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passage-ranker.strawberry

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xlm-roberta
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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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- - it
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- - ja
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- - nl
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- - pt
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- - zh
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- ---
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-
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- # Model Card for `passage-ranker.strawberry`
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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.strawberry`
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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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- - Chinese (simplified)
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- - Dutch
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- - English
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- - French
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- - German
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- - Italian
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- - Japanese
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- - Portuguese
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- - Spanish
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-
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- Besides the aforementioned languages, basic support can be expected for additional 91 languages that were used during
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- the pretraining of the base model (see Appendix A of [XLM-R paper](https://arxiv.org/abs/1911.02116)).
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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) | 0.451 |
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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 | 22 ms |
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- | NVIDIA T4 | 63 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: 1060 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: 107 million
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- - Base language model:
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- [mMiniLMv2-L6-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large)
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- ([Paper](https://arxiv.org/abs/2012.15828), [GitHub](https://github.com/microsoft/unilm/tree/master/minilm))
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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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- - MS MARCO Passage Ranking
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- ([Paper](https://arxiv.org/abs/1611.09268),
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- [Official Page](https://microsoft.github.io/msmarco/),
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- [English & translated datasets on the HF dataset hub](https://huggingface.co/datasets/unicamp-dl/mmarco))
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- - Original English dataset
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- - Translated datasets for the other eight 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 | 0.451 |
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- | | |
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- | Arguana | 0.527 |
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- | CLIMATE-FEVER | 0.167 |
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- | DBPedia Entity | 0.343 |
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- | FEVER | 0.698 |
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- | FiQA-2018 | 0.297 |
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- | HotpotQA | 0.648 |
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- | MS MARCO | 0.409 |
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- | NFCorpus | 0.317 |
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- | NQ | 0.430 |
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- | Quora | 0.761 |
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- | SCIDOCS | 0.135 |
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- | SciFact | 0.597 |
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- | TREC-COVID | 0.670 |
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- | Webis-Touche-2020 | 0.311 |
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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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- | Chinese (simplified) | 0.414 |
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- | French | 0.382 |
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- | German | 0.320 |
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- | Japanese | 0.479 |
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- | Spanish | 0.418 |
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - de
4
+ - en
5
+ - es
6
+ - fr
7
+ - it
8
+ - ja
9
+ - nl
10
+ - pt
11
+ - zh
12
+ ---
13
+
14
+ # Model Card for `passage-ranker.strawberry`
15
+
16
+ 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.
17
+
18
+ Model name: `passage-ranker.strawberry`
19
+
20
+ ## Supported Languages
21
+
22
+ The model was trained and tested in the following languages:
23
+
24
+ - Chinese (simplified)
25
+ - Dutch
26
+ - English
27
+ - French
28
+ - German
29
+ - Italian
30
+ - Japanese
31
+ - Portuguese
32
+ - Spanish
33
+
34
+ Besides the aforementioned languages, basic support can be expected for additional 91 languages that were used during the pretraining of the base model (see Appendix A of [XLM-R paper](https://arxiv.org/abs/1911.02116)).
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+
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+ ## Scores
37
+
38
+ | Metric | Value |
39
+ |:--------------------|------:|
40
+ | Relevance (NDCG@10) | 0.451 |
41
+
42
+ Note that the relevance score is computed as an average over 14 retrieval datasets (see
43
+ [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 | 5 ms |
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+ | NVIDIA A10 | FP32 | 2 ms | 22 ms |
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+ | NVIDIA T4 | FP16 | 1 ms | 13 ms |
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+ | NVIDIA T4 | FP32 | 3 ms | 64 ms |
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+ | NVIDIA L4 | FP16 | 2 ms | 6 ms |
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+ | NVIDIA L4 | FP32 | 2 ms | 30 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 | 550 MiB |
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+ | FP32 | 1100 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
64
+ size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which
65
+ can be around 0.5 to 1 GiB depending on the used GPU.
66
+
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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: 107 million
78
+ - Base language model:
79
+ [mMiniLMv2-L6-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large)
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+ ([Paper](https://arxiv.org/abs/2012.15828), [GitHub](https://github.com/microsoft/unilm/tree/master/minilm))
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+ - Insensitive to casing and accents
82
+ - Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
83
+
84
+ ### Training Data
85
+
86
+ - MS MARCO Passage Ranking
87
+ ([Paper](https://arxiv.org/abs/1611.09268),
88
+ [Official Page](https://microsoft.github.io/msmarco/),
89
+ [English & translated datasets on the HF dataset hub](https://huggingface.co/datasets/unicamp-dl/mmarco))
90
+ - Original English dataset
91
+ - Translated datasets for the other eight supported languages
92
+
93
+ ### Evaluation Metrics
94
+
95
+ To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
96
+ [BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
97
+
98
+ | Dataset | NDCG@10 |
99
+ |:------------------|--------:|
100
+ | Average | 0.451 |
101
+ | | |
102
+ | Arguana | 0.527 |
103
+ | CLIMATE-FEVER | 0.167 |
104
+ | DBPedia Entity | 0.343 |
105
+ | FEVER | 0.698 |
106
+ | FiQA-2018 | 0.297 |
107
+ | HotpotQA | 0.648 |
108
+ | MS MARCO | 0.409 |
109
+ | NFCorpus | 0.317 |
110
+ | NQ | 0.430 |
111
+ | Quora | 0.761 |
112
+ | SCIDOCS | 0.135 |
113
+ | SciFact | 0.597 |
114
+ | TREC-COVID | 0.670 |
115
+ | Webis-Touche-2020 | 0.311 |
116
+
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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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+
119
+ | Language | NDCG@10 |
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+ |:----------------------|--------:|
121
+ | Chinese (simplified) | 0.414 |
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+ | French | 0.382 |
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+ | German | 0.320 |
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+ | Japanese | 0.479 |
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+ | Spanish | 0.418 |