Model Card for passage-ranker.pistachio
This model is a passage ranker developed by Sinequa. It produces a relevance score given a query-passage pair and is used to order search results.
Model name: passage-ranker.pistachio
Supported Languages
The model was trained and tested in the following languages:
- English
- French
- German
- Spanish
- Italian
- Dutch
- Japanese
- Portuguese
- Chinese (simplified)
- Polish
Besides the aforementioned languages, basic support can be expected for additional 93 languages that were used during the pretraining of the base model (see list of languages).
Scores
| Metric | Value |
|---|---|
| English Relevance (NDCG@10) | 0.474 |
| Polish Relevance (NDCG@10) | 0.380 |
Note that the relevance score is computed as an average over several retrieval datasets (see details below).
Inference Times
| GPU | Quantization type | Batch size 1 | Batch size 32 |
|---|---|---|---|
| NVIDIA A10 | FP16 | 2 ms | 28 ms |
| NVIDIA A10 | FP32 | 4 ms | 82 ms |
| NVIDIA T4 | FP16 | 3 ms | 65 ms |
| NVIDIA T4 | FP32 | 14 ms | 369 ms |
| NVIDIA L4 | FP16 | 3 ms | 38 ms |
| NVIDIA L4 | FP32 | 5 ms | 123 ms |
GPU Memory usage
| Quantization type | Memory |
|---|---|
| FP16 | 850 MiB |
| FP32 | 1200 MiB |
Note that GPU memory usage only includes how much GPU memory the actual model consumes on an NVIDIA T4 GPU with a batch size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which can be around 0.5 to 1 GiB depending on the used GPU.
Requirements
- Minimal Sinequa version: 11.10.0
- CUDA compute capability: above 7.5
Model Details
Overview
- Number of parameters: 167 million
- Base language model: Multilingual BERT-Base
- Insensitive to casing and accents
- Training procedure: MonoBERT
Training Data
- MS MARCO Passage Ranking
(Paper,
Official Page,
English & translated datasets on the HF dataset hub, translated dataset in Polish on the HF dataset hub)
- Original English dataset
- Translated datasets for the other nine supported languages
Evaluation Metrics
English
To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the BEIR benchmark. Note that all these datasets are in English.
| Dataset | NDCG@10 |
|---|---|
| Average | 0.474 |
| Arguana | 0.539 |
| CLIMATE-FEVER | 0.230 |
| DBPedia Entity | 0.369 |
| FEVER | 0.765 |
| FiQA-2018 | 0.329 |
| HotpotQA | 0.694 |
| MS MARCO | 0.413 |
| NFCorpus | 0.337 |
| NQ | 0.486 |
| Quora | 0.714 |
| SCIDOCS | 0.144 |
| SciFact | 0.649 |
| TREC-COVID | 0.651 |
| Webis-Touche-2020 | 0.312 |
Polish
This model has polish capacities, that are being evaluated over a subset of the PIRBenchmark with BM25 as the first stage retrieval.
| Dataset | NDCG@10 |
|---|---|
| Average | 0.380 |
| arguana-pl | 0.285 |
| dbpedia-pl | 0.283 |
| fiqa-pl | 0.223 |
| hotpotqa-pl | 0.603 |
| msmarco-pl | 0.259 |
| nfcorpus-pl | 0.293 |
| nq-pl | 0.355 |
| quora-pl | 0.613 |
| scidocs-pl | 0.128 |
| scifact-pl | 0.581 |
| trec-covid-pl | 0.560 |
Other languages
We evaluated the model on the datasets of the MIRACL benchmark to test its multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics for the existing languages.
| Language | NDCG@10 |
|---|---|
| French | 0.439 |
| German | 0.418 |
| Spanish | 0.487 |
| Japanese | 0.517 |
| Chinese (simplified) | 0.454 |