sinequa
/

passage-ranker.chocolate

@@ -1,94 +1,93 @@
----
-language:
-  - en
----
-# 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
-| Metric              | Value |
-|:--------------------|------:|
-| Relevance (NDCG@10) | 0.484 |
-Note that the relevance score is computed as an average over 14 retrieval datasets (see
-[details below](#evaluation-metrics)).
-## Inference Times
-| GPU                                       | Quantization type |  Batch size 1  |  Batch size 32 |
-|:------------------------------------------|:------------------|---------------:|---------------:|
-| NVIDIA A10                                | FP16              |           1 ms |           5 ms |
-| NVIDIA A10                                | FP32              |           2 ms |          22 ms |
-| NVIDIA T4                                 | FP16              |           1 ms |          13 ms |
-| NVIDIA T4                                 | FP32              |           3 ms |          66 ms |
-| NVIDIA L4                                 | FP16              |           2 ms |           6 ms |
-| NVIDIA L4                                 | FP32              |           3 ms |          30 ms |
-## Gpu Memory usage
-| Quantization type                                |   Memory   |
-|:-------------------------------------------------|-----------:|
-| FP16                                             |    300 MiB |
-| FP32                                             |    550 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](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)
-## Model Details
-### Overview
-- Number of parameters: 23 million
-- Base language model: [MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased)
-  ([Paper](https://arxiv.org/abs/2002.10957), [GitHub](https://github.com/microsoft/unilm/tree/master/minilm))
-- Insensitive to casing and accents
-- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
-### Training Data
-- MS MARCO Passage Ranking
-  ([Paper](https://arxiv.org/abs/1611.09268),
-  [Official Page](https://microsoft.github.io/msmarco/),
-  [dataset on HF hub](https://huggingface.co/datasets/unicamp-dl/mmarco))
-### Evaluation Metrics
-To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
-[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
-| Dataset           | NDCG@10 |
-|:------------------|--------:|
-| Average           |   0.486 |
-|                   |         |
-| Arguana           |   0.554 |
-| CLIMATE-FEVER     |   0.209 |
-| DBPedia Entity    |   0.367 |
-| FEVER             |   0.744 |
-| FiQA-2018         |   0.339 |
-| HotpotQA          |   0.685 |
-| MS MARCO          |   0.412 |
-| NFCorpus          |   0.352 |
-| NQ                |   0.454 |
-| Quora             |   0.818 |
-| SCIDOCS           |   0.158 |
-| SciFact           |   0.658 |
-| TREC-COVID        |   0.674 |
-| Webis-Touche-2020 |   0.345 |

+---
+language:
+  - en
+---
+# 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
+| Metric              | Value |
+|:--------------------|------:|
+| Relevance (NDCG@10) | 0.484 |
+Note that the relevance score is computed as an average over 14 retrieval datasets (see
+[details below](#evaluation-metrics)).
+## Inference Times
+| GPU                                       | Quantization type |  Batch size 1  |  Batch size 32 |
+|:------------------------------------------|:------------------|---------------:|---------------:|
+| NVIDIA A10                                | FP16              |           1 ms |           5 ms |
+| NVIDIA A10                                | FP32              |           2 ms |          22 ms |
+| NVIDIA T4                                 | FP16              |           1 ms |          13 ms |
+| NVIDIA T4                                 | FP32              |           3 ms |          66 ms |
+| NVIDIA L4                                 | FP16              |           2 ms |           6 ms |
+| NVIDIA L4                                 | FP32              |           3 ms |          30 ms |
+## Gpu Memory usage
+| Quantization type                                |   Memory   |
+|:-------------------------------------------------|-----------:|
+| FP16                                             |    300 MiB |
+| FP32                                             |    550 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](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)
+## Model Details
+### Overview
+- Number of parameters: 23 million
+- Base language model: [MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased)
+  ([Paper](https://arxiv.org/abs/2002.10957), [GitHub](https://github.com/microsoft/unilm/tree/master/minilm))
+- Insensitive to casing and accents
+- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
+### Training Data
+- MS MARCO Passage Ranking
+  ([Paper](https://arxiv.org/abs/1611.09268),
+  [Official Page](https://microsoft.github.io/msmarco/),
+  [dataset on HF hub](https://huggingface.co/datasets/unicamp-dl/mmarco))
+### Evaluation Metrics
+To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
+[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
+| Dataset           | NDCG@10 |
+|:------------------|--------:|
+| Average           |   0.486 |
+|                   |         |
+| Arguana           |   0.554 |
+| CLIMATE-FEVER     |   0.209 |
+| DBPedia Entity    |   0.367 |
+| FEVER             |   0.744 |
+| FiQA-2018         |   0.339 |
+| HotpotQA          |   0.685 |
+| MS MARCO          |   0.412 |
+| NFCorpus          |   0.352 |
+| NQ                |   0.454 |
+| Quora             |   0.818 |
+| SCIDOCS           |   0.158 |
+| SciFact           |   0.658 |
+| TREC-COVID        |   0.674 |
+| Webis-Touche-2020 |   0.345 |