sinequa
/

passage-ranker-v1-XS-en

@@ -1,84 +1,92 @@
----
-language:
-  - en
----
-# Model Card for `passage-ranker-v1-XS-en`
-This model is a passage ranker developed by Sinequa. It produces a relevance score given a query-passage pair and is
-used to order search results.
-Model name: `passage-ranker-v1-XS-en`
-## Supported Languages
-The model was trained and tested in the following languages:
-- English
-## Scores
-| Metric              | Value |
-|:--------------------|------:|
-| Relevance (NDCG@10) | 0.438 |
-Note that the relevance score is computed as an average over 14 retrieval datasets (see
-[details below](#evaluation-metrics)).
-## Inference Times
-| GPU        | Batch size 32 |
-|:-----------|--------------:|
-| NVIDIA A10 |          8 ms |
-| NVIDIA T4  |         20 ms |
-The inference times only measure the time the model takes to process a single batch, it does not include pre- or
-post-processing steps like the tokenization.
-## Requirements
-- Minimal Sinequa version: 11.10.0
-- GPU memory usage: 170 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.
-## Model Details
-### Overview
-- Number of parameters: 11 million
-- Base language model: [English BERT-Mini](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4)
-- Insensitive to casing and accents
-- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
-### Training Data
-- Probably-Asked Questions
-  ([Paper](https://arxiv.org/abs/2102.07033),
-  [Official Page](https://github.com/facebookresearch/PAQ))
-### Evaluation Metrics
-To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
-[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
-| Dataset           | NDCG@10 |
-|:------------------|--------:|
-| Average           |   0.438 |
-|                   |         |
-| Arguana           |   0.524 |
-| CLIMATE-FEVER     |   0.150 |
-| DBPedia Entity    |   0.338 |
-| FEVER             |   0.706 |
-| FiQA-2018         |   0.269 |
-| HotpotQA          |   0.630 |
-| MS MARCO          |   0.328 |
-| NFCorpus          |   0.340 |
-| NQ                |   0.429 |
-| Quora             |   0.722 |
-| SCIDOCS           |   0.141 |
-| SciFact           |   0.627 |
-| TREC-COVID        |   0.628 |
-| Webis-Touche-2020 |   0.306 |

+---
+language:
+  - en
+---
+# Model Card for `passage-ranker-v1-XS-en`
+This model is a passage ranker developed by Sinequa. It produces a relevance score given a query-passage pair and is used to order search results.
+Model name: `passage-ranker-v1-XS-en`
+## Supported Languages
+The model was trained and tested in the following languages:
+- English
+## Scores
+| Metric              | Value |
+|:--------------------|------:|
+| Relevance (NDCG@10) | 0.438 |
+Note that the relevance score is computed as an average over 14 retrieval datasets (see
+[details below](#evaluation-metrics)).
+## Inference Times
+| GPU                                       | Quantization type |  Batch size 1  |  Batch size 32 |
+|:------------------------------------------|:------------------|---------------:|---------------:|
+| NVIDIA A10                                | FP16              |           1 ms |           2 ms |
+| NVIDIA A10                                | FP32              |           1 ms |           8 ms |
+| NVIDIA T4                                 | FP16              |           1 ms |           6 ms |
+| NVIDIA T4                                 | FP32              |           3 ms |          23 ms |
+| NVIDIA L4                                 | FP16              |           1 ms |           3 ms |
+| NVIDIA L4                                 | FP32              |           2 ms |           8 ms |
+## Gpu Memory usage
+| Quantization type                                |   Memory   |
+|:-------------------------------------------------|-----------:|
+| FP16                                             |    150 MiB |
+| FP32                                             |    200 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: 11 million
+- Base language model: [English BERT-Mini](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4)
+- Insensitive to casing and accents
+- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
+### Training Data
+- Probably-Asked Questions
+  ([Paper](https://arxiv.org/abs/2102.07033),
+  [Official Page](https://github.com/facebookresearch/PAQ))
+### Evaluation Metrics
+To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
+[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
+| Dataset           | NDCG@10 |
+|:------------------|--------:|
+| Average           |   0.438 |
+|                   |         |
+| Arguana           |   0.524 |
+| CLIMATE-FEVER     |   0.150 |
+| DBPedia Entity    |   0.338 |
+| FEVER             |   0.706 |
+| FiQA-2018         |   0.269 |
+| HotpotQA          |   0.630 |
+| MS MARCO          |   0.328 |
+| NFCorpus          |   0.340 |
+| NQ                |   0.429 |
+| Quora             |   0.722 |
+| SCIDOCS           |   0.141 |
+| SciFact           |   0.627 |
+| TREC-COVID        |   0.628 |
+| Webis-Touche-2020 |   0.306 |