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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- feature-extraction |
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- sentence-similarity |
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language: |
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- de |
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- en |
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- es |
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- fr |
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- it |
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- nl |
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- ja |
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- pt |
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- zh |
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--- |
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# Model Card for `vectorizer.raspberry` |
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This model is a vectorizer developed by Sinequa. It produces an embedding vector given a passage or a query. The |
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passage vectors are stored in our vector index and the query vector is used at query time to look up relevant passages |
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in the index. |
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Model name: `vectorizer.raspberry` |
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## Supported Languages |
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The model was trained and tested in the following languages: |
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- English |
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- French |
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- German |
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- Spanish |
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- Italian |
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- Dutch |
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- Japanese |
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- Portuguese |
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- Chinese (simplified) |
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Besides these languages, basic support can be expected for additional 91 languages that were used during the pretraining |
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of the base model (see Appendix A of XLM-R paper). |
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## Scores |
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| Metric | Value | |
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|:-----------------------|------:| |
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| Relevance (Recall@100) | 0.613 | |
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Note that the relevance score is computed as an average over 14 retrieval datasets (see |
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[details below](#evaluation-metrics)). |
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## Inference Times |
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| GPU | Batch size 1 (at query time) | Batch size 32 (at indexing) | |
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|:-----------|-----------------------------:|----------------------------:| |
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| NVIDIA A10 | 2 ms | 19 ms | |
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| NVIDIA T4 | 4 ms | 52 ms | |
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The inference times only measure the time the model takes to process a single batch, it does not include pre- or |
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post-processing steps like the tokenization. |
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## Requirements |
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- Minimal Sinequa version: 11.10.0 |
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- GPU memory usage: 610 MiB |
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Note that GPU memory usage only includes how much GPU memory the actual model consumes on an NVIDIA T4 GPU with a batch |
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size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which |
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can be around 0.5 to 1 GiB depending on the used GPU. |
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## Model Details |
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### Overview |
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- Number of parameters: 107 million |
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- Base language |
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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)) |
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- Insensitive to casing and accents |
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- Output dimensions: 256 (reduced with an additional dense layer) |
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- Training procedure: Query-passage-negative triplets for datasets that have mined hard negative data, Query-passage |
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pairs for the rest. Number of negatives is augmented with in-batch negative strategy |
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### Training Data |
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The model have been trained using all datasets that are cited in |
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the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model. |
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In addition to that, this model has been trained on the datasets cited |
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in [this paper](https://arxiv.org/pdf/2108.13897.pdf) on the 9 aforementioned languages. |
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### Evaluation Metrics |
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To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the |
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[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English. |
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| Dataset | Recall@100 | |
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|:------------------|-----------:| |
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| Average | 0.613 | |
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| Arguana | 0.957 | |
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| CLIMATE-FEVER | 0.468 | |
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| DBPedia Entity | 0.377 | |
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| FEVER | 0.820 | |
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| FiQA-2018 | 0.639 | |
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| HotpotQA | 0.560 | |
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| MS MARCO | 0.845 | |
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| NFCorpus | 0.287 | |
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| NQ | 0.756 | |
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| Quora | 0.992 | |
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| SCIDOCS | 0.456 | |
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| SciFact | 0.906 | |
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| TREC-COVID | 0.100 | |
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| Webis-Touche-2020 | 0.413 | |
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We evaluated the model on the datasets of the [MIRACL benchmark](https://github.com/project-miracl/miracl) to test its |
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multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics |
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for the existing languages. |
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| Language | Recall@100 | |
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|:----------------------|-----------:| |
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| French | 0.650 | |
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| German | 0.528 | |
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| Spanish | 0.602 | |
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| Japanese | 0.614 | |
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| Chinese (simplified) | 0.680 | |
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