sinequa / vectorizer.raspberry

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

Note that the relevance score is computed as an average over 14 retrieval datasets (see details below ).

Inference Times

Gpu Memory usage

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 : 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 ( Paper , GitHub )
• 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 model. In addition to that, this model has been trained on the datasets cited in this paper 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 . Note that all these datasets are in English.

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.