisher reported |
Evaluated revision not stated | — |
| MTEB MMarcoReranking | Configuration defaultTask RerankingMetric mrrComparison conditions not established | 34.6024 | BAAI Publisher reported |
Evaluated revision not stated | — |
| MTEB T2Reranking | Configuration defaultTask RerankingMetric mapComparison conditions not established | 67.2773 | BAAI Publisher reported |
Evaluated revision not stated | — |
| MTEB T2Reranking | Configuration defaultTask RerankingMetric mrrComparison conditions not established | 77.1315 | BAAI Publisher reported |
Evaluated revision not stated | — |
SAVRN's Notes on bge-reranker-base
Retrieval pipelines need a second pass that re-scores what the first-stage retriever returns, and that is this 278M-parameter classifier's job: it reads a query and a passage together and scores the match. Memory is not the decision. At 16-bit the weights are 0.6 GB and the run needs 0.7 GB, under one percent of the 192 GB MI300X we price at $1.85 an hour, so it never gets its own card; it rides beside whatever generation model lives there.
The MIT terms are short and permissive: commercial use, modification and redistribution are allowed provided the copyright and permission notices stay with the files. The 514-token context must hold query and passage together, so chunk to fit. And the publisher's summary says newer rerankers with larger inputs and more languages shipped March 18, 2024, so decide whether this version or a successor is what you standardize on.
SAVRN's Notes on xlm-roberta-base-language-detection
Sorting inbound text by language before it reaches a larger model is the job this one does, with a 278M-parameter XLM-RoBERTa classifier covering 20 languages. At 16-bit the weights take 0.6 GB and the run needs 0.7 GB; at 8-bit it fits in 0.3 GB. It never gets its own accelerator in our facilities. It rides beside whatever else is on the card, and the cheapest Index host, one MI300X with 192 GB at $1.85 per hour, would give it a fraction of one percent of its memory.
MIT is the license, so a commercial deployment only has to carry the copyright and permission notices. Two checks: the context window is 514 tokens, so long documents get chunked first, and the model is a fine-tune of FacebookAI/xlm-roberta-base on the papluca/language-identification dataset, worth reading if your traffic includes languages outside those 20.
Questions
Which is larger, bge-reranker-base or xlm-roberta-base-language-detection?
xlm-roberta-base-language-detection (278M parameters) is larger than bge-reranker-base (278M parameters), by the parameter counts their publishers report.
Which is cheaper to run, bge-reranker-base or xlm-roberta-base-language-detection?
At 4-bit, bge-reranker-base fits on 1x MI300X from $1.85 an hour and xlm-roberta-base-language-detection on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use bge-reranker-base commercially?
Yes. bge-reranker-base is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
Can I use xlm-roberta-base-language-detection commercially?
Yes. xlm-roberta-base-language-detection is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.