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---
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
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