First README.md draft

.gitattributes

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 *.zst filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+  - feature-extraction
+  - sentence-similarity
+language:
+  - de
+  - en
+  - es
+  - fr
+  - it
+  - nl
+  - ja
+  - pt
+  - zh
+  - pl
+---
+
+# Model Card for `vectorizer.hazelnut`
+
+This model is a vectorizer developed by Sinequa. It produces an embedding vector given a passage or a query. The
+passage vectors are stored in our vector index and the query vector is used at query time to look up relevant passages
+in the index.
+
+Model name: `vectorizer.hazelnut`
+
+## Supported Languages
+
+The model was trained and tested in the following languages:
+
+- English
+- French
+- German
+- Spanish
+- Italian
+- Dutch
+- Japanese
+- Portuguese
+- Chinese (simplified)
+- Polish
+
+Besides these 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).
+
+## Scores
+
+| Metric                         | Value |
+|:-------------------------------|------:|
+| English Relevance (Recall@100) | 0.590 |
+| Polish Relevance (Recall@100)  | 0.543 |
+
+Note that the relevance scores are computed as an average over several retrieval datasets (see
+[details below](#evaluation-metrics)).
+
+## Inference Times
+
+| GPU                                       | Quantization type |  Batch size 1  |  Batch size 32 |
+|:------------------------------------------|:------------------|---------------:|---------------:|
+| NVIDIA A10                                | FP16              |           1 ms |           5 ms |
+| NVIDIA A10                                | FP32              |           2 ms |          18 ms |
+| NVIDIA T4                                 | FP16              |           1 ms |          12 ms |
+| NVIDIA T4                                 | FP32              |           3 ms |          52 ms |
+| NVIDIA L4                                 | FP16              |           2 ms |           5 ms |
+| NVIDIA L4                                 | FP32              |           4 ms |          24 ms |
+
+## Gpu Memory usage
+
+| Quantization type                                |   Memory   |
+|:-------------------------------------------------|-----------:|
+| FP16                                             |    550 MiB |
+| FP32                                             |   1050 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: 107 million
+- Base language
+  model: [mMiniLMv2-L6-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large) ([Paper](https://arxiv.org/abs/2012.15828), [GitHub](https://github.com/microsoft/unilm/tree/master/minilm))
+- Insensitive to casing and accents
+- Output dimensions: 256 (reduced with an additional dense layer)
+- Training procedure: Query-passage-negative triplets for datasets that have mined hard negative data, Query-passage
+  pairs for the rest. Number of negatives is augmented with in-batch negative strategy
+
+### Training Data
+
+The model have been trained using all datasets that are cited in
+the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model.
+In addition to that, this model has been trained on the datasets cited
+in [this paper](https://arxiv.org/pdf/2108.13897.pdf) on the first 9 aforementioned languages.
+It has also been trained on [this dataset](https://huggingface.co/datasets/clarin-knext/msmarco-pl) for polish capacities.
+
+### Evaluation Metrics
+
+#### English
+
+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           | Recall@100 |
+|:------------------|-----------:|
+| Average           |      0.590 |
+|                   |            |
+| Arguana           |      0.961 |
+| CLIMATE-FEVER     |      0.432 |
+| DBPedia Entity    |      0.371 |
+| FEVER             |      0.723 |
+| FiQA-2018         |      0.611 |
+| HotpotQA          |      0.564 |
+| MS MARCO          |      0.825 |
+| NFCorpus          |      0.266 |
+| NQ                |      0.722 |
+| Quora             |      0.991 |
+| SCIDOCS           |      0.426 |
+| SciFact           |      0.864 |
+| TREC-COVID        |      0.092 |
+| Webis-Touche-2020 |      0.415 |
+
+#### Polish
+
+This model has polish capacities, that are being evaluated over a subset of the [PIRBenchmark](https://github.com/sdadas/pirb).
+
+| Dataset           | Recall@100 |
+|:------------------|-----------:|
+| Average           |      0.534 |
+|                   |            |
+| arguana-pl        |      0.909 |
+| dbpedia-pl        |      0.282 |
+| fiqa-pl           |      0.439 |
+| hotpotqa-pl       |      0.530 |
+| msmarco-pl        |      0.694 |
+| nfcorpus-pl       |      0.218 |
+| nq-pl             |      0.697 |
+| quora-pl          |      0.949 |
+| scidocs-pl        |      0.291 |
+| scifact-pl        |      0.805 |
+| trec-covid-pl     |      0.059 |
+
+#### Other languages
+
+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              | Recall@100 |
+|:----------------------|-----------:|
+| French                |      0.649 |
+| German                |      0.598 |
+| Spanish               |      0.609 |
+| Japanese              |      0.623 |
+| Chinese (simplified)  |      0.707 |

config.json

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+{
+  "_name_or_path": "nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large",
+  "architectures": [
+    "XLMRobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

modules.json

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+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

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