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---
language:
- en
---
# Model Card for `passage-ranker.chocolate`
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.chocolate`
## Supported Languages
The model was trained and tested in the following languages:
- English
## Scores
| Metric | Value |
|:--------------------|------:|
| Relevance (NDCG@10) | 0.484 |
Note that the relevance score is computed as an average over 14 retrieval datasets (see
[details below](#evaluation-metrics)).
## Inference Times
| GPU | Quantization type | Batch size 1 | Batch size 32 |
|:------------------------------------------|:------------------|---------------:|---------------:|
| NVIDIA A10 | FP16 | 1 ms | 5 ms |
| NVIDIA A10 | FP32 | 2 ms | 22 ms |
| NVIDIA T4 | FP16 | 1 ms | 13 ms |
| NVIDIA T4 | FP32 | 3 ms | 66 ms |
| NVIDIA L4 | FP16 | 2 ms | 6 ms |
| NVIDIA L4 | FP32 | 3 ms | 30 ms |
## Gpu Memory usage
| Quantization type | Memory |
|:-------------------------------------------------|-----------:|
| FP16 | 300 MiB |
| FP32 | 550 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](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)
## Model Details
### Overview
- Number of parameters: 23 million
- Base language model: [MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased)
([Paper](https://arxiv.org/abs/2002.10957), [GitHub](https://github.com/microsoft/unilm/tree/master/minilm))
- Insensitive to casing and accents
- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
### Training Data
- MS MARCO Passage Ranking
([Paper](https://arxiv.org/abs/1611.09268),
[Official Page](https://microsoft.github.io/msmarco/),
[dataset on HF hub](https://huggingface.co/datasets/unicamp-dl/mmarco))
### Evaluation Metrics
To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
| Dataset | NDCG@10 |
|:------------------|--------:|
| Average | 0.486 |
| | |
| Arguana | 0.554 |
| CLIMATE-FEVER | 0.209 |
| DBPedia Entity | 0.367 |
| FEVER | 0.744 |
| FiQA-2018 | 0.339 |
| HotpotQA | 0.685 |
| MS MARCO | 0.412 |
| NFCorpus | 0.352 |
| NQ | 0.454 |
| Quora | 0.818 |
| SCIDOCS | 0.158 |
| SciFact | 0.658 |
| TREC-COVID | 0.674 |
| Webis-Touche-2020 | 0.345 |
|