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sinequa
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passage-ranker-v1-XS-multilingual

Text Classification
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PyTorch
4 languages
bert
Inference Endpoints
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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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-
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- # Model Card for `passage-ranker-v1-XS-multilingual`
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-
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- This model is a passage ranker developed by Sinequa. It produces a relevance score given a query-passage pair and is
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- used to order search results.
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-
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- Model name: `passage-ranker-v1-XS-multilingual`
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-
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- ## Supported Languages
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-
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- The model was trained and tested in the following languages:
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-
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- - English
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- - French
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- - German
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- - Spanish
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-
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- ## Scores
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-
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- | Metric | Value |
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- |:--------------------|------:|
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- | Relevance (NDCG@10) | 0.453 |
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-
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- Note that the relevance score is computed as an average over 14 retrieval datasets (see
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- [details below](#evaluation-metrics)).
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-
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- ## Inference Times
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-
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- | GPU | Batch size 32 |
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- |:-----------|--------------:|
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- | NVIDIA A10 | 8 ms |
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- | NVIDIA T4 | 21 ms |
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-
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- The inference times only measure the time the model takes to process a single batch, it does not include pre- or
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- post-processing steps like the tokenization.
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-
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- ## Requirements
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-
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- - Minimal Sinequa version: 11.10.0
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- - GPU memory usage: 300 MiB
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-
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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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-
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- ## Model Details
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-
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- ### Overview
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-
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- - Number of parameters: 16 million
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- - Base language model: Homegrown Sinequa BERT-Mini ([Paper](https://arxiv.org/abs/1908.08962)) pretrained in the four
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- supported languages
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- - Insensitive to casing and accents
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- - Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
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-
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- ### Training Data
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-
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- - Probably-Asked Questions
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- ([Paper](https://arxiv.org/abs/2102.07033),
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- [Official Page](https://github.com/facebookresearch/PAQ))
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- - Original English dataset
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- - Translated datasets for the other three supported languages
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-
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- ### Evaluation Metrics
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-
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- To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
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- [BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
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-
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- | Dataset | NDCG@10 |
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- |:------------------|--------:|
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- | Average | 0.453 |
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- | | |
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- | Arguana | 0.516 |
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- | CLIMATE-FEVER | 0.159 |
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- | DBPedia Entity | 0.355 |
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- | FEVER | 0.729 |
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- | FiQA-2018 | 0.282 |
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- | HotpotQA | 0.688 |
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- | MS MARCO | 0.334 |
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- | NFCorpus | 0.341 |
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- | NQ | 0.438 |
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- | Quora | 0.726 |
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- | SCIDOCS | 0.143 |
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- | SciFact | 0.630 |
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- | TREC-COVID | 0.664 |
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- | Webis-Touche-2020 | 0.337 |
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-
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- We evaluated the model on the datasets of the [MIRACL benchmark](https://github.com/project-miracl/miracl) to test its
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- multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics
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- for the existing languages.
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-
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- | Language | NDCG@10 |
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- |:---------|--------:|
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- | French | 0.346 |
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- | German | 0.368 |
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- | Spanish | 0.416 |
 
 
 
 
 
 
 
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+ ---
2
+ language:
3
+ - de
4
+ - en
5
+ - es
6
+ - fr
7
+ ---
8
+
9
+ # Model Card for `passage-ranker-v1-XS-multilingual`
10
+
11
+ 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.
12
+
13
+ Model name: `passage-ranker-v1-XS-multilingual`
14
+
15
+ ## Supported Languages
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+
17
+ The model was trained and tested in the following languages:
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+
19
+ - English
20
+ - French
21
+ - German
22
+ - Spanish
23
+
24
+ ## Scores
25
+
26
+ | Metric | Value |
27
+ |:--------------------|------:|
28
+ | Relevance (NDCG@10) | 0.453 |
29
+
30
+ Note that the relevance score is computed as an average over 14 retrieval datasets (see
31
+ [details below](#evaluation-metrics)).
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+
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+ ## Inference Times
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+
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+ | GPU | Quantization type | Batch size 1 | Batch size 32 |
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+ |:------------------------------------------|:------------------|---------------:|---------------:|
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+ | NVIDIA A10 | FP16 | 1 ms | 2 ms |
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+ | NVIDIA A10 | FP32 | 1 ms | 7 ms |
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+ | NVIDIA T4 | FP16 | 1 ms | 6 ms |
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+ | NVIDIA T4 | FP32 | 1 ms | 20 ms |
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+ | NVIDIA L4 | FP16 | 1 ms | 3 ms |
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+ | NVIDIA L4 | FP32 | 2 ms | 8 ms |
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+
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+ ## Gpu Memory usage
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+
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+ | Quantization type | Memory |
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+ |:-------------------------------------------------|-----------:|
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+ | FP16 | 150 MiB |
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+ | FP32 | 300 MiB |
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+
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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
52
+ size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which
53
+ can be around 0.5 to 1 GiB depending on the used GPU.
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+
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+ ## Requirements
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+
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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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+
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+ ## Model Details
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+
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+ ### Overview
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+
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+ - Number of parameters: 16 million
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+ - Base language model: Homegrown Sinequa BERT-Mini ([Paper](https://arxiv.org/abs/1908.08962)) pretrained in the four
67
+ supported languages
68
+ - Insensitive to casing and accents
69
+ - Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
70
+
71
+ ### Training Data
72
+
73
+ - Probably-Asked Questions
74
+ ([Paper](https://arxiv.org/abs/2102.07033),
75
+ [Official Page](https://github.com/facebookresearch/PAQ))
76
+ - Original English dataset
77
+ - Translated datasets for the other three supported languages
78
+
79
+ ### Evaluation Metrics
80
+
81
+ To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
82
+ [BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
83
+
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+ | Dataset | NDCG@10 |
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+ |:------------------|--------:|
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+ | Average | 0.453 |
87
+ | | |
88
+ | Arguana | 0.516 |
89
+ | CLIMATE-FEVER | 0.159 |
90
+ | DBPedia Entity | 0.355 |
91
+ | FEVER | 0.729 |
92
+ | FiQA-2018 | 0.282 |
93
+ | HotpotQA | 0.688 |
94
+ | MS MARCO | 0.334 |
95
+ | NFCorpus | 0.341 |
96
+ | NQ | 0.438 |
97
+ | Quora | 0.726 |
98
+ | SCIDOCS | 0.143 |
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+ | SciFact | 0.630 |
100
+ | TREC-COVID | 0.664 |
101
+ | Webis-Touche-2020 | 0.337 |
102
+
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+ We evaluated the model on the datasets of the [MIRACL benchmark](https://github.com/project-miracl/miracl) to test its multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics for the existing languages.
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+
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+ | Language | NDCG@10 |
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+ |:---------|--------:|
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+ | French | 0.346 |
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+ | German | 0.368 |
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+ | Spanish | 0.416 |