Initial commit (#1)

- Model sources and card (53f7300522fb5cdd1d83339dd60803813262d740)
- Use FP32 metrics (b3e6c6485de9476bd5c2c9154d750e0577fd5112)

README.md

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+---
+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 |

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 256,
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.23.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50099
+}

pytorch_model.bin

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