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
- CUDA compute capability: above 7.5
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 |
