datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
msmarco_answerai_colbert_small_embeddings
MS MARCO ColBERT Embeddings
Pre-computed ColBERT embeddings for MS MARCO using PyLate and answerdotai/answerai-colbert-small-v1.
Dataset Structure
The dataset contains:
data/corpus/: 177 parquet files with document embeddings
data/queries/: 11 parquet files with query embeddings
data/qrels/train.parquet: Relevance judgments (532,751 pairs)
Usage
from datasets import load_dataset
# Load from directory (recommended for large datasets)
corpus =… See the full description on the dataset page: https://huggingface.co/datasets/WenxingZhu/msmarco_answerai_colbert_small_embeddings.lotte_pooled_colbertv2ms_marco_colbertv2pgturbohybrid_dbpedia_colbert
johannhartmann/pgturbohybrid_dbpedia_colbert
Precomputed DBpedia ColBERT multivectors for pgturbohybrid benchmark runs.
The dataset stores packed little-endian float16 values for the document and query embeddings.
Importing these rows into PostgreSQL avoids llama.cpp embedding generation
during retrieval/index benchmarks.
This compact export stores half-precision values and reconstructs turbohybrid_multivector values on import. Use it for benchmark loading where avoiding runtime… See the full description on the dataset page: https://huggingface.co/datasets/johannhartmann/pgturbohybrid_dbpedia_colbert.clini-colbert-pairs-dev-v2NQ-colbert-10k-case-entitynfcorpus_colbertv2nfcorpus_modern_colbertscidocs_colbertv2scidocs_modern_colbertfiqa_colbertv2fiqa_modern_colbert