sinequa
/

vectorizer.raspberry

@@ -1,124 +1,125 @@
----
-pipeline_tag: sentence-similarity
-tags:
-  - feature-extraction
-  - sentence-similarity
-language:
-  - de
-  - en
-  - es
-  - fr
-  - it
-  - nl
-  - ja
-  - pt
-  - zh
----
-# Model Card for `vectorizer.raspberry`
-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.raspberry`
-## Supported Languages
-The model was trained and tested in the following languages:
-- English
-- French
-- German
-- Spanish
-- Italian
-- Dutch
-- Japanese
-- Portuguese
-- Chinese (simplified)
-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 |
-|:-----------------------|------:|
-| Relevance (Recall@100) | 0.613 |
-Note that the relevance score is computed as an average over 14 retrieval datasets (see
-[details below](#evaluation-metrics)).
-## Inference Times
-| GPU        | Batch size 1 (at query time) | Batch size 32 (at indexing) |
-|:-----------|-----------------------------:|----------------------------:|
-| NVIDIA A10 |                         2 ms |                       19 ms |
-| NVIDIA T4  |                         4 ms |                       52 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: 610 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: 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 9 aforementioned 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           | Recall@100 |
-|:------------------|-----------:|
-| Average           |      0.613 |
-|                   |            |
-| Arguana           |      0.957 |
-| CLIMATE-FEVER     |      0.468 |
-| DBPedia Entity    |      0.377 |
-| FEVER             |      0.820 |
-| FiQA-2018         |      0.639 |
-| HotpotQA          |      0.560 |
-| MS MARCO          |      0.845 |
-| NFCorpus          |      0.287 |
-| NQ                |      0.756 |
-| Quora             |      0.992 |
-| SCIDOCS           |      0.456 |
-| SciFact           |      0.906 |
-| TREC-COVID        |      0.100 |
-| Webis-Touche-2020 |      0.413 |
-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                |      0.650 |
-| German                |      0.528 |
-| Spanish               |      0.602 |
-| Japanese              |      0.614 |
-| Chinese (simplified)  |      0.680 |

+---
+pipeline_tag: sentence-similarity
+tags:
+  - feature-extraction
+  - sentence-similarity
+language:
+  - de
+  - en
+  - es
+  - fr
+  - it
+  - nl
+  - ja
+  - pt
+  - zh
+---
+# Model Card for `vectorizer.raspberry`
+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.raspberry`
+## Supported Languages
+The model was trained and tested in the following languages:
+- English
+- French
+- German
+- Spanish
+- Italian
+- Dutch
+- Japanese
+- Portuguese
+- Chinese (simplified)
+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 |
+|:-----------------------|------:|
+| Relevance (Recall@100) | 0.613 |
+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              |           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 9 aforementioned 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           | Recall@100 |
+|:------------------|-----------:|
+| Average           |      0.613 |
+|                   |            |
+| Arguana           |      0.957 |
+| CLIMATE-FEVER     |      0.468 |
+| DBPedia Entity    |      0.377 |
+| FEVER             |      0.820 |
+| FiQA-2018         |      0.639 |
+| HotpotQA          |      0.560 |
+| MS MARCO          |      0.845 |
+| NFCorpus          |      0.287 |
+| NQ                |      0.756 |
+| Quora             |      0.992 |
+| SCIDOCS           |      0.456 |
+| SciFact           |      0.906 |
+| TREC-COVID        |      0.100 |
+| Webis-Touche-2020 |      0.413 |
+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                |      0.650 |
+| German                |      0.528 |
+| Spanish               |      0.602 |
+| Japanese              |      0.614 |
+| Chinese (simplified)  |      0.680 |