Update model card with FP16 info
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README.md
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language:
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# Model Card for `answer-finder-v1-L-multilingual`
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This model is a question answering model developed by Sinequa. It produces two lists of logit scores corresponding to
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| F1 Score on SQuAD v2 EN with
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| F1 Score on SQuAD v2
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| F1 Score on SQuAD v2
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| F1 Score on SQuAD v2
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| NVIDIA A10
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| NVIDIA T4
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---
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language:
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- de
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- en
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- es
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- fr
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---
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# Model Card for `answer-finder-v1-L-multilingual`
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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.
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Model name: `answer-finder-v1-L-multilingual`
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## Supported Languages
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The model was trained and tested in the following languages:
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- English
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- French
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- German
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- Spanish
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## Scores
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| Metric | Value |
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|:--------------------------------------------------------------|-------:|
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| F1 Score on SQuAD v2 EN with Hugging Face evaluation pipeline | 75 |
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| F1 Score on SQuAD v2 EN with Haystack evaluation pipeline | 75 |
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| F1 Score on SQuAD v2 FR with Haystack evaluation pipeline | 73.4 |
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| F1 Score on SQuAD v2 DE with Haystack evaluation pipeline | 90.8 |
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| F1 Score on SQuAD v2 ES with Haystack evaluation pipeline | 67.1 |
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## Inference Time
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| GPU | Quantization type | Batch size 1 | Batch size 32 |
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|:------------------------------------------|:------------------|---------------:|---------------:|
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| NVIDIA A10 | FP16 | 2 ms | 30 ms |
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| NVIDIA A10 | FP32 | 4 ms | 83 ms |
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| NVIDIA T4 | FP16 | 3 ms | 65 ms |
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| NVIDIA T4 | FP32 | 14 ms | 373 ms |
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| NVIDIA L4 | FP16 | 2 ms | 38 ms |
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| NVIDIA L4 | FP32 | 5 ms | 124 ms |
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**Note that the Answer Finder models are only used at query time.**
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## Gpu Memory usage
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| Quantization type | Memory |
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|:-------------------------------------------------|-----------:|
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| FP16 | 550 MiB |
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| FP32 | 1050 MiB |
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Note that GPU memory usage only includes how much GPU memory the actual model consumes on an NVIDIA T4 GPU with a batch
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size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which
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can be around 0.5 to 1 GiB depending on the used GPU.
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## GPU Memory usage
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| Quantization type | Memory |
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|:-------------------------------------------------|-----------:|
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| FP16 | 547 MiB |
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| FP32 | 1060 MiB |
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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.
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## Requirements
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- Minimal Sinequa version: 11.10.0
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- Minimal Sinequa version for using FP16 models and GPUs with CUDA compute capability of 8.9+ (like NVIDIA L4): 11.11.0
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- [Cuda compute capability](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)
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## Model Details
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### Overview
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- Number of parameters: 110 million
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- Base language model: [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
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pre-trained by Sinequa in English, French, German and Spanish
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- Insensitive to casing and accents
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### Training Data
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- [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/)
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- [French-SQuAD](https://github.com/Alikabbadj/French-SQuAD) + French translation of SQuAD v2 "impossible" query-passage pairs
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- [GermanQuAD](https://www.deepset.ai/germanquad) + German translation of SQuAD v2 "impossible" query-passage pairs
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- [SQuAD-es-v2](https://github.com/ccasimiro88/TranslateAlignRetrieve)
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