sinequa/passage-ranker.chocolate

Model Card for passage-ranker.chocolate

This model is a passage ranker developed by Sinequa. It produces a relevance score given a query-passage pair and is used to order search results.

Model name: passage-ranker.chocolate

Supported Languages

The model was trained and tested in the following languages:

• —English

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
• —CUDA compute capability: above 7.5

Model Details

Overview

• —Number of parameters: 23 million
• —Base language model: MiniLM-L6-H384-uncased (Paper, GitHub)
• —Insensitive to casing and accents
• —Training procedure: MonoBERT

Training Data

• —MS MARCO Passage Ranking (Paper, Official Page, dataset on HF hub)

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.