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Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

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01hotchpotch /japanese-splade-v1-hard-negatives日本語 SPLADE v2 の学習に用いたデータセットです。 SPLADE モデルである hotchpotch/japanese-splade-base-v1-mmarco-only, japanese-splade-base-v1_5 を用いてハードネガティブマイニングを行なっています。また BAAI/bge-reranker-v2-m3 を用いたリランカースコアを付与しています。 mqa, mmarco はhpprc/emb のデータを用いています。 mqa の query 作成には MinHash を用い約40万件になるようフィルタしました。 msmarco-ja は hpprc/msmarco-jaのデータを用いています。 ライセンスは、各データセットのライセンスを継承します。 text10M<n<100M1 likes242 downloads2y agoHugging Face02nirantk /dbpedia-entities-efficient-splade-100K DBPedia SPLADE + OpenAI: 100,000 SPLADE Sparse Vectors + OpenAI Embedding This dataset has both OpenAI and SPLADE vectors for 100,000 DBPedia entries. This adds SPLADE Vectors to KShivendu/dbpedia-entities-openai-1M/ Model id used to make these vectors: model_id = "naver/efficient-splade-VI-BT-large-doc" For processing the query, use this: model_id = "naver/efficient-splade-VI-BT-large-query" If you'd like to extract the indices and weights/values from the vectors, you can do so… See the full description on the dataset page: https://huggingface.co/datasets/nirantk/dbpedia-entities-efficient-splade-100K.textfeature-extraction100K<n<1M3 likes153 downloads3y agoHugging Face03routir /msmarco_v2.1_segmented-spladev3-anserini0 likes134 downloads9mo agoHugging Face04routir /browsecomp-plus-spladev3-anserini0 likes90 downloads7d agoHugging Face05pyterrier /msmarco-passage-v2.splade-lg.pisa msmarco-passage-v2.splade-lg.pisa Description TODO: What is the artifact? Usage # Load the artifact import pyterrier as pt artifact = pt.Artifact.from_hf('pyterrier/msmarco-passage-v2.splade-lg.pisa') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the artifact. Metadata { "type": "sparse_index", "format":… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier/msmarco-passage-v2.splade-lg.pisa.text-retrieval0 likes80 downloads3mo agoHugging Face06Sicheng-Chroma /wikipedia-en-splade-bge Wikipedia English with SPLADE and BGE-M3 Pre-computed SPLADE sparse and BGE-M3 dense embeddings for 6.4M English Wikipedia articles. Direct Usage HuggingFace automatically discovers parquet files. You can load this dataset directly: from datasets import load_dataset # Stream the entire dataset (recommended for large dataset) dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", streaming=True) # Load specific splits train_dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Sicheng-Chroma/wikipedia-en-splade-bge.texttext-retrieval1M<n<10M1 likes62 downloads1y agoHugging Face07DandyTian /ragwiki-splade-cocondenser-ensembledistil.pisa ragwiki-splade-cocondenser-ensembledistil.pisa Description TODO: What is the artifact? Usage # Load the artifact import pyterrier as pt artifact = pt.Artifact.from_hf('DandyTian/ragwiki-splade-cocondenser-ensembledistil.pisa') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the artifact. Metadata { "type":… See the full description on the dataset page: https://huggingface.co/datasets/DandyTian/ragwiki-splade-cocondenser-ensembledistil.pisa.text-retrieval1 likes56 downloads2d agoHugging Face08tuskanny /seismic-msmarco-splade0 likes51 downloads3y agoHugging Face09tuskanny /kannolo-msmarco-spladetabular1K<n<10K0 likes40 downloads1y agoHugging Face10slupart /qrecc-distillation-mistral-splade0 likes40 downloads1y agoHugging Face11pyterrier /dbpedia-entity.splade-v3.cache dbpedia-entity.splade-v3.cache Description TODO: What is the artifact? Usage # Load the artifact import pyterrier_alpha as pta artifact = pta.Artifact.from_hf('pyterrier/dbpedia-entity.splade-v3.cache') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the artifact. Metadata { "type": "indexer_cache", "format": "lz4pickle"… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier/dbpedia-entity.splade-v3.cache.text-retrieval0 likes37 downloads2y agoHugging Face12slupart /topiocqa-distillation-llama-mistral-splade DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search This distillation dataset can be used to distill knowledge from Llama and Mistral LLMs into a conversational search dataset (TopiOCQA). Please refer to the DiSCo github for complete usage [github]. Citation If you use our distillation file, please cite our work: @article{lupart2024disco, title={DiSCo Meets LLMs: A Unified Approach for Sparse Retrieval and Contextual Distillation in… See the full description on the dataset page: https://huggingface.co/datasets/slupart/topiocqa-distillation-llama-mistral-splade.1 likes35 downloads1y agoHugging Face13yosefw /msmarco-train-distil-splade-v3-similaritytext100K<n<1M0 likes35 downloads1y agoHugging Face14pyterrier-tutorial /bright.sustainable.splade.cache bright.sustainable.splade.cache Description TODO: What is the artifact? Usage # Load the artifact import pyterrier as pt artifact = pt.Artifact.from_hf('pyterrier-tutorial/bright.sustainable.splade.cache') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the artifact. Metadata { "type": "indexer_cache"… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier-tutorial/bright.sustainable.splade.cache.text-retrieval0 likes35 downloads3mo agoHugging Face15pyterrier /hotpotqa.splade-v3.cache hotpotqa.splade-v3.cache Description TODO: What is the artifact? Usage # Load the artifact import pyterrier_alpha as pta artifact = pta.Artifact.from_hf('pyterrier/hotpotqa.splade-v3.cache') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the artifact. Metadata { "type": "indexer_cache", "format": "lz4pickle", "package_hint":… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier/hotpotqa.splade-v3.cache.text-retrieval0 likes34 downloads2y agoHugging Face16macavaney /msmarco-passage.splade-lg.cache msmarco-passage.splade-lg.cache Description TODO: What is the artifact? Usage # Load the artifact import pyterrier as pt artifact = pt.Artifact.from_hf('macavaney/msmarco-passage.splade-lg.cache') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the artifact. Metadata { "type": "indexer_cache", "format": "lz4pickle", "record_count":… See the full description on the dataset page: https://huggingface.co/datasets/macavaney/msmarco-passage.splade-lg.cache.text-retrieval0 likes31 downloads2y agoHugging Face17pyterrier /climate-fever.splade-v1.cache climate-fever.splade-v1.cache Description TODO: What is the artifact? Usage # Load the artifact import pyterrier_alpha as pta artifact = pta.Artifact.from_hf('pyterrier/climate-fever.splade-v1.cache') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the artifact. Metadata { "type": "indexer_cache", "format": "lz4pickle"… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier/climate-fever.splade-v1.cache.text-retrieval0 likes28 downloads2y agoHugging Face18Hyukkyu /msmarco-spladev2-hard-negatives-scorestext100K<n<1M0 likes27 downloads8mo agoHugging Face19yosefw /msmarco-train-distil-splade-v3text100K<n<1M0 likes25 downloads1y agoHugging Face20HeydarS /enwiki_20251001_spladepp_index0 likes25 downloads8mo agoHugging Face21tuskanny /splade-v3-ciff0 likes23 downloads7mo agoHugging Face22pyterrier-tutorial /bright.sustainable.splade.pisa bright.sustainable.splade.pisa Description TODO: What is the artifact? Usage # Load the artifact import pyterrier as pt artifact = pt.Artifact.from_hf('pyterrier-tutorial/bright.sustainable.splade.pisa') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the artifact. Metadata { "type": "sparse_index", "format":… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier-tutorial/bright.sustainable.splade.pisa.text-retrieval0 likes23 downloads3mo agoHugging Face23pyterrier /nq.splade-v3.cache nq.splade-v3.cache Description TODO: What is the artifact? Usage # Load the artifact import pyterrier_alpha as pta artifact = pta.Artifact.from_hf('pyterrier/nq.splade-v3.cache') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the artifact. Metadata { "type": "indexer_cache", "format": "lz4pickle", "package_hint":… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier/nq.splade-v3.cache.text-retrieval0 likes22 downloads2y agoHugging Face24tuskanny /seismic-msmarco-splade-bin0 likes22 downloads1y agoHugging Face25thivy /eti-embedding-splade-v7-distill-v1tabular100K<n<1M0 likes22 downloads3mo agoHugging Face26slupart /qrecc-distillation-human-mistral-splade0 likes21 downloads1y agoHugging Face27JackMcKechnie /msmarco_passage_trec_dl_2019_judged_splade_naver_splade_v2_distil.terrier msmarco_passage_trec_dl_2019_judged_splade_naver_splade_v2_distil.terrier Description TODO: What is the artifact? Usage # Load the artifact import pyterrier as pt artifact = pt.Artifact.from_hf('JackMcKechnie/msmarco_passage_trec_dl_2019_judged_splade_naver_splade_v2_distil.terrier') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the… See the full description on the dataset page: https://huggingface.co/datasets/JackMcKechnie/msmarco_passage_trec_dl_2019_judged_splade_naver_splade_v2_distil.terrier.text-retrieval0 likes21 downloads9mo agoHugging Face28JackMcKechnie /msmarco_passage_trec_dl_2019_judged_splade_naver_splade_cocondenser_selfdistil.terrier msmarco_passage_trec_dl_2019_judged_splade_naver_splade_cocondenser_selfdistil.terrier Description TODO: What is the artifact? Usage # Load the artifact import pyterrier as pt artifact = pt.Artifact.from_hf('JackMcKechnie/msmarco_passage_trec_dl_2019_judged_splade_naver_splade_cocondenser_selfdistil.terrier') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how… See the full description on the dataset page: https://huggingface.co/datasets/JackMcKechnie/msmarco_passage_trec_dl_2019_judged_splade_naver_splade_cocondenser_selfdistil.terrier.text-retrieval0 likes20 downloads9mo agoHugging Face29pyterrier /climate-fever.splade-v3.cache climate-fever.splade-v3.cache Description TODO: What is the artifact? Usage # Load the artifact import pyterrier_alpha as pta artifact = pta.Artifact.from_hf('pyterrier/climate-fever.splade-v3.cache') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the artifact. Metadata { "type": "indexer_cache", "format": "lz4pickle"… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier/climate-fever.splade-v3.cache.text-retrieval0 likes19 downloads2y agoHugging Face30pyterrier /hotpotqa.splade-v1.cache hotpotqa.splade-v1.cache Description TODO: What is the artifact? Usage # Load the artifact import pyterrier_alpha as pta artifact = pta.Artifact.from_hf('pyterrier/hotpotqa.splade-v1.cache') # TODO: Show how you use the artifact Benchmarks TODO: Provide benchmarks for the artifact. Reproduction # TODO: Show how you constructed the artifact. Metadata { "type": "indexer_cache", "format": "lz4pickle", "package_hint":… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier/hotpotqa.splade-v1.cache.text-retrieval0 likes19 downloads2y agoHugging Face

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