Add model files and README

README.md

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+---
+language:
+  - de
+  - en
+  - es
+  - fr
+  - it
+  - ja
+  - nl
+  - pl
+  - pt
+  - zh
+---
+
+# Model Card for `passage-ranker.pistachio`
+
+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.pistachio`
+
+## Supported Languages
+
+The model was trained and tested in the following languages:
+
+- Chinese (simplified)
+- Dutch
+- English
+- French
+- German
+- Italian
+- Japanese
+- Polish
+- Portuguese
+- Spanish
+
+Besides the aforementioned languages, basic support can be expected for additional 93 languages that were used during the pretraining of the base model (see
+[list of languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages)).
+
+## Scores
+
+| Metric              | Value |
+|:--------------------|------:|
+| Relevance (NDCG@10) | 0.480 |
+
+Note that the relevance score is computed as an average over 14 retrieval datasets (see
+[details below](#evaluation-metrics)).
+
+## Inference Times
+
+| GPU                                       | Quantization type |  Batch size 1  |  Batch size 32 |
+|:------------------------------------------|:------------------|---------------:|---------------:|
+| NVIDIA A10                                | FP16              |           2 ms |          28 ms |
+| NVIDIA A10                                | FP32              |           4 ms |          82 ms |
+| NVIDIA T4                                 | FP16              |           3 ms |          65 ms |
+| NVIDIA T4                                 | FP32              |          14 ms |         369 ms |
+| NVIDIA L4                                 | FP16              |           3 ms |          38 ms |
+| NVIDIA L4                                 | FP32              |           5 ms |         123 ms |
+
+## Gpu Memory usage
+
+| Quantization type                                |   Memory   |
+|:-------------------------------------------------|-----------:|
+| FP16                                             |    850 MiB |
+| FP32                                             |   1200 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.
+
+## Requirements
+
+- Minimal Sinequa version: 11.10.0
+- Minimal Sinequa version for using FP16 models and GPUs with CUDA compute capability of 8.9+ (like NVIDIA L4): 11.11.0
+- [Cuda compute capability](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)
+
+## Model Details
+
+### Overview
+
+- Number of parameters: 167 million
+- Base language model: [Multilingual BERT-Base](https://huggingface.co/bert-base-multilingual-uncased)
+- 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), [translated dataset in Polish on the HF dataset hub](https://huggingface.co/datasets/clarin-knext/msmarco-pl))
+    - Original English dataset
+    - Translated datasets for the other nine 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           |   0.474 |
+|                   |         |
+| Arguana           |   0.539 |
+| CLIMATE-FEVER     |   0.230 |
+| DBPedia Entity    |   0.369 |
+| FEVER             |   0.765 |
+| FiQA-2018         |   0.329 |
+| HotpotQA          |   0.694 |
+| MS MARCO          |   0.413 |
+| NFCorpus          |   0.337 |
+| NQ                |   0.486 |
+| Quora             |   0.714 |
+| SCIDOCS           |   0.144 |
+| SciFact           |   0.649 |
+| TREC-COVID        |   0.651 |
+| Webis-Touche-2020 |   0.312 |
+
+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 |
+|:----------------------|--------:|
+| Chinese (simplified)  |   0.454 |
+| French                |   0.439 |
+| German                |   0.418 |
+| Japanese              |   0.517 |
+| Spanish               |   0.487 |

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.34.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 105879
+}

pytorch_model.bin

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