Downloads · 30 days
296
4% of all-time downloads
answer-finder-v1-L-multilingual
sinequa/answer-finder-v1-L-multilingual
answer-finder-v1-L-multilingual is a question answering model from sinequa. Use it when the input is a question plus a passage. It is set up for transformers.
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.
All-time downloads
8.2K
Public
Repo size
992 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin496 MB · 100%
At a glance
- Task
- Question Answering
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Jul 10, 2023
- Updated
- Aug 7, 2025
- SHA
- 3910d9fa
Try a prompt
Model card
From the Hugging Face model README
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
- Cuda compute capability: above 5.0 (above 6.0 for FP16 use)
Model Details
Overview
- Number of parameters: 110 million
- Base language model: bert-base-multilingual-cased pre-trained by Sinequa in English, French, German and Spanish
- Insensitive to casing and accents
Training Data
- SQuAD v2
- French-SQuAD + French translation of SQuAD v2 "impossible" query-passage pairs
- GermanQuAD + German translation of SQuAD v2 "impossible" query-passage pairs
- SQuAD-es-v2
- Task
- Question Answering
- Library
- transformers
- Type
- bert
- Languages
- de, en, es, fr
- Created
- Jul 10, 2023
- Updated
- Aug 7, 2025