pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
language:
- de
- en
- es
- fr
- it
- nl
- ja
- pt
- zh
- pl
Model Card for vectorizer.guava
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.guava
Supported Languages
The model was trained and tested in the following languages:
- English
- French
- German
- Spanish
- Italian
- Dutch
- Japanese
- Portuguese
- Chinese (simplified)
- Chinese (traditional)
- Polish
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 |
|---|---|
| English Relevance (Recall@100) | 0.616 |
Note that the relevance scores are computed as an average over several retrieval datasets (see details below).
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: 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 first 9 aforementioned languages. It has also been trained on this dataset for polish capacities, and a translated version of msmarco-zh for traditional chinese capacities.
Evaluation Metrics
English
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 | 0.616 |
| Arguana | 0.956 |
| CLIMATE-FEVER | 0.471 |
| DBPedia Entity | 0.379 |
| FEVER | 0.824 |
| FiQA-2018 | 0.642 |
| HotpotQA | 0.579 |
| MS MARCO | 0.85 |
| NFCorpus | 0.289 |
| NQ | 0.765 |
| Quora | 0.993 |
| SCIDOCS | 0.467 |
| SciFact | 0.899 |
| TREC-COVID | 0.104 |
| Webis-Touche-2020 | 0.407 |
Traditional Chinese
This model has traditional chinese capacities, that are being evaluated over the same dev set at msmarco-zh, translated in traditional chinese.
| Dataset | Recall@100 |
|---|---|
| msmarco-zh-traditional | 0.738 |
In comparison, raspberry scores a 0.693 on this dataset.
Other languages
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 | 0.672 |
| German | 0.594 |
| Spanish | 0.632 |
| Japanese | 0.603 |
| Chinese (simplified) | 0.702 |