---
language:
- ja
---

# Model Card for `answer-finder.yuzu`

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.yuzu`

## Supported Languages

The model was trained and tested in the following languages:

- Japanese

Besides the aforementioned languages, basic support can be expected for the 104 languages that were used during the pretraining of the base model (See [original repository](https://github.com/google-research/bert)).

## Scores

| Metric                                                        |  Value |
|:--------------------------------------------------------------|-------:|
| F1 Score on JSQuAD with Hugging Face evaluation pipeline      |   92.1 |
| F1 Score on JSQuAD with Haystack evaluation pipeline          |   91.5 |

## Inference Time

| GPU                                       | Quantization type |  Batch size 1  |  Batch size 32 |
|:------------------------------------------|:------------------|---------------:|---------------:|
| NVIDIA A10                                | FP16              |          17 ms |          27 ms |
| NVIDIA A10                                | FP32              |           4 ms |          88 ms |
| NVIDIA T4                                 | FP16              |           3 ms |          64 ms |
| NVIDIA T4                                 | FP32              |          15 ms |         374 ms |
| NVIDIA L4                                 | FP16              |           3 ms |          39 ms |
| NVIDIA L4                                 | FP32              |           5 ms |         125 ms |

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

## Gpu Memory usage

| Quantization type                                |   Memory   |
|:-------------------------------------------------|-----------:|
| FP16                                             |    950 MiB |
| FP32                                             |   1350 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)
- Sensitive to casing and accents

### Training Data

- [JSQuAD](https://github.com/yahoojapan/JGLUE) see [Paper](https://aclanthology.org/2022.lrec-1.317.pdf)
- Japanese translation of SQuAD v2 "impossible" query-passage pairs