Add metrics on Polish datasets
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clarine
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README.md
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---
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# Model Card for `passage-ranker.pistachio`
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The model was trained and tested in the following languages:
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- Chinese (simplified)
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- Dutch
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- English
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- French
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- German
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- Italian
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- Japanese
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- Polish
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- Portuguese
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Besides the aforementioned languages, basic support can be expected for additional 93 languages that were used during the pretraining of the base model (see
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[list of languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages)).
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## Scores
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| Metric
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| Relevance (NDCG@10) | 0.
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Note that the relevance score is computed as an average over
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[details below](#evaluation-metrics)).
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## Inference Times
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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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| TREC-COVID | 0.651 |
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| Webis-Touche-2020 | 0.312 |
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| Language | NDCG@10 |
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|:----------------------|--------:|
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| Chinese (simplified) | 0.454 |
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| French | 0.439 |
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| German | 0.418 |
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| Japanese | 0.517 |
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- nl
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- pl
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---
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# Model Card for `passage-ranker.pistachio`
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The model was trained and tested in the following languages:
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- English
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- French
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- German
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- Spanish
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- Italian
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- Dutch
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- Japanese
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- Portuguese
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- Chinese (simplified)
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- Polish
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Besides the aforementioned languages, basic support can be expected for additional 93 languages that were used during the pretraining of the base model (see
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[list of languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages)).
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## Scores
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| Metric | Value |
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|:----------------------------|------:|
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| English Relevance (NDCG@10) | 0.474 |
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| Polish Relevance (NDCG@10) | 0.380 |
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Note that the relevance score is computed as an average over several retrieval datasets (see
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[details below](#evaluation-metrics)).
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## Inference Times
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### Evaluation Metrics
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##### English
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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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| TREC-COVID | 0.651 |
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| Webis-Touche-2020 | 0.312 |
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#### Polish
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This model has polish capacities, that are being evaluated over a subset of
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the [PIRBenchmark](https://github.com/sdadas/pirb) with BM25 as the first stage retrieval.
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| Dataset | NDCG@10 |
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|:--------------|--------:|
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| Average | 0.380 |
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| arguana-pl | 0.285 |
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| dbpedia-pl | 0.283 |
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| fiqa-pl | 0.223 |
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| hotpotqa-pl | 0.603 |
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| msmarco-pl | 0.259 |
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| nfcorpus-pl | 0.293 |
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| nq-pl | 0.355 |
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| quora-pl | 0.613 |
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| scidocs-pl | 0.128 |
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| scifact-pl | 0.581 |
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| trec-covid-pl | 0.560 |
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#### Other languages
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We evaluated the model on the datasets of the [MIRACL benchmark](https://github.com/project-miracl/miracl) to test its
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multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics
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for the existing languages.
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| Language | NDCG@10 |
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|:----------------------|--------:|
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| French | 0.439 |
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| German | 0.418 |
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| Spanish | 0.487 |
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| Japanese | 0.517 |
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| Chinese (simplified) | 0.454 |
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