Correct dataset definition

README.md

@@ -1,104 +1,103 @@
----
-language:
-  - de
-  - en
-  - es
-  - fr
----
-
-# Model Card for `passage-ranker-v1-XS-multilingual`
-
-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-multilingual`
-
-## Supported Languages
-
-The model was trained and tested in the following languages:
-
-- English
-- French
-- German
-- Spanish
-
-## Scores
-
-| Metric              | Value |
-|:--------------------|------:|
-| Relevance (NDCG@10) | 0.453 |
-
-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  |         21 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: 300 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: 16 million
-- Base language model: Homegrown Sinequa BERT-Mini ([Paper](https://arxiv.org/abs/1908.08962)) pretrained in the four
-  supported languages
-- Insensitive to casing and accents
-- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
-
-### Training Data
-
-- MS MARCO Passage Ranking
-  ([Paper](https://arxiv.org/abs/1611.09268),
-  [Official Page](https://microsoft.github.io/msmarco/),
-  [English & translated datasets on the HF dataset hub](https://huggingface.co/datasets/unicamp-dl/mmarco))
-    - Original English dataset
-    - Translated datasets for the other three supported languages
-
-### 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           |   0.453 |
-|                   |         |
-| Arguana           |   0.516 |
-| CLIMATE-FEVER     |   0.159 |
-| DBPedia Entity    |   0.355 |
-| FEVER             |   0.729 |
-| FiQA-2018         |   0.282 |
-| HotpotQA          |   0.688 |
-| MS MARCO          |   0.334 |
-| NFCorpus          |   0.341 |
-| NQ                |   0.438 |
-| Quora             |   0.726 |
-| SCIDOCS           |   0.143 |
-| SciFact           |   0.630 |
-| TREC-COVID        |   0.664 |
-| Webis-Touche-2020 |   0.337 |
-
-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.
-
-| Language | NDCG@10 |
-|:---------|--------:|
-| French   |   0.346 |
-| German   |   0.368 |
-| Spanish  |   0.416 |
+---
+language:
+  - de
+  - en
+  - es
+  - fr
+---
+
+# Model Card for `passage-ranker-v1-XS-multilingual`
+
+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-multilingual`
+
+## Supported Languages
+
+The model was trained and tested in the following languages:
+
+- English
+- French
+- German
+- Spanish
+
+## Scores
+
+| Metric              | Value |
+|:--------------------|------:|
+| Relevance (NDCG@10) | 0.453 |
+
+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  |         21 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: 300 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: 16 million
+- Base language model: Homegrown Sinequa BERT-Mini ([Paper](https://arxiv.org/abs/1908.08962)) pretrained in the four
+  supported languages
+- 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))
+    - Original English dataset
+    - Translated datasets for the other three supported languages
+
+### 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           |   0.453 |
+|                   |         |
+| Arguana           |   0.516 |
+| CLIMATE-FEVER     |   0.159 |
+| DBPedia Entity    |   0.355 |
+| FEVER             |   0.729 |
+| FiQA-2018         |   0.282 |
+| HotpotQA          |   0.688 |
+| MS MARCO          |   0.334 |
+| NFCorpus          |   0.341 |
+| NQ                |   0.438 |
+| Quora             |   0.726 |
+| SCIDOCS           |   0.143 |
+| SciFact           |   0.630 |
+| TREC-COVID        |   0.664 |
+| Webis-Touche-2020 |   0.337 |
+
+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.
+
+| Language | NDCG@10 |
+|:---------|--------:|
+| French   |   0.346 |
+| German   |   0.368 |
+| Spanish  |   0.416 |