cqadupstack
cqadupstack-gaming
CQADupstackGamingRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
CQADupStack: A Benchmark Data Set for Community Question-Answering Research
Task category
t2t
Domains
Web, Written
Reference
http://nlp.cis.unimelb.edu.au/resources/cqadupstack/
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(["CQADupstackGamingRetrieval"])
evaluator = mteb.MTEB(task)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cqadupstack-gaming.cqadupstack-unix
CQADupstackUnixRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
CQADupStack: A Benchmark Data Set for Community Question-Answering Research
Task category
t2t
Domains
Written, Web, Programming
Reference
http://nlp.cis.unimelb.edu.au/resources/cqadupstack/
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(["CQADupstackUnixRetrieval"])
evaluator =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cqadupstack-unix.beir-nl-cqadupstack
Dataset Card for BEIR-NL Benchmark
Dataset Summary
BEIR-NL is a Dutch-translated version of the BEIR benchmark, a diverse and heterogeneous collection of datasets covering various domains from biomedical and financial texts to general web content. Our benchmark is integrated into the Massive Multilingual Text Embedding Benchmark (MMTEB).
BEIR-NL contains the following tasks:
Fact-checking: FEVER, Climate-FEVER, SciFact
Question-Answering: NQ, HotpotQA, FiQA-2018… See the full description on the dataset page: https://huggingface.co/datasets/clips/beir-nl-cqadupstack.cqadupstack-physics
CQADupstackPhysicsRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
CQADupStack: A Benchmark Data Set for Community Question-Answering Research
Task category
t2t
Domains
Written, Academic, Non-fiction
Referencehttp://nlp.cis.unimelb.edu.au/resources/cqadupstack/
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(["CQADupstackPhysicsRetrieval"])
evaluator… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cqadupstack-physics.cqadupstack-english
CQADupstackEnglishRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
CQADupStack: A Benchmark Data Set for Community Question-Answering Research
Task category
t2t
Domains
Written
Reference
http://nlp.cis.unimelb.edu.au/resources/cqadupstack/
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(["CQADupstackEnglishRetrieval"])
evaluator = mteb.MTEB(task)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cqadupstack-english.cqadupstack-programmers
CQADupstackProgrammersRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
CQADupStack: A Benchmark Data Set for Community Question-Answering Research
Task category
t2t
Domains
Programming, Written, Non-fiction
Referencehttp://nlp.cis.unimelb.edu.au/resources/cqadupstack/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cqadupstack-programmers.