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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 We have also added new, previously unpublished datasets.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. The source code of our evaluation framework as well as additional information on the benchmark can be found at <a target=\"_blank\" href=\"https://github.com/sdadas/pirb\">github.com/sdadas/pirb</a>\n<br/>\nThe benchmark is divided into sections containing the evaluation results for three groups of methods: standalone sparse and dense retrievers (<strong>standalone models</strong>), hybrid systems combining two or more retrieval models (<strong>hybrid retrieval</strong>), and two-stage retrieval pipelines combining retriever and reranker models (<strong>rerankers</strong>). Due to the high computational cost of reranking, the validation data for this group was limited to 1000 queries per dataset. We used mmlw-retrieval-roberta-large as the first stage retrieval method for each of the evaluated rerankers.", "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": [{"name": "Show methods", "options": [{"name": "Standalone models", "tag": "standalone"}, {"name": "Hybrid retrieval", "tag": "hybrid"}, {"name": "Rerankers", "tag": "reranker"}]}], "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": "BAAI/bge-m3", "name": "BAAI/bge-m3 (dense)", "url": "https://huggingface.co/BAAI/bge-m3"}, {"id": "ce-permutation-base", "name": "polish-roberta-base-v2 (RankNet)", "description": "Cross-encoder (polish-roberta-base-v2) trained using instruction distillation technique, following the example from the RankGPT repository.", "url": "https://github.com/sunnweiwei/RankGPT/tree/main/InstructDistill"}, {"id": "ce-permutation-large", "name": "polish-roberta-large-v2 (RankNet)", "description": "Cross-encoder (polish-roberta-large-v2) trained using instruction distillation technique, following the example from the RankGPT repository.", "url": "https://github.com/sunnweiwei/RankGPT/tree/main/InstructDistill"}, {"id": "ranknet-large-final", "name": "sdadas/polish-reranker-large-ranknet", "url": "https://huggingface.co/sdadas/polish-reranker-large-ranknet"}, {"id": "ranknet-base-final", "name": "sdadas/polish-reranker-base-ranknet", "url": "https://huggingface.co/sdadas/polish-reranker-base-ranknet"}, {"id": "reranker_roberta_large", "name": "sdadas/polish-reranker-large-mse", "url": "https://huggingface.co/sdadas/polish-reranker-large-mse"}, {"id": "reranker_roberta_base", "name": "sdadas/polish-reranker-base-mse", "url": "https://huggingface.co/sdadas/polish-reranker-base-mse"}, {"id": "herbert-base-reranker-msmarco", "name": "clarin-knext/herbert-base-reranker-msmarco", "url": "https://huggingface.co/clarin-knext/herbert-base-reranker-msmarco"}, {"id": "herbert-large-reranker-msmarco", "name": "clarin-knext/herbert-large-msmarco", "url": "https://huggingface.co/clarin-knext/herbert-large-msmarco"}, {"id": "herbert-large-reranker-msmarco", "name": "clarin-knext/herbert-large-msmarco", "url": "https://huggingface.co/clarin-knext/herbert-large-msmarco"}, {"id": "mmarco-mMiniLMv2-L12-H384-v1", "name": "nreimers/mmarco-mMiniLMv2-L12-H384-v1", "url": "https://huggingface.co/nreimers/mmarco-mMiniLMv2-L12-H384-v1"}, {"id": "mMiniLM-L6-v2-mmarco-v2", "name": "unicamp-dl/mMiniLM-L6-v2-mmarco-v2", "url": "https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2"}, {"id": "mt5-3b-mmarco-100k-kdd-alltrain", "name": "unicamp-dl/mt5-3b-mmarco-100k-kdd-alltrain-4.5e", "url": "https://huggingface.co/unicamp-dl/mt5-3b-mmarco-100k-kdd-alltrain-4.5e"}, {"id": "mt5-3b-mmarco-100k-kdd-alltrain", "name": "unicamp-dl/mt5-3b-mmarco-100k-kdd-alltrain-4.5e", "url": "https://huggingface.co/unicamp-dl/mt5-3b-mmarco-100k-kdd-alltrain-4.5e"}, {"id": "mt5-13b-mmarco-100k", "name": "unicamp-dl/mt5-13b-mmarco-100k", "url": "https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k"}, {"id": "plt5-base-msmarco", "name": "clarin-knext/plt5-base-msmarco", "url": "https://huggingface.co/clarin-knext/plt5-base-msmarco"}, {"id": "plt5-large-msmarco", "name": "clarin-knext/plt5-large-msmarco", "url": "https://huggingface.co/clarin-knext/plt5-large-msmarco"}, {"id": "mt5-base-mmarco-v2o", "name": "unicamp-dl/mt5-base-mmarco-v2", "url": "https://huggingface.co/unicamp-dl/mt5-base-mmarco-v2"}, {"id": "ce-basic-base", "name": "polish-roberta-base-v2 (BCE)", "description": "Cross-encoder (polish-roberta-base-v2) trained from scratch on Polish MS Marco, using script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_cross-encoder_scratch.py"}, {"id": "ce-basic-large", "name": "polish-roberta-large-v2 (BCE)", "description": "Cross-encoder (polish-roberta-large-v2) trained from scratch on Polish MS Marco, using script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_cross-encoder_scratch.py"}, {"id": "ce-kd-base", "name": "polish-roberta-base-v2 (MSE)", "description": "Cross-encoder (polish-roberta-base-v2) trained with knowledge distillation technique on Polish MS Marco, using script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_cross-encoder_kd.py"}, {"id": "ce-kd-large", "name": "polish-roberta-large-v2 (MSE)", "description": "Cross-encoder (polish-roberta-large-v2) trained with knowledge distillation technique on Polish MS Marco, using script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_cross-encoder_kd.py"}, {"id": "splade+mmlw-retrieval-roberta-large", "name": "SPLADE++ & mmlw-retrieval-roberta-large", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and mmlw-retrieval-roberta-large. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/splade%2Bmmlw-retrieval-roberta-large.json"}, {"id": "splade+mmlw-retrieval-roberta-base", "name": "SPLADE++ & mmlw-retrieval-roberta-base", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and mmlw-retrieval-roberta-base. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/splade%2Bmmlw-retrieval-roberta-base.json"}, {"id": "bm25+mmlw-retrieval-roberta-large", "name": "BM25 & mmlw-retrieval-roberta-large", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and mmlw-retrieval-roberta-large. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/bm25%2Bmmlw-retrieval-roberta-large.json"}, {"id": "bm25+mmlw-retrieval-roberta-base", "name": "BM25 & mmlw-retrieval-roberta-base", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and mmlw-retrieval-roberta-base. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/bm25%2Bmmlw-retrieval-roberta-base.json"}, {"id": "splade+mmlw-retrieval-e5-large", "name": "SPLADE++ & mmlw-retrieval-e5-large", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and mmlw-retrieval-e5-large. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/splade%2Bmmlw-retrieval-e5-large.json"}, {"id": "splade+mmlw-retrieval-e5-base", "name": "SPLADE++ & mmlw-retrieval-e5-base", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and mmlw-retrieval-e5-base. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/splade%2Bmmlw-retrieval-e5-base.json"}, {"id": "splade+mmlw-retrieval-e5-small", "name": "SPLADE++ & mmlw-retrieval-e5-small", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and mmlw-retrieval-e5-small. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/splade%2Bmmlw-retrieval-e5-small.json"}, {"id": "bm25+mmlw-retrieval-e5-large", "name": "BM25 & mmlw-retrieval-e5-large", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and mmlw-retrieval-e5-large. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/bm25%2Bmmlw-retrieval-e5-large.json"}, {"id": "bm25+mmlw-retrieval-e5-base", "name": "BM25 & mmlw-retrieval-e5-base", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and mmlw-retrieval-e5-base. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/bm25%2Bmmlw-retrieval-e5-base.json"}, {"id": "bm25+mmlw-retrieval-e5-small", "name": "BM25 & mmlw-retrieval-e5-small", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and mmlw-retrieval-e5-small. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/bm25%2Bmmlw-retrieval-e5-small.json"}, {"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 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14.44826779912439}, "scifact-pl": {"Accuracy@1": 49.333333333333336, "Recall@100": 89.43333333333334, "MRR@10": 58.12354497354497, "NDCG@10": 61.597678907663635}, "trec-covid-pl": {"Accuracy@1": 58.0, "Recall@100": 9.250812666043705, "MRR@10": 70.15238095238095, "NDCG@10": 48.69733981458553}, "mfaq": {"Accuracy@1": 57.71331760533274, "Recall@100": 81.91016039393891, "MRR@10": 62.87934816510376, "NDCG@10": 65.14101719179519}, "gpt-exams": {"Accuracy@1": 97.52797933833477, "Recall@100": 100.0, "MRR@10": 98.67666953634233, "NDCG@10": 99.01821445671122}}}]}
\ 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. The source code of our evaluation framework as well as additional information on the benchmark can be found at <a target=\"_blank\" href=\"https://github.com/sdadas/pirb\">github.com/sdadas/pirb</a>\n<br/>\nThe benchmark is divided into sections containing the evaluation results for three groups of methods: standalone sparse and dense retrievers (<strong>standalone models</strong>), hybrid systems combining two or more retrieval models (<strong>hybrid retrieval</strong>), and two-stage retrieval pipelines combining retriever and reranker models (<strong>rerankers</strong>). Due to the high computational cost of reranking, the validation data for this group was limited to 1000 queries per dataset. We used mmlw-retrieval-roberta-large as the first stage retrieval method for each of the evaluated rerankers.", "paper": "https://arxiv.org/abs/2402.13350", "citation": "@article{dadas2024pirb,\n  title={{PIRB}: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods}, \n  author={Sławomir Dadas and Michał Perełkiewicz and Rafał Poświata},\n  year={2024},\n  eprint={2402.13350},\n  archivePrefix={arXiv},\n  primaryClass={cs.CL}\n}", "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": [{"name": "Show methods", "options": [{"name": "Standalone models", "tag": "standalone"}, {"name": "Hybrid retrieval", "tag": "hybrid"}, {"name": "Rerankers", "tag": "reranker"}]}], "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": "BAAI/bge-m3", "name": "BAAI/bge-m3 (dense)", "url": "https://huggingface.co/BAAI/bge-m3"}, {"id": "ce-permutation-base", "name": "polish-roberta-base-v2 (RankNet)", "description": "Cross-encoder (polish-roberta-base-v2) trained using instruction distillation technique, following the example from the RankGPT repository.", "url": "https://github.com/sunnweiwei/RankGPT/tree/main/InstructDistill"}, {"id": "ce-permutation-large", "name": "polish-roberta-large-v2 (RankNet)", "description": "Cross-encoder (polish-roberta-large-v2) trained using instruction distillation technique, following the example from the RankGPT repository.", "url": "https://github.com/sunnweiwei/RankGPT/tree/main/InstructDistill"}, {"id": "ranknet-large-final", "name": "sdadas/polish-reranker-large-ranknet", "url": "https://huggingface.co/sdadas/polish-reranker-large-ranknet"}, {"id": "ranknet-base-final", "name": "sdadas/polish-reranker-base-ranknet", "url": "https://huggingface.co/sdadas/polish-reranker-base-ranknet"}, {"id": "reranker_roberta_large", "name": "sdadas/polish-reranker-large-mse", "url": "https://huggingface.co/sdadas/polish-reranker-large-mse"}, {"id": "reranker_roberta_base", "name": "sdadas/polish-reranker-base-mse", "url": "https://huggingface.co/sdadas/polish-reranker-base-mse"}, {"id": "herbert-base-reranker-msmarco", "name": "clarin-knext/herbert-base-reranker-msmarco", "url": "https://huggingface.co/clarin-knext/herbert-base-reranker-msmarco"}, {"id": "herbert-large-reranker-msmarco", "name": "clarin-knext/herbert-large-msmarco", "url": "https://huggingface.co/clarin-knext/herbert-large-msmarco"}, {"id": "herbert-large-reranker-msmarco", "name": "clarin-knext/herbert-large-msmarco", "url": "https://huggingface.co/clarin-knext/herbert-large-msmarco"}, {"id": "mmarco-mMiniLMv2-L12-H384-v1", "name": "nreimers/mmarco-mMiniLMv2-L12-H384-v1", "url": "https://huggingface.co/nreimers/mmarco-mMiniLMv2-L12-H384-v1"}, {"id": "mMiniLM-L6-v2-mmarco-v2", "name": "unicamp-dl/mMiniLM-L6-v2-mmarco-v2", "url": "https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2"}, {"id": "mt5-3b-mmarco-100k-kdd-alltrain", "name": "unicamp-dl/mt5-3b-mmarco-100k-kdd-alltrain-4.5e", "url": "https://huggingface.co/unicamp-dl/mt5-3b-mmarco-100k-kdd-alltrain-4.5e"}, {"id": "mt5-3b-mmarco-100k-kdd-alltrain", "name": "unicamp-dl/mt5-3b-mmarco-100k-kdd-alltrain-4.5e", "url": "https://huggingface.co/unicamp-dl/mt5-3b-mmarco-100k-kdd-alltrain-4.5e"}, {"id": "mt5-13b-mmarco-100k", "name": "unicamp-dl/mt5-13b-mmarco-100k", "url": "https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k"}, {"id": "plt5-base-msmarco", "name": "clarin-knext/plt5-base-msmarco", "url": "https://huggingface.co/clarin-knext/plt5-base-msmarco"}, {"id": "plt5-large-msmarco", "name": "clarin-knext/plt5-large-msmarco", "url": "https://huggingface.co/clarin-knext/plt5-large-msmarco"}, {"id": "mt5-base-mmarco-v2o", "name": "unicamp-dl/mt5-base-mmarco-v2", "url": "https://huggingface.co/unicamp-dl/mt5-base-mmarco-v2"}, {"id": "ce-basic-base", "name": "polish-roberta-base-v2 (BCE)", "description": "Cross-encoder (polish-roberta-base-v2) trained from scratch on Polish MS Marco, using script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_cross-encoder_scratch.py"}, {"id": "ce-basic-large", "name": "polish-roberta-large-v2 (BCE)", "description": "Cross-encoder (polish-roberta-large-v2) trained from scratch on Polish MS Marco, using script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_cross-encoder_scratch.py"}, {"id": "ce-kd-base", "name": "polish-roberta-base-v2 (MSE)", "description": "Cross-encoder (polish-roberta-base-v2) trained with knowledge distillation technique on Polish MS Marco, using script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_cross-encoder_kd.py"}, {"id": "ce-kd-large", "name": "polish-roberta-large-v2 (MSE)", "description": "Cross-encoder (polish-roberta-large-v2) trained with knowledge distillation technique on Polish MS Marco, using script from Sentence-Transformers library.", "url": "https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_cross-encoder_kd.py"}, {"id": "splade+mmlw-retrieval-roberta-large", "name": "SPLADE++ & mmlw-retrieval-roberta-large", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and mmlw-retrieval-roberta-large. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/splade%2Bmmlw-retrieval-roberta-large.json"}, {"id": "splade+mmlw-retrieval-roberta-base", "name": "SPLADE++ & mmlw-retrieval-roberta-base", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and mmlw-retrieval-roberta-base. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/splade%2Bmmlw-retrieval-roberta-base.json"}, {"id": "bm25+mmlw-retrieval-roberta-large", "name": "BM25 & mmlw-retrieval-roberta-large", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and mmlw-retrieval-roberta-large. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/bm25%2Bmmlw-retrieval-roberta-large.json"}, {"id": "bm25+mmlw-retrieval-roberta-base", "name": "BM25 & mmlw-retrieval-roberta-base", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and mmlw-retrieval-roberta-base. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/bm25%2Bmmlw-retrieval-roberta-base.json"}, {"id": "splade+mmlw-retrieval-e5-large", "name": "SPLADE++ & mmlw-retrieval-e5-large", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and mmlw-retrieval-e5-large. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/splade%2Bmmlw-retrieval-e5-large.json"}, {"id": "splade+mmlw-retrieval-e5-base", "name": "SPLADE++ & mmlw-retrieval-e5-base", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and mmlw-retrieval-e5-base. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/splade%2Bmmlw-retrieval-e5-base.json"}, {"id": "splade+mmlw-retrieval-e5-small", "name": "SPLADE++ & mmlw-retrieval-e5-small", "description": "Hybrid sparse-dense retrieval system consisting of SPLADE++ and mmlw-retrieval-e5-small. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/splade%2Bmmlw-retrieval-e5-small.json"}, {"id": "bm25+mmlw-retrieval-e5-large", "name": "BM25 & mmlw-retrieval-e5-large", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and mmlw-retrieval-e5-large. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/bm25%2Bmmlw-retrieval-e5-large.json"}, {"id": "bm25+mmlw-retrieval-e5-base", "name": "BM25 & mmlw-retrieval-e5-base", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and mmlw-retrieval-e5-base. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/bm25%2Bmmlw-retrieval-e5-base.json"}, {"id": "bm25+mmlw-retrieval-e5-small", "name": "BM25 & mmlw-retrieval-e5-small", "description": "Hybrid sparse-dense retrieval system consisting of BM25 and mmlw-retrieval-e5-small. Results from both indexes are aggregated using an additional XGBRanker model.", "url": "https://github.com/sdadas/pirb/blob/master/config/hybrids/bm25%2Bmmlw-retrieval-e5-small.json"}, {"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": 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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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