transformers pytorch bert text-classification en arxiv:1901.04085 arxiv:2102.07033 autotrain_compatible endpoints_compatible region:us
---
language:
- en
---

# Model Card for `passage-ranker-v1-XS-en`

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-v1-XS-en`

## Supported Languages

The model was trained and tested in the following languages:

- English

## Scores

| Metric              | Value |
|:--------------------|------:|
| Relevance (NDCG@10) | 0.438 |

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 |           2 ms |
| NVIDIA A10                                | FP32              |           1 ms |           8 ms |
| NVIDIA T4                                 | FP16              |           1 ms |           6 ms |
| NVIDIA T4                                 | FP32              |           3 ms |          23 ms |
| NVIDIA L4                                 | FP16              |           1 ms |           3 ms |
| NVIDIA L4                                 | FP32              |           2 ms |           8 ms |

## Gpu Memory usage

| Quantization type                                |   Memory   |
|:-------------------------------------------------|-----------:|
| FP16                                             |    150 MiB |
| FP32                                             |    200 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
- Minimal Sinequa version for using FP16 models and GPUs with CUDA compute capability of 8.9+ (like NVIDIA L4): 11.11.0
- [Cuda compute capability](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)

## Model Details

### Overview

- Number of parameters: 11 million
- Base language model: [English BERT-Mini](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4)
- Insensitive to casing and accents
- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)

### Training Data

- Probably-Asked Questions
  ([Paper](https://arxiv.org/abs/2102.07033),
  [Official Page](https://github.com/facebookresearch/PAQ))

### 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           |   0.438 |
|                   |         |
| Arguana           |   0.524 |
| CLIMATE-FEVER     |   0.150 |
| DBPedia Entity    |   0.338 |
| FEVER             |   0.706 |
| FiQA-2018         |   0.269 |
| HotpotQA          |   0.630 |
| MS MARCO          |   0.328 |
| NFCorpus          |   0.340 |
| NQ                |   0.429 |
| Quora             |   0.722 |
| SCIDOCS           |   0.141 |
| SciFact           |   0.627 |
| TREC-COVID        |   0.628 |
| Webis-Touche-2020 |   0.306 |
