pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
language:
- de
- en
- es
- fr
Model Card for vectorizer-v1-S-multilingual
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-v1-S-multilingual
Supported Languages
The model was trained and tested in the following languages:
- English
- French
- German
- Spanish
Scores
| Metric | Value |
|---|---|
| Relevance (Recall@100) | 0.448 |
Note that the relevance score is computed as an average over 14 retrieval datasets (see details below).
Inference Times
| GPU | Batch size 1 (at query time) | Batch size 32 (at indexing) |
|---|---|---|
| NVIDIA A10 | 2 ms | 14 ms |
| NVIDIA T4 | 4 ms | 51 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: 580 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: 39 million
- Base language model: Homegrown Sinequa BERT-Small (Paper) pretrained in the four supported languages
- Insensitive to casing and accents
- Training procedure: Query-passage pairs using in-batch negatives
Training Data
- Natural Questions
(Paper,
Official Page)
- 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. Note that all these datasets are in English.
| Dataset | Recall@100 |
|---|---|
| Average | 0.448 |
| Arguana | 0.835 |
| CLIMATE-FEVER | 0.350 |
| DBPedia Entity | 0.287 |
| FEVER | 0.645 |
| FiQA-2018 | 0.305 |
| HotpotQA | 0.396 |
| MS MARCO | 0.533 |
| NFCorpus | 0.162 |
| NQ | 0.701 |
| Quora | 0.947 |
| SCIDOCS | 0.194 |
| SciFact | 0.580 |
| TREC-COVID | 0.051 |
| Webis-Touche-2020 | 0.289 |
We evaluated the model on the datasets of the MIRACL benchmark 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 | 0.583 |
| German | 0.524 |
| Spanish | 0.483 |