Initial commit (#1)
Browse files- Model sources and card (d858640c6d58503cc6c9d36590ed5243c5d93920)
- README.md +86 -0
- config.json +24 -0
- pytorch_model.bin +3 -0
- tokenizer.json +0 -0
README.md
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---
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language:
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- en
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---
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# Model Card for `passage-ranker.chocolate`
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This model is a passage ranker developed by Sinequa. It produces a relevance score given a query-passage pair and is
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used to order search results.
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Model name: `passage-ranker.chocolate`
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## Supported Languages
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The model was trained and tested in the following languages:
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- English
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## Scores
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| Metric | Value |
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|:--------------------|------:|
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| Relevance (NDCG@10) | 0.484 |
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Note that the relevance score is computed as an average over 14 retrieval datasets (see
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[details below](#evaluation-metrics)).
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## Inference Times
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| GPU | Batch size 32 |
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|:-----------|--------------:|
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| NVIDIA A10 | 22 ms |
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| NVIDIA T4 | 64 ms |
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The inference times only measure the time the model takes to process a single batch, it does not include pre- or
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post-processing steps like the tokenization.
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## Requirements
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- Minimal Sinequa version: 11.10.0
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- GPU memory usage: 550 MiB
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Note that GPU memory usage only includes how much GPU memory the actual model consumes on an NVIDIA T4 GPU with a batch
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size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which
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can be around 0.5 to 1 GiB depending on the used GPU.
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## Model Details
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### Overview
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- Number of parameters: 23 million
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- Base language model: [MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased)
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([Paper](https://arxiv.org/abs/2002.10957), [GitHub](https://github.com/microsoft/unilm/tree/master/minilm))
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- Insensitive to casing and accents
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- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
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### Training Data
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- MS MARCO Passage Ranking
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([Paper](https://arxiv.org/abs/1611.09268),
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[Official Page](https://microsoft.github.io/msmarco/),
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[dataset on HF hub](https://huggingface.co/datasets/unicamp-dl/mmarco))
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### Evaluation Metrics
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To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
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[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
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| Dataset | NDCG@10 |
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|:------------------|--------:|
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| Average | 0.486 |
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| Arguana | 0.554 |
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| CLIMATE-FEVER | 0.209 |
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| DBPedia Entity | 0.367 |
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| FEVER | 0.744 |
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| FiQA-2018 | 0.339 |
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| HotpotQA | 0.685 |
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| MS MARCO | 0.412 |
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| NFCorpus | 0.352 |
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| NQ | 0.454 |
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| Quora | 0.818 |
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| SCIDOCS | 0.158 |
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| SciFact | 0.658 |
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| TREC-COVID | 0.674 |
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| Webis-Touche-2020 | 0.345 |
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config.json
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{
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 6,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.25.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:86393158c095eeeb286403c07797c391bed615a4cb70983fbb3640199f377c2e
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size 90893101
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tokenizer.json
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