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README.md
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language:
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- de
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- es
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- fr
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- it
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- ja
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---
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# Model Card for `passage-ranker.mango`
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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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Model name: `passage-ranker.mango`
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## Supported Languages
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The model was trained and tested in the following languages:
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- Chinese
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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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Besides the aforementioned languages, basic support can be expected for additional 93 languages that were used during
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the pretraining of the base model (see
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[list of languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages)).
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## Scores
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| Metric | Value |
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|:--------------------|------:|
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| Relevance (NDCG@10) | 0.480 |
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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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## Inference Times
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| GPU | Batch size 32 |
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|:-----------|--------------:|
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| NVIDIA A10 | 84 ms |
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| NVIDIA T4 | 358 ms |
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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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## Requirements
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- Minimal Sinequa version: 11.10.0
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- GPU memory usage: 1070 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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## Model Details
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### Overview
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- Number of parameters: 167 million
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- Base language model: [Multilingual BERT-Base](https://huggingface.co/bert-base-multilingual-uncased)
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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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### Training Data
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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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### Evaluation Metrics
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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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| Dataset | NDCG@10 |
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|:------------------|--------:|
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| Average | 0.480 |
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| Arguana | 0.537 |
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| CLIMATE-FEVER | 0.241 |
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| DBPedia Entity | 0.371 |
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| FEVER | 0.777 |
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| FiQA-2018 | 0.327 |
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| HotpotQA | 0.696 |
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| MS MARCO | 0.414 |
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| NFCorpus | 0.332 |
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| NQ | 0.484 |
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| Quora | 0.768 |
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| SCIDOCS | 0.143 |
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| SciFact | 0.648 |
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| TREC-COVID | 0.673 |
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| Webis-Touche-2020 | 0.310 |
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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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| Language
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| Chinese | 0.463 |
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| French
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| German
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| Japanese
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| Spanish
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| 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.mango`
|
| 15 |
+
|
| 16 |
+
This model is a passage ranker developed by Sinequa. It produces a relevance score given a query-passage pair and is
|
| 17 |
+
used to order search results.
|
| 18 |
+
|
| 19 |
+
Model name: `passage-ranker.mango`
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| 20 |
+
|
| 21 |
+
## Supported Languages
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| 22 |
+
|
| 23 |
+
The model was trained and tested in the following languages:
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+
|
| 25 |
+
- Chinese (simplified)
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| 26 |
+
- Dutch
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| 27 |
+
- English
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| 28 |
+
- French
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| 29 |
+
- German
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| 30 |
+
- Italian
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| 31 |
+
- Japanese
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| 32 |
+
- Portuguese
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| 33 |
+
- Spanish
|
| 34 |
+
|
| 35 |
+
Besides the aforementioned languages, basic support can be expected for additional 93 languages that were used during
|
| 36 |
+
the pretraining of the base model (see
|
| 37 |
+
[list of languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages)).
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| 38 |
+
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+
## Scores
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| 40 |
+
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+
| Metric | Value |
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| 42 |
+
|:--------------------|------:|
|
| 43 |
+
| Relevance (NDCG@10) | 0.480 |
|
| 44 |
+
|
| 45 |
+
Note that the relevance score is computed as an average over 14 retrieval datasets (see
|
| 46 |
+
[details below](#evaluation-metrics)).
|
| 47 |
+
|
| 48 |
+
## Inference Times
|
| 49 |
+
|
| 50 |
+
| GPU | Batch size 32 |
|
| 51 |
+
|:-----------|--------------:|
|
| 52 |
+
| NVIDIA A10 | 84 ms |
|
| 53 |
+
| NVIDIA T4 | 358 ms |
|
| 54 |
+
|
| 55 |
+
The inference times only measure the time the model takes to process a single batch, it does not include pre- or
|
| 56 |
+
post-processing steps like the tokenization.
|
| 57 |
+
|
| 58 |
+
## Requirements
|
| 59 |
+
|
| 60 |
+
- Minimal Sinequa version: 11.10.0
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| 61 |
+
- GPU memory usage: 1070 MiB
|
| 62 |
+
|
| 63 |
+
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 |
+
|
| 67 |
+
## Model Details
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| 68 |
+
|
| 69 |
+
### Overview
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| 70 |
+
|
| 71 |
+
- Number of parameters: 167 million
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| 72 |
+
- Base language model: [Multilingual BERT-Base](https://huggingface.co/bert-base-multilingual-uncased)
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| 73 |
+
- Insensitive to casing and accents
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| 74 |
+
- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
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| 75 |
+
|
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+
### Training Data
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| 77 |
+
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+
- MS MARCO Passage Ranking
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+
([Paper](https://arxiv.org/abs/1611.09268),
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| 80 |
+
[Official Page](https://microsoft.github.io/msmarco/),
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| 81 |
+
[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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| 84 |
+
|
| 85 |
+
### Evaluation Metrics
|
| 86 |
+
|
| 87 |
+
To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
|
| 88 |
+
[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
|
| 89 |
+
|
| 90 |
+
| Dataset | NDCG@10 |
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| 91 |
+
|:------------------|--------:|
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| 92 |
+
| Average | 0.480 |
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| 93 |
+
| | |
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| 94 |
+
| Arguana | 0.537 |
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| 95 |
+
| CLIMATE-FEVER | 0.241 |
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| 96 |
+
| DBPedia Entity | 0.371 |
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| 97 |
+
| FEVER | 0.777 |
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| 98 |
+
| FiQA-2018 | 0.327 |
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+
| HotpotQA | 0.696 |
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+
| MS MARCO | 0.414 |
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+
| NFCorpus | 0.332 |
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+
| NQ | 0.484 |
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+
| Quora | 0.768 |
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+
| SCIDOCS | 0.143 |
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+
| SciFact | 0.648 |
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+
| TREC-COVID | 0.673 |
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| 107 |
+
| Webis-Touche-2020 | 0.310 |
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| 108 |
+
|
| 109 |
+
We evaluated the model on the datasets of the [MIRACL benchmark](https://github.com/project-miracl/miracl) to test its
|
| 110 |
+
multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics
|
| 111 |
+
for the existing languages.
|
| 112 |
+
|
| 113 |
+
| Language | NDCG@10 |
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| 114 |
+
|:----------------------|--------:|
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| 115 |
+
| Chinese (simplified) | 0.463 |
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| 116 |
+
| French | 0.447 |
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+
| German | 0.415 |
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+
| Japanese | 0.526 |
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| Spanish | 0.485 |
|