---
language:
- de
- en
- es
- fr
---

# Model Card for `answer-finder-v1-L-multilingual`

This model is a question answering model developed by Sinequa. It produces two lists of logit scores corresponding to the start token and end token of an answer.

Model name: `answer-finder-v1-L-multilingual`

## Supported Languages

The model was trained and tested in the following languages:

- English
- French
- German
- Spanish

## Scores

| Metric                                                        |  Value |
|:--------------------------------------------------------------|-------:|
| F1 Score on SQuAD v2 EN with Hugging Face evaluation pipeline |     75 |
| F1 Score on SQuAD v2 EN with Haystack evaluation pipeline     |     75 |
| F1 Score on SQuAD v2 FR with Haystack evaluation pipeline     |   73.4 |
| F1 Score on SQuAD v2 DE with Haystack evaluation pipeline     |   90.8 |
| F1 Score on SQuAD v2 ES with Haystack evaluation pipeline     |   67.1 |

## Inference Time

| GPU                                       | Quantization type |  Batch size 1  |  Batch size 32 |
|:------------------------------------------|:------------------|---------------:|---------------:|
| NVIDIA A10                                | FP16              |           2 ms |          30 ms |
| NVIDIA A10                                | FP32              |           4 ms |          83 ms |
| NVIDIA T4                                 | FP16              |           3 ms |          65 ms |
| NVIDIA T4                                 | FP32              |          14 ms |         373 ms |
| NVIDIA L4                                 | FP16              |           2 ms |          38 ms |
| NVIDIA L4                                 | FP32              |           5 ms |         124 ms |

**Note that the Answer Finder models are only used at query time.**

## Gpu Memory usage

| Quantization type                                |   Memory   |
|:-------------------------------------------------|-----------:|
| FP16                                             |    550 MiB |
| FP32                                             |   1050 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: 110 million
- Base language model: [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
  pre-trained by Sinequa in English, French, German and Spanish
- Insensitive to casing and accents

### Training Data

- [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/)
- [French-SQuAD](https://github.com/Alikabbadj/French-SQuAD) + French translation of SQuAD v2 "impossible" query-passage pairs
- [GermanQuAD](https://www.deepset.ai/germanquad) + German translation of SQuAD v2 "impossible" query-passage pairs
- [SQuAD-es-v2](https://github.com/ccasimiro88/TranslateAlignRetrieve)