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-{"title": "Polish Information Retrieval Benchmark", "description": "Polish Information Retrieval Benchmark (PIRB) covers 41 Polish multidomain information retrieval tasks. Its purpose is to evaluate Polish and multilingual information retrieval methods on a wide range of problems with different characteristics, thus testing  the generalization ability of the models and their zero-shot performance. The benchmark includes pre-existing datasets such as MaupQA, BEIR-PL and PolEval-2022. We have also added new, previously unpublished datasets. The \"Web Datasets\" group contains real questions and answers from Polish web services.", "options": {"closeable": true, "showHelp": true, "showFooter": false}, "metrics": [{"id": "NDCG@10", "description": "Normalized Discounted Cumulative Gain at 10", "greaterIsBetter": true}, {"id": "MRR@10", "description": "Mean reciprocal rank at 10", "greaterIsBetter": true}, {"id": "Recall@100", "description": "Recall for the first 100 results", "greaterIsBetter": true}, {"id": "Accuracy@1", "description": "Is the first result relevant?", "greaterIsBetter": true}], "filters": [], "taskGroups": [{"id": "PolEval-2022", "description": "Tasks from the PolEval 2022 Passage Retrieval challenge. The evaluation includes all data splits except training.", "url": "https://beta.poleval.pl/challenge/2022-passage-retrieval"}, {"id": "Web Datasets", "description": "Question answering datasets crawled from various web sources."}, {"id": "BEIR-PL", "description": "Retrieval tasks from BEIR benchmark translated to Polish using Google Translate. Created by Wrocław University of Science and Technology.", "url": "https://arxiv.org/abs/2305.19840"}, {"id": "MaupQA", "description": "Massive Automatically-created Polish Question Answering Dataset, published by Piotr Rybak from IPI PAN.", "url": "https://aclanthology.org/2023.bsnlp-1.2/"}, {"id": "Other", "description": "Other datasets."}], "tasks": [{"id": "maupqa-1z10", "name": "1z10", "groupId": "MaupQA"}, {"id": "maupqa-czy-wiesz-v2", "name": "czy-wiesz-v2", "groupId": "MaupQA"}, {"id": "maupqa-gpt3-cc", "name": "gpt3-cc", "groupId": "MaupQA"}, {"id": "maupqa-gpt3.5-cc", "name": "gpt3.5-cc", "groupId": "MaupQA"}, {"id": "maupqa-gpt3.5-wiki", "name": "gpt3.5-wiki", "groupId": "MaupQA"}, {"id": "maupqa-mkqa", "name": "mkqa", "groupId": "MaupQA"}, {"id": "maupqa-mqa", "name": "mqa", "groupId": "MaupQA"}, {"id": "maupqa-multilingual-NLI", "name": "multilingual-NLI", "groupId": "MaupQA"}, {"id": "maupqa-poleval2021-pairs", "name": "poleval2021\npairs", "groupId": "MaupQA"}, {"id": "maupqa-poquad", "name": "poquad", "groupId": "MaupQA"}, {"id": "maupqa-templates", "name": "templates", "groupId": "MaupQA"}, {"id": "maupqa-wiki-def", "name": "wiki-def", "groupId": "MaupQA"}, {"id": "poleval-2022-dev-0-wiki-trivia", "name": "dev-0\n(wiki-trivia)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-A-wiki-trivia", "name": "test-A\n(wiki-trivia)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-A-legal-questions", "name": "test-A\n(legal-questions)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-A-allegro-faq", "name": "test-A\n(allegro-faq)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-B-wiki-trivia", "name": "test-B\n(wiki-trivia)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-B-legal-questions", "name": "test-B\n(legal-questions)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-B-allegro-faq", "name": "test-B\n(allegro-faq)", "groupId": "PolEval-2022"}, {"id": "arguana-pl", "name": "arguana", "groupId": "BEIR-PL"}, {"id": "dbpedia-pl", "name": "dbpedia", "groupId": "BEIR-PL"}, {"id": "fiqa-pl", "name": "fiqa", "groupId": "BEIR-PL"}, {"id": "hotpotqa-pl", "name": "hotpotqa", "groupId": "BEIR-PL"}, {"id": "msmarco-pl", "name": "msmarco", "groupId": "BEIR-PL"}, {"id": "nfcorpus-pl", "name": "nfcorpus", "groupId": "BEIR-PL"}, {"id": "nq-pl", "name": "nq", "groupId": "BEIR-PL"}, {"id": "quora-pl", "name": "quora", "groupId": "BEIR-PL"}, {"id": "scidocs-pl", "name": "scidocs", "groupId": "BEIR-PL"}, {"id": "scifact-pl", "name": "scifact", "groupId": "BEIR-PL"}, {"id": "trec-covid-pl", "name": "trec-covid", "groupId": "BEIR-PL"}, {"id": "gemini", "name": "gemini", "groupId": "Web Datasets"}, {"id": "odi", "name": "odi", "groupId": "Web Datasets"}, {"id": "onet", "name": "onet", "groupId": "Web Datasets"}, {"id": "zapytajfizyka", "name": "zapytajfizyka", "groupId": "Web Datasets"}, {"id": "techpedia", "name": "techpedia", "groupId": "Web Datasets"}, {"id": "pwn", "name": "pwn", "groupId": "Web Datasets"}, {"id": "eprawnik", "name": "eprawnik", "groupId": "Web Datasets"}, {"id": "specprawnik", "name": "specprawnik", "groupId": "Web Datasets"}, {"id": "abczdrowie", "name": "abczdrowie", "groupId": "Web Datasets"}, {"id": "mfaq", "name": "MFAQ", "groupId": "Other"}, {"id": "gpt-exams", "name": "GPT-exams", "groupId": "Other"}], "models": [{"id": "splade+mmlw-retrieval-roberta-large", "name": "SPLADE++ & roberta-large", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and polish-roberta-large-v2 (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+mmlw-retrieval-roberta-base", "name": "SPLADE++ & roberta-base", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and polish-roberta-base-v2 (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+mmlw-retrieval-roberta-large", "name": "BM25 & roberta-large", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and polish-roberta-large-v2 (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+mmlw-retrieval-roberta-base", "name": "BM25 & roberta-base", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and polish-roberta-base-v2 (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+mmlw-retrieval-e5-large", "name": "SPLADE++ & e5-large", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and e5-large (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+mmlw-retrieval-e5-base", "name": "SPLADE++ & e5-base", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and e5-base (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+mmlw-retrieval-e5-small", "name": "SPLADE++ & e5-small", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and e5-small (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+mmlw-retrieval-e5-large", "name": "BM25 & e5-large", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and e5-large (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+mmlw-retrieval-e5-base", "name": "BM25 & e5-base", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and e5-base (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+mmlw-retrieval-e5-small", "name": "BM25 & e5-small", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and e5-small (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+retrieval-e5-large", "name": "SPLADE++ & intfloat/multilingual-e5-large", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and intfloat/multilingual-e5-large. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+retrieval-e5-base", "name": "SPLADE++ & intfloat/multilingual-e5-base", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and intfloat/multilingual-e5-base. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+retrieval-e5-small", "name": "SPLADE++ & intfloat/multilingual-e5-small", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and intfloat/multilingual-e5-small. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+retrieval-e5-large", "name": "BM25 & intfloat/multilingual-e5-large", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and intfloat/multilingual-e5-large. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+retrieval-e5-base", "name": "BM25 & intfloat/multilingual-e5-base", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and intfloat/multilingual-e5-base. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+retrieval-e5-small", "name": "BM25 & intfloat/multilingual-e5-small", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and intfloat/multilingual-e5-small. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "./models/mmlw/mmlw-roberta-base", "name": "roberta-base (distilled)", "description": "polish-roberta-base-v2 distilled from bge-base-en using a bilingual corpus of 60 million text pairs."}, {"id": "./models/mmlw/mmlw-roberta-large", "name": "roberta-large (distilled)", "description": "polish-roberta-large-v2 distilled from bge-large-en using a bilingual corpus of 60 million text pairs."}, {"id": "./models/mmlw/mmlw-e5-small", "name": "e5-small (distilled)", "description": "multilingual-e5-small distilled from bge-small-en using a bilingual corpus of 60 million text pairs."}, {"id": "./models/mmlw/mmlw-e5-base", "name": "e5-base (distilled)", "description": "multilingual-e5-base distilled from bge-base-en using a bilingual corpus of 60 million text pairs."}, {"id": "./models/mmlw/mmlw-e5-large", "name": "e5-large (distilled)", "description": "multilingual-e5-large distilled from bge-large-en using a bilingual corpus of 60 million text pairs."}, {"id": "./models/mmlw/mmlw-retrieval-roberta-base", "name": "sdadas/mmlw-retrieval-roberta-base", "description": "polish-roberta-base-v2 distilled from bge-base-en using a bilingual corpus of 60 million text pairs and then fine-tuned with contrastive loss on Polish MS MARCO training split.", "url": "https://huggingface.co/sdadas/mmlw-retrieval-roberta-base"}, {"id": "./models/mmlw/mmlw-retrieval-roberta-large", "name": "sdadas/mmlw-retrieval-roberta-large", "description": "polish-roberta-large-v2 distilled from bge-large-en using a bilingual corpus of 60 million text pairs and then fine-tuned with contrastive loss on Polish MS MARCO training split.", "url": "https://huggingface.co/sdadas/mmlw-retrieval-roberta-large"}, {"id": "./models/mmlw/mmlw-retrieval-e5-small", "name": "sdadas/mmlw-retrieval-e5-small", "description": "multilingual-e5-small distilled from bge-small-en using a bilingual corpus of 60 million text pairs and then fine-tuned with contrastive loss on Polish MS MARCO training split.", "url": "https://huggingface.co/sdadas/mmlw-retrieval-e5-small"}, {"id": "./models/mmlw/mmlw-retrieval-e5-base", "name": "sdadas/mmlw-retrieval-e5-base", "description": "multilingual-e5-base distilled from bge-base-en using a bilingual corpus of 60 million text pairs and then fine-tuned with contrastive loss on Polish MS MARCO training split.", "url": "https://huggingface.co/sdadas/mmlw-retrieval-e5-base"}, {"id": "./models/mmlw/mmlw-retrieval-e5-large", "name": "sdadas/mmlw-retrieval-e5-large", "description": "multilingual-e5-large distilled from bge-large-en using a bilingual corpus of 60 million text pairs and then fine-tuned with contrastive loss on Polish MS MARCO training split.", "url": "https://huggingface.co/sdadas/mmlw-retrieval-e5-large"}, {"id": "./models/mmlw/retrieval-e5-small", "name": "e5-small (fine-tuned)", "description": "multilingual-e5-small fine-tuned with contrastive loss on Polish MS MARCO training split."}, {"id": "./models/mmlw/retrieval-e5-base", "name": "e5-base (fine-tuned)", "description": "multilingual-e5-base fine-tuned with contrastive loss on Polish MS MARCO training split."}, {"id": "./models/mmlw/retrieval-e5-large", "name": "e5-large (fine-tuned)", "description": "multilingual-e5-large fine-tuned with contrastive loss on Polish MS MARCO training split."}, {"id": "sparse", "name": "BM25", "description": "BM25 retrieval implementation based on pyserini", "url": "https://github.com/castorini/pyserini"}, {"id": "bm25", "name": "BM25", "description": "BM25 retrieval implementation based on pyserini", "url": "https://github.com/castorini/pyserini"}, {"id": "./models/polish-splade", "name": "SPLADE++", "description": "SPLADE model for Polish trained using SPLADE++ EnsembleDistil method. We utilised polish-distilroberta as the base model.", "url": "https://huggingface.co/sdadas/polish-splade"}, {"id": "intfloat/multilingual-e5-small", "url": "https://huggingface.co/intfloat/multilingual-e5-small"}, {"id": "intfloat/multilingual-e5-base", "url": "https://huggingface.co/intfloat/multilingual-e5-base"}, {"id": "intfloat/multilingual-e5-large", "url": "https://huggingface.co/intfloat/multilingual-e5-large"}, {"id": "ipipan/herbert-base-retrieval-v1", "url": "https://huggingface.co/ipipan/herbert-base-retrieval-v1", "warning": "The model was trained on MaupQA datasets, so the results reported for them may be overestimated and unreliable. \nYou can use the ⮾ button to disable the results for a specific task group."}, {"id": "ipipan/herbert-base-retrieval-v2", "url": "https://huggingface.co/ipipan/herbert-base-retrieval-v2", "warning": "The model was trained on MaupQA datasets, so the results reported for them may be overestimated and unreliable. \nYou can use the ⮾ button to disable the results for a specific task group."}, {"id": "ipipan/silver-retriever-base-v1", "url": "https://huggingface.co/ipipan/silver-retriever-base-v1", "warning": "The model was trained on MaupQA datasets, so the results reported for them may be overestimated and unreliable. \nYou can use the ⮾ button to disable the results for a specific task group."}, {"id": "ipipan/herbert-base-qa-v1", "url": "https://huggingface.co/ipipan/herbert-base-qa-v1", "warning": "The model was trained on MaupQA datasets, so the results reported for them may be overestimated and unreliable."}, {"id": "./models/baseline-mnr", "name": "MNR baseline (base)", "description": "A baseline model (polish-roberta-base-v2) fine-tuned on MSMarco using MNR training script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py"}, {"id": "./models/baseline-mnr-large", "name": "MNR baseline (large)", "description": "A baseline model (polish-roberta-large-v2) fine-tuned on MSMarco using MNR training script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py"}, {"id": "./models/baseline-mse", "name": "MSE baseline (base)", "description": "A baseline model (polish-roberta-base-v2) fine-tuned on MSMarco using MSE training script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py"}, {"id": "./models/baseline-mse-large", "name": "MSE baseline (large)", "description": "A baseline model (polish-roberta-large-v2) fine-tuned on MSMarco using MSE training script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py"}, {"id": "./models/baseline-poleval2022-base", "name": "poleval-2022-base-model (mkozlowski)", "description": "A base model trained by Marek Kozłowski for his PolEval-2022 Passage Retrieval submission.", "url": "https://beta.poleval.pl/challenge/2022-passage-retrieval"}, {"id": "./models/mnr-large-v6-poleval2022", "name": "poleval-2022-large-model (mkozlowski)", "description": "A large model trained by Marek Kozłowski for his PolEval-2022 Passage Retrieval submission.", "url": "https://beta.poleval.pl/challenge/2022-passage-retrieval"}, {"id": "LaBSE", "warning": "The model was trained for bitext mining, so it's probably not suited for information retrieval.", "url": "https://huggingface.co/sentence-transformers/LaBSE"}, {"id": "distiluse-base-multilingual-cased-v2", "url": "https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2"}, {"id": "paraphrase-multilingual-MiniLM-L12-v2", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"}, {"id": "paraphrase-multilingual-mpnet-base-v2", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2"}, {"id": "xlm-r-bert-base-nli-stsb-mean-tokens", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens"}, {"id": "xlm-r-distilroberta-base-paraphrase-v1", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1"}, {"id": "Voicelab/sbert-base-cased-pl", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/Voicelab/sbert-base-cased-pl"}, {"id": "Voicelab/sbert-large-cased-pl", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/Voicelab/sbert-large-cased-pl"}, {"id": "radlab/polish-sts-v2", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/radlab/polish-sts-v2"}, {"id": "sdadas/st-polish-paraphrase-from-mpnet", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet"}, {"id": "sdadas/st-polish-paraphrase-from-distilroberta", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/sdadas/st-polish-paraphrase-from-distilroberta"}], "results": [{"id": "bm25", "tags": ["standalone"], "results": {"gemini": {"Accuracy@1": 63.23529411764706, "Recall@100": 99.63235294117648, "MRR@10": 73.69149743230626, "NDCG@10": 78.3248260027549}, "odi": {"Accuracy@1": 63.36429308565531, "Recall@100": 91.22151784690482, "MRR@10": 71.01651186790505, "NDCG@10": 69.08801869530248}, "onet": {"Accuracy@1": 29.54400073106095, "Recall@100": 66.14273965091839, "MRR@10": 36.309632598356146, "NDCG@10": 39.89454435827909}, "zapytajfizyka": {"Accuracy@1": 59.47441217150761, "Recall@100": 96.61134163208852, "MRR@10": 67.77009374519757, "NDCG@10": 72.0657769671022}, "techpedia": {"Accuracy@1": 46.12710483432917, "Recall@100": 79.31558935361217, "MRR@10": 52.609227214333195, "NDCG@10": 55.80039285334023}, "pwn": {"Accuracy@1": 51.07736000493919, "Recall@100": 80.4655183058591, "MRR@10": 56.92787759050032, "NDCG@10": 59.719140350855724}, "eprawnik": {"Accuracy@1": 22.173139967772407, "Recall@100": 75.93209979441018, "MRR@10": 29.746880211958217, "NDCG@10": 34.10027937798345}, "specprawnik": {"Accuracy@1": 6.5531277951189235, "Recall@100": 28.85210740558206, "MRR@10": 9.164852872618042, "NDCG@10": 9.800796752741524}, "abczdrowie": {"Accuracy@1": 4.604222987266905, "Recall@100": 19.741084652667706, "MRR@10": 6.2974606765834125, "NDCG@10": 6.668492560182432}, "maupqa-1z10": {"Accuracy@1": 29.898348840961653, "Recall@100": 66.9149679987092, "MRR@10": 39.013084374644734, "NDCG@10": 42.31884377184016}, "maupqa-czy-wiesz-v2": {"Accuracy@1": 59.55017539032946, "Recall@100": 88.83348235779627, "MRR@10": 66.1626828552002, "NDCG@10": 69.39198712669831}, "maupqa-gpt3-cc": {"Accuracy@1": 15.345949142519219, "Recall@100": 20.89953347788948, "MRR@10": 16.421363176981156, "NDCG@10": 16.934937368256303}, "maupqa-gpt3.5-cc": {"Accuracy@1": 16.275218816532053, "Recall@100": 21.463310916607526, "MRR@10": 17.35369049898805, "NDCG@10": 17.860970197315385}, 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64.05098693056527, "MRR@10": 39.81068997634326, "NDCG@10": 42.073695314955415}, "maupqa-czy-wiesz-v2": {"Accuracy@1": 65.78169062521494, "Recall@100": 92.1624595914437, "MRR@10": 72.30568138023993, "NDCG@10": 75.26143777094441}, "maupqa-gpt3-cc": {"Accuracy@1": 14.409619554504237, "Recall@100": 20.779617583284054, "MRR@10": 15.612634895603549, "NDCG@10": 16.225979050229594}, "maupqa-gpt3.5-cc": {"Accuracy@1": 16.244804163428068, "Recall@100": 21.86137676996384, "MRR@10": 17.409297818459383, "NDCG@10": 17.947442158422156}, "maupqa-gpt3.5-wiki": {"Accuracy@1": 76.60578284019681, "Recall@100": 94.84397115319808, "MRR@10": 81.16750701538712, "NDCG@10": 83.2064072876311}, "maupqa-mkqa": {"Accuracy@1": 45.640120967741936, "Recall@100": 85.68548387096774, "MRR@10": 54.80398745519703, "NDCG@10": 59.29082274906483}, "maupqa-mqa": {"Accuracy@1": 13.579482311539174, "Recall@100": 35.63937715304288, "MRR@10": 17.179576951713397, "NDCG@10": 19.013396523849423}, "maupqa-multilingual-NLI": {"Accuracy@1": 10.861325115562403, "Recall@100": 20.813396678650935, "MRR@10": 13.211438843642124, "NDCG@10": 14.25013978437439}, "maupqa-poleval2021-pairs": {"Accuracy@1": 69.4135115070527, "Recall@100": 95.26107399158623, "MRR@10": 77.4867135668918, "NDCG@10": 78.99953513344492}, "maupqa-poquad": {"Accuracy@1": 9.726825675353654, "Recall@100": 28.868525378566325, "MRR@10": 12.746477735104426, "NDCG@10": 14.345592889930984}, "maupqa-templates": {"Accuracy@1": 63.1276183330207, "Recall@100": 84.79334708935158, "MRR@10": 68.47192075104313, "NDCG@10": 70.90001038001975}, "maupqa-wiki-def": {"Accuracy@1": 42.54684131984745, "Recall@100": 85.27054662023987, "MRR@10": 51.925477186213364, "NDCG@10": 56.577047414995526}, "poleval-2022-dev-0-wiki-trivia": {"Accuracy@1": 38.73121869782972, "Recall@100": 59.8135781858654, "MRR@10": 49.151694623313965, "NDCG@10": 33.18806847951395}, "poleval-2022-test-A-wiki-trivia": {"Accuracy@1": 38.25, "Recall@100": 57.8625, "MRR@10": 49.01021825396824, "NDCG@10": 33.609358384436945}, "poleval-2022-test-A-legal-questions": {"Accuracy@1": 65.75, "Recall@100": 92.72440476190478, "MRR@10": 75.7939484126984, "NDCG@10": 72.31372449566328}, "poleval-2022-test-A-allegro-faq": {"Accuracy@1": 54.5, "Recall@100": 96.25, "MRR@10": 66.19434523809521, "NDCG@10": 71.19637189257574}, "poleval-2022-test-B-wiki-trivia": {"Accuracy@1": 39.50617283950617, "Recall@100": 58.56528245417134, "MRR@10": 50.24829422977565, "NDCG@10": 33.370451592976536}, "poleval-2022-test-B-legal-questions": {"Accuracy@1": 65.09433962264151, "Recall@100": 92.67894579215333, "MRR@10": 74.58545472696414, "NDCG@10": 70.65992469982557}, "poleval-2022-test-B-allegro-faq": {"Accuracy@1": 54.8, "Recall@100": 93.8, "MRR@10": 65.65373015873016, "NDCG@10": 70.39111996657964}, "arguana-pl": {"Accuracy@1": 26.7425320056899, "Recall@100": 98.0796586059744, "MRR@10": 42.875488270225105, "NDCG@10": 51.97210303658613}, "dbpedia-pl": {"Accuracy@1": 53.25, "Recall@100": 40.722505816587656, "MRR@10": 62.50436507936508, "NDCG@10": 32.15194668867302}, "fiqa-pl": {"Accuracy@1": 30.246913580246915, "Recall@100": 60.01243612354725, "MRR@10": 38.314410640799515, "NDCG@10": 31.128760840619563}, "hotpotqa-pl": {"Accuracy@1": 69.9392302498312, "Recall@100": 74.01080351114112, "MRR@10": 76.74373278458351, "NDCG@10": 59.67683151615295}, "msmarco-pl": {"Accuracy@1": 17.679083094555875, "Recall@100": 78.16499522445079, "MRR@10": 27.66932732978564, "NDCG@10": 32.93223639467684}, "nfcorpus-pl": {"Accuracy@1": 40.86687306501548, "Recall@100": 29.066476369961343, "MRR@10": 49.50120399036806, "NDCG@10": 31.40416317107943}, "nq-pl": {"Accuracy@1": 26.100811123986094, "Recall@100": 83.95374662031672, "MRR@10": 38.04889964869685, "NDCG@10": 42.38341099683887}, "quora-pl": {"Accuracy@1": 73.85, "Recall@100": 98.49586248087125, "MRR@10": 81.4185317460313, "NDCG@10": 82.49141107148891}, "scidocs-pl": {"Accuracy@1": 18.3, "Recall@100": 36.49499999999991, "MRR@10": 27.114087301587286, "NDCG@10": 15.434951711782341}, "scifact-pl": {"Accuracy@1": 54.0, "Recall@100": 88.53333333333335, "MRR@10": 62.30013227513227, "NDCG@10": 65.34853686972808}, "trec-covid-pl": {"Accuracy@1": 78.0, "Recall@100": 11.883102472796576, "MRR@10": 87.56666666666666, "NDCG@10": 63.316178260281696}, "mfaq": {"Accuracy@1": 53.39233972481277, "Recall@100": 78.9825355859208, "MRR@10": 59.32502046424418, "NDCG@10": 61.91858894359377}, "gpt-exams": {"Accuracy@1": 96.13823637928914, "Recall@100": 100.0, "MRR@10": 97.88015882776666, "NDCG@10": 98.40963067829725}}}]}
\ No newline at end of file
+{"title": "Polish Information Retrieval Benchmark", "description": "Polish Information Retrieval Benchmark (PIRB) covers 41 Polish multidomain information retrieval tasks. Its purpose is to evaluate Polish and multilingual information retrieval methods on a wide range of problems with different characteristics, thus testing  the generalization ability of the models and their zero-shot performance. The benchmark includes pre-existing datasets such as MaupQA, BEIR-PL and PolEval-2022. We have also added new, previously unpublished datasets. The \"Web Datasets\" group contains real questions and answers from Polish web services.", "options": {"closeable": true, "showHelp": true, "showFooter": true}, "metrics": [{"id": "NDCG@10", "description": "Normalized Discounted Cumulative Gain at 10", "greaterIsBetter": true}, {"id": "MRR@10", "description": "Mean reciprocal rank at 10", "greaterIsBetter": true}, {"id": "Recall@100", "description": "Recall for the first 100 results", "greaterIsBetter": true}, {"id": "Accuracy@1", "description": "Is the first result relevant?", "greaterIsBetter": true}], "filters": [], "taskGroups": [{"id": "PolEval-2022", "description": "Tasks from the PolEval 2022 Passage Retrieval challenge. The evaluation includes all data splits except training.", "url": "https://beta.poleval.pl/challenge/2022-passage-retrieval"}, {"id": "Web Datasets", "description": "Question answering datasets crawled from various web sources."}, {"id": "BEIR-PL", "description": "Retrieval tasks from BEIR benchmark translated to Polish using Google Translate. Created by Wrocław University of Science and Technology.", "url": "https://arxiv.org/abs/2305.19840"}, {"id": "MaupQA", "description": "Massive Automatically-created Polish Question Answering Dataset, published by Piotr Rybak from IPI PAN.", "url": "https://aclanthology.org/2023.bsnlp-1.2/"}, {"id": "Other", "description": "Other datasets."}], "tasks": [{"id": "maupqa-1z10", "name": "1z10", "groupId": "MaupQA"}, {"id": "maupqa-czy-wiesz-v2", "name": "czy-wiesz-v2", "groupId": "MaupQA"}, {"id": "maupqa-gpt3-cc", "name": "gpt3-cc", "groupId": "MaupQA"}, {"id": "maupqa-gpt3.5-cc", "name": "gpt3.5-cc", "groupId": "MaupQA"}, {"id": "maupqa-gpt3.5-wiki", "name": "gpt3.5-wiki", "groupId": "MaupQA"}, {"id": "maupqa-mkqa", "name": "mkqa", "groupId": "MaupQA"}, {"id": "maupqa-mqa", "name": "mqa", "groupId": "MaupQA"}, {"id": "maupqa-multilingual-NLI", "name": "multilingual-NLI", "groupId": "MaupQA"}, {"id": "maupqa-poleval2021-pairs", "name": "poleval2021\npairs", "groupId": "MaupQA"}, {"id": "maupqa-poquad", "name": "poquad", "groupId": "MaupQA"}, {"id": "maupqa-templates", "name": "templates", "groupId": "MaupQA"}, {"id": "maupqa-wiki-def", "name": "wiki-def", "groupId": "MaupQA"}, {"id": "poleval-2022-dev-0-wiki-trivia", "name": "dev-0\n(wiki-trivia)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-A-wiki-trivia", "name": "test-A\n(wiki-trivia)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-A-legal-questions", "name": "test-A\n(legal-questions)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-A-allegro-faq", "name": "test-A\n(allegro-faq)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-B-wiki-trivia", "name": "test-B\n(wiki-trivia)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-B-legal-questions", "name": "test-B\n(legal-questions)", "groupId": "PolEval-2022"}, {"id": "poleval-2022-test-B-allegro-faq", "name": "test-B\n(allegro-faq)", "groupId": "PolEval-2022"}, {"id": "arguana-pl", "name": "arguana", "groupId": "BEIR-PL"}, {"id": "dbpedia-pl", "name": "dbpedia", "groupId": "BEIR-PL"}, {"id": "fiqa-pl", "name": "fiqa", "groupId": "BEIR-PL"}, {"id": "hotpotqa-pl", "name": "hotpotqa", "groupId": "BEIR-PL"}, {"id": "msmarco-pl", "name": "msmarco", "groupId": "BEIR-PL"}, {"id": "nfcorpus-pl", "name": "nfcorpus", "groupId": "BEIR-PL"}, {"id": "nq-pl", "name": "nq", "groupId": "BEIR-PL"}, {"id": "quora-pl", "name": "quora", "groupId": "BEIR-PL"}, {"id": "scidocs-pl", "name": "scidocs", "groupId": "BEIR-PL"}, {"id": "scifact-pl", "name": "scifact", "groupId": "BEIR-PL"}, {"id": "trec-covid-pl", "name": "trec-covid", "groupId": "BEIR-PL"}, {"id": "gemini", "name": "gemini", "groupId": "Web Datasets"}, {"id": "odi", "name": "odi", "groupId": "Web Datasets"}, {"id": "onet", "name": "onet", "groupId": "Web Datasets"}, {"id": "zapytajfizyka", "name": "zapytajfizyka", "groupId": "Web Datasets"}, {"id": "techpedia", "name": "techpedia", "groupId": "Web Datasets"}, {"id": "pwn", "name": "pwn", "groupId": "Web Datasets"}, {"id": "eprawnik", "name": "eprawnik", "groupId": "Web Datasets"}, {"id": "specprawnik", "name": "specprawnik", "groupId": "Web Datasets"}, {"id": "abczdrowie", "name": "abczdrowie", "groupId": "Web Datasets"}, {"id": "mfaq", "name": "MFAQ", "groupId": "Other"}, {"id": "gpt-exams", "name": "GPT-exams", "groupId": "Other"}], "models": [{"id": "splade+mmlw-retrieval-roberta-large", "name": "SPLADE++ & roberta-large", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and polish-roberta-large-v2 (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+mmlw-retrieval-roberta-base", "name": "SPLADE++ & roberta-base", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and polish-roberta-base-v2 (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+mmlw-retrieval-roberta-large", "name": "BM25 & roberta-large", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and polish-roberta-large-v2 (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+mmlw-retrieval-roberta-base", "name": "BM25 & roberta-base", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and polish-roberta-base-v2 (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+mmlw-retrieval-e5-large", "name": "SPLADE++ & e5-large", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and e5-large (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+mmlw-retrieval-e5-base", "name": "SPLADE++ & e5-base", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and e5-base (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+mmlw-retrieval-e5-small", "name": "SPLADE++ & e5-small", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and e5-small (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+mmlw-retrieval-e5-large", "name": "BM25 & e5-large", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and e5-large (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+mmlw-retrieval-e5-base", "name": "BM25 & e5-base", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and e5-base (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+mmlw-retrieval-e5-small", "name": "BM25 & e5-small", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and e5-small (distilled + fine-tuned). Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+retrieval-e5-large", "name": "SPLADE++ & intfloat/multilingual-e5-large", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and intfloat/multilingual-e5-large. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+retrieval-e5-base", "name": "SPLADE++ & intfloat/multilingual-e5-base", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and intfloat/multilingual-e5-base. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "splade+retrieval-e5-small", "name": "SPLADE++ & intfloat/multilingual-e5-small", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and intfloat/multilingual-e5-small. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+retrieval-e5-large", "name": "BM25 & intfloat/multilingual-e5-large", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and intfloat/multilingual-e5-large. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+retrieval-e5-base", "name": "BM25 & intfloat/multilingual-e5-base", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and intfloat/multilingual-e5-base. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "bm25+retrieval-e5-small", "name": "BM25 & intfloat/multilingual-e5-small", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and intfloat/multilingual-e5-small. Results from both indexes are aggregated using an additional XGBRanker model."}, {"id": "sdadas/mmlw-roberta-base", "name": "sdadas/mmlw-roberta-base", "description": "polish-roberta-base-v2 distilled from bge-base-en using a bilingual corpus of 60 million text pairs.", "url": "https://huggingface.co/sdadas/mmlw-roberta-base"}, {"id": "sdadas/mmlw-roberta-large", "name": "sdadas/mmlw-roberta-large", "description": "polish-roberta-large-v2 distilled from bge-large-en using a bilingual corpus of 60 million text pairs.", "url": "https://huggingface.co/sdadas/mmlw-roberta-large"}, {"id": "sdadas/mmlw-e5-small", "name": "sdadas/mmlw-e5-small", "description": "multilingual-e5-small distilled from bge-small-en using a bilingual corpus of 60 million text pairs.", "url": "https://huggingface.co/sdadas/mmlw-e5-small"}, {"id": "sdadas/mmlw-e5-base", "name": "sdadas/mmlw-e5-base", "description": "multilingual-e5-base distilled from bge-base-en using a bilingual corpus of 60 million text pairs.", "url": "https://huggingface.co/sdadas/mmlw-e5-base"}, {"id": "sdadas/mmlw-e5-large", "name": "sdadas/mmlw-e5-large", "description": "multilingual-e5-large distilled from bge-large-en using a bilingual corpus of 60 million text pairs.", "url": "https://huggingface.co/sdadas/mmlw-e5-large"}, {"id": "./models/mmlw/mmlw-retrieval-roberta-base", "name": "sdadas/mmlw-retrieval-roberta-base", "description": "polish-roberta-base-v2 distilled from bge-base-en using a bilingual corpus of 60 million text pairs and then fine-tuned with contrastive loss on Polish MS MARCO training split.", "url": "https://huggingface.co/sdadas/mmlw-retrieval-roberta-base"}, {"id": "./models/mmlw/mmlw-retrieval-roberta-large", "name": "sdadas/mmlw-retrieval-roberta-large", "description": "polish-roberta-large-v2 distilled from bge-large-en using a bilingual corpus of 60 million text pairs and then fine-tuned with contrastive loss on Polish MS MARCO training split.", "url": "https://huggingface.co/sdadas/mmlw-retrieval-roberta-large"}, {"id": "./models/mmlw/mmlw-retrieval-e5-small", "name": "sdadas/mmlw-retrieval-e5-small", "description": "multilingual-e5-small distilled from bge-small-en using a bilingual corpus of 60 million text pairs and then fine-tuned with contrastive loss on Polish MS MARCO training split.", "url": "https://huggingface.co/sdadas/mmlw-retrieval-e5-small"}, {"id": "./models/mmlw/mmlw-retrieval-e5-base", "name": "sdadas/mmlw-retrieval-e5-base", "description": "multilingual-e5-base distilled from bge-base-en using a bilingual corpus of 60 million text pairs and then fine-tuned with contrastive loss on Polish MS MARCO training split.", "url": "https://huggingface.co/sdadas/mmlw-retrieval-e5-base"}, {"id": "./models/mmlw/mmlw-retrieval-e5-large", "name": "sdadas/mmlw-retrieval-e5-large", "description": "multilingual-e5-large distilled from bge-large-en using a bilingual corpus of 60 million text pairs and then fine-tuned with contrastive loss on Polish MS MARCO training split.", "url": "https://huggingface.co/sdadas/mmlw-retrieval-e5-large"}, {"id": "./models/mmlw/retrieval-e5-small", "name": "e5-small (fine-tuned)", "description": "multilingual-e5-small fine-tuned with contrastive loss on Polish MS MARCO training split."}, {"id": "./models/mmlw/retrieval-e5-base", "name": "e5-base (fine-tuned)", "description": "multilingual-e5-base fine-tuned with contrastive loss on Polish MS MARCO training split."}, {"id": "./models/mmlw/retrieval-e5-large", "name": "e5-large (fine-tuned)", "description": "multilingual-e5-large fine-tuned with contrastive loss on Polish MS MARCO training split."}, {"id": "sparse", "name": "BM25", "description": "BM25 retrieval implementation based on pyserini", "url": "https://github.com/castorini/pyserini"}, {"id": "bm25", "name": "BM25", "description": "BM25 retrieval implementation based on pyserini", "url": "https://github.com/castorini/pyserini"}, {"id": "./models/polish-splade", "name": "SPLADE++", "description": "SPLADE model for Polish trained using SPLADE++ EnsembleDistil method. We utilised polish-distilroberta as the base model.", "url": "https://huggingface.co/sdadas/polish-splade"}, {"id": "intfloat/multilingual-e5-small", "url": "https://huggingface.co/intfloat/multilingual-e5-small"}, {"id": "intfloat/multilingual-e5-base", "url": "https://huggingface.co/intfloat/multilingual-e5-base"}, {"id": "intfloat/multilingual-e5-large", "url": "https://huggingface.co/intfloat/multilingual-e5-large"}, {"id": "ipipan/herbert-base-retrieval-v1", "url": "https://huggingface.co/ipipan/herbert-base-retrieval-v1", "warning": "The model was trained on MaupQA datasets, so the results reported for them may be overestimated and unreliable. \nYou can use the ⮾ button to disable the results for a specific task group."}, {"id": "ipipan/herbert-base-retrieval-v2", "url": "https://huggingface.co/ipipan/herbert-base-retrieval-v2", "warning": "The model was trained on MaupQA datasets, so the results reported for them may be overestimated and unreliable. \nYou can use the ⮾ button to disable the results for a specific task group."}, {"id": "ipipan/silver-retriever-base-v1", "url": "https://huggingface.co/ipipan/silver-retriever-base-v1", "warning": "The model was trained on MaupQA datasets, so the results reported for them may be overestimated and unreliable. \nYou can use the ⮾ button to disable the results for a specific task group."}, {"id": "ipipan/herbert-base-qa-v1", "url": "https://huggingface.co/ipipan/herbert-base-qa-v1", "warning": "The model was trained on MaupQA datasets, so the results reported for them may be overestimated and unreliable."}, {"id": "./models/baseline-mnr", "name": "MNR baseline (base)", "description": "A baseline model (polish-roberta-base-v2) fine-tuned on MSMarco using MNR training script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py"}, {"id": "./models/baseline-mnr-large", "name": "MNR baseline (large)", "description": "A baseline model (polish-roberta-large-v2) fine-tuned on MSMarco using MNR training script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py"}, {"id": "./models/baseline-mse", "name": "MSE baseline (base)", "description": "A baseline model (polish-roberta-base-v2) fine-tuned on MSMarco using MSE training script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py"}, {"id": "./models/baseline-mse-large", "name": "MSE baseline (large)", "description": "A baseline model (polish-roberta-large-v2) fine-tuned on MSMarco using MSE training script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py"}, {"id": "./models/baseline-poleval2022-base", "name": "poleval-2022-base-model (mkozlowski)", "description": "A base model trained by Marek Kozłowski for his PolEval-2022 Passage Retrieval submission.", "url": "https://beta.poleval.pl/challenge/2022-passage-retrieval"}, {"id": "./models/mnr-large-v6-poleval2022", "name": "poleval-2022-large-model (mkozlowski)", "description": "A large model trained by Marek Kozłowski for his PolEval-2022 Passage Retrieval submission.", "url": "https://beta.poleval.pl/challenge/2022-passage-retrieval"}, {"id": "LaBSE", "warning": "The model was trained for bitext mining, so it's probably not suited for information retrieval.", "url": "https://huggingface.co/sentence-transformers/LaBSE"}, {"id": "distiluse-base-multilingual-cased-v2", "url": "https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2"}, {"id": "paraphrase-multilingual-MiniLM-L12-v2", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"}, {"id": "paraphrase-multilingual-mpnet-base-v2", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2"}, {"id": "xlm-r-bert-base-nli-stsb-mean-tokens", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens"}, {"id": "xlm-r-distilroberta-base-paraphrase-v1", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1"}, {"id": "Voicelab/sbert-base-cased-pl", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/Voicelab/sbert-base-cased-pl"}, {"id": "Voicelab/sbert-large-cased-pl", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/Voicelab/sbert-large-cased-pl"}, {"id": "radlab/polish-sts-v2", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/radlab/polish-sts-v2"}, {"id": "sdadas/st-polish-paraphrase-from-mpnet", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": "https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet"}, {"id": "sdadas/st-polish-paraphrase-from-distilroberta", "warning": "The model was trained for semantic similarity, so it's probably not suited for information retrieval tasks.", "url": 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