datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ms_marco
Dataset Card for "ms_marco"
Dataset Summary
Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.
The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer.
Since then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset,
keyphrase extraction dataset, crawling dataset, and a conversational search.… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/ms_marco.msmarco-beir-e5prebuilt-indexes-msmarco-v1
Prebuilt Indexes for MS MARCO v1
Available indexes:
Lucene Standard Inverted
msmarco-v1-doc
[readme]
Lucene index of the MS MARCO V1 document corpus.
msmarco-v1-doc-slim
[readme]
Lucene index of the MS MARCO V1 document corpus ('slim' version).
msmarco-v1-doc-full
[readme]
Lucene index of the MS MARCO V1 document corpus ('full' version).
msmarco-v1-doc.d2q-t5
[readme]
Lucene index of the MS MARCO V1 document corpus with doc2query-T5 expansions.… See the full description on the dataset page: https://huggingface.co/datasets/castorini/prebuilt-indexes-msmarco-v1.msmarco-v2.1-embed-english-v3
TREC-RAG 2024 Corpus (MSMARCO 2.1) - Encoded with Cohere Embed English v3
This dataset contains the embeddings for the TREC-RAG Corpus 2024 embedded with the Cohere Embed V3 English model.
It contains embeddings for 113,520,750 passages, embeddings for 1677 queries from TREC-Deep Learning 2021-2023, as well as top-1000 hits for all queries using a brute-force (flat) index.
Search over the Index
We have a pre-build index that only requires 300 MB available at… See the full description on the dataset page: https://huggingface.co/datasets/CohereLabs/msmarco-v2.1-embed-english-v3.msmarco-distilbert-margin-mse-mean-dot-v1
MS MARCO with hard negatives from distilbert-margin-mse-mean-dot-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-mean-dot-v1.msmarco-v2.1-snowflake-arctic-embed-l
Snowflake Arctic Embed L Embeddings for MSMARCO V2.1 for TREC-RAG
This dataset contains the embeddings for the MSMARCO-V2.1 dataset which is used as the corpora for TREC RAG
All embeddings are created using Snowflake's Arctic Embed L and are intended to serve as a simple baseline for dense retrieval-based methods.
Retrieval Performance
Retrieval performance for the TREC DL21-23, MSMARCOV2-Dev and Raggy Queries can be found below with BM25 as a baseline. For both… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/msmarco-v2.1-snowflake-arctic-embed-l.msmarco-bm25
MS MARCO with hard negatives from bm25
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:
msmarco-bm25… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-bm25.msmarco-beir-constbertmsmarco
Dataset Card for "msmarco"
More Information needed
msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
MS MARCO with hard negatives from co-condenser-margin-mse-sym-mnrl-mean-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1.msmarco
Dataset Card for BEIR Benchmark
Dataset Summary
BEIR is a heterogeneous benchmark built from 18 diverse datasets representing 9 information retrieval tasks.
This msmarco subset is part of BEIR.
Languages
All tasks are in English (en).
Dataset Structure
This dataset uses the standard BEIR retrieval layout and includes:
corpus: one row per document with _id, title, text
queries: one row per query with _id, title, text
Data Fields
_id… See the full description on the dataset page: https://huggingface.co/datasets/BeIR/msmarco.msmarco-msmarco-distilbert-base-v3
MS MARCO with hard negatives from msmarco-distilbert-base-v3
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:
msmarco-bm25… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3.msmarco-distilbert-margin-mse-sym-mnrl-mean-v2
MS MARCO with hard negatives from distilbert-margin-mse-sym-mnrl-mean-v2
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-sym-mnrl-mean-v2.msmarco
MS MARCO Training Dataset
This dataset consists of 4 separate datasets, each using the MS MARCO Queries and passages:
triplets: This subset contains triplets of query-id, positive-id, negative-id as provided in qidpidtriples.train.full.2.tsv.gz from the MS MARCO Website. The only change is that this dataset has been reshuffled. This dataset can easily be used with an MultipleNegativesRankingLoss a.k.a. InfoNCE loss.
labeled-list: This subset contains triplets of query-id, doc-ids… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco.msmarco-passagemsmarco-msmarco-distilbert-base-tas-b
MS MARCO with hard negatives from msmarco-distilbert-base-tas-b
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-tas-b.msmarco-distilbert-margin-mse-cls-dot-v1
MS MARCO with hard negatives from distilbert-margin-mse-cls-dot-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-cls-dot-v1.msmarco-mpnet-margin-mse-mean-v1
MS MARCO with hard negatives from mpnet-margin-mse-mean-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:
msmarco-bm25… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-mpnet-margin-mse-mean-v1.msmarco
MSMARCO
An MTEB dataset
Massive Text Embedding Benchmark
MS MARCO is a collection of datasets focused on deep learning in search
Task category
t2t
Domains
Encyclopaedic, Academic, Blog, News, Medical, Government, Reviews, Non-fiction, Social, Web
Reference
https://microsoft.github.io/msmarco/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["MSMARCO"])
evaluator… See the full description on the dataset page: https://huggingface.co/datasets/mteb/msmarco.MSMARCO-XI
MS MARCO Translations Dataset
Dataset Description
This dataset contains the MS MARCO dataset translated into various Indic languages. The original MS MARCO dataset is a collection of queries, passages, and answers for machine reading comprehension and question answering tasks. Each example includes both the original English content and the translated content, along with translation metadata.
Supported Languages
Language Code
Language Name… See the full description on the dataset page: https://huggingface.co/datasets/ai4bharat/MSMARCO-XI.msmarco-distilbert-margin-mse-sym-mnrl-mean-v1
MS MARCO with hard negatives from distilbert-margin-mse-sym-mnrl-mean-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-sym-mnrl-mean-v1.msmarco-passage-doct5querymixed_msm_evalsmsmarco-qrels
Dataset Card for BEIR Benchmark
Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
Fact-checking: FEVER, Climate-FEVER, SciFact
Question-Answering: NQ, HotpotQA, FiQA-2018
Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
News Retrieval: TREC-NEWS, Robust04
Argument Retrieval: Touche-2020, ArguAna
Duplicate Question Retrieval: Quora, CqaDupstack
Citation-Prediction: SCIDOCS
Tweet… See the full description on the dataset page: https://huggingface.co/datasets/BeIR/msmarco-qrels.ms-marco-en-bge-gemma
ms-marco-en-bge
This dataset contains the MS MARCO dataset with negatives mined using ColBERT and then scored by bge-reranker-v2-gemma.
It can be used to train a retrieval model using knowledge distillation, for example using PyLate.
knowledge distillation
To fine-tune a model using knowledge distillation loss we will need three distinct file:
Datasetsfrom datasets import load_dataset
train = load_dataset(
"lightonai/ms-marco-en-gemma",
"train"… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma.msmarco-distilbert-margin-mse-mnrl-mean-v1
MS MARCO with hard negatives from distilbert-margin-mse-mnrl-mean-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-mnrl-mean-v1.MSM_evalsmsmarco-msmarco-MiniLM-L6-v3
MS MARCO with hard negatives from msmarco-MiniLM-L6-v3
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:
msmarco-bm25… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-MiniLM-L6-v3.msmarco-co-condenser-margin-mse-cls-v1
MS MARCO with hard negatives from co-condenser-margin-mse-cls-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-cls-v1.msmarco-v2.1-snowflake-arctic-embed-m-v1.5
Snowflake Arctic Embed M V1.5 Embeddings for MSMARCO V2.1 for TREC-RAG
This dataset contains the embeddings for the MSMARCO-V2.1 dataset which is used as the corpora for TREC RAG
All embeddings are created using Snowflake's Arctic Embed M v1.5 and are intended to serve as a simple baseline for dense retrieval-based methods.
It's worth noting that Snowflake's Arctic Embed M v1.5 is optimized for efficient embeddings and thus supports embedding truncation and quantization. More… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/msmarco-v2.1-snowflake-arctic-embed-m-v1.5.