context
string | question
string | answer_prefix
string | answers
list | task
string | max_new_tokens
int64 |
|---|---|---|---|---|---|
"You will be given a list of documents. You need to read carefully and understand all of them. Then (...TRUNCATED)
| "====== Now let's start! ======\nBased on the documents above, can you answer the following query? P(...TRUNCATED)
|
Final Answer:
| ["2018 Kentucky Bank Tennis Championships – Men's Singles","2018 Kunming Open – Men's Doubles","(...TRUNCATED)
|
qampari_128k
| 256
|
"You will be given a list of documents. You need to read carefully and understand all of them. Then (...TRUNCATED)
| "====== Now let's start! ======\nBased on the documents above, can you answer the following query? P(...TRUNCATED)
|
Final Answer:
|
[
"Dead Heavens",
"Gorilla Biscuits",
"Quicksand",
"Rival Schools",
"Walking Concert"
] |
qampari_128k
| 256
|
"You will be given a list of documents. You need to read carefully and understand all of them. Then (...TRUNCATED)
| "====== Now let's start! ======\nBased on the documents above, can you answer the following query? P(...TRUNCATED)
|
Final Answer:
|
[
"Arabsat-1A",
"Arabsat-1B",
"Arabsat-5A",
"Badr-4",
"INSAT-2DT"
] |
qampari_128k
| 256
|
"You will be given a list of documents. You need to read carefully and understand all of them. Then (...TRUNCATED)
| "====== Now let's start! ======\nBased on the documents above, can you answer the following query? P(...TRUNCATED)
|
Final Answer:
|
[
"Diplopedia",
"RationalWiki",
"Uncyclopedia",
"Veropedia",
"Wiktionary"
] |
qampari_128k
| 256
|
"You will be given a list of documents. You need to read carefully and understand all of them. Then (...TRUNCATED)
| "====== Now let's start! ======\nBased on the documents above, can you answer the following query? P(...TRUNCATED)
|
Final Answer:
|
[
"Chris McCormack",
"Chrissie Wellington",
"Helen Jenkins",
"Magali Messmer",
"Pete Jacobs"
] |
qampari_128k
| 256
|
"You will be given a list of documents. You need to read carefully and understand all of them. Then (...TRUNCATED)
| "====== Now let's start! ======\nBased on the documents above, can you answer the following query? P(...TRUNCATED)
|
Final Answer:
|
[
"Ammonia",
"Collins reagent",
"Dimethylformamide",
"Ethanol",
"Sodium azide"
] |
qampari_128k
| 256
|
"You will be given a list of documents. You need to read carefully and understand all of them. Then (...TRUNCATED)
| "====== Now let's start! ======\nBased on the documents above, can you answer the following query? P(...TRUNCATED)
|
Final Answer:
|
[
"Byeongsan Seowon",
"Dosan Seowon",
"Oksan Seowon",
"Seoak Seowon",
"Sosu Seowon"
] |
qampari_128k
| 256
|
"You will be given a list of documents. You need to read carefully and understand all of them. Then (...TRUNCATED)
| "====== Now let's start! ======\nBased on the documents above, can you answer the following query? P(...TRUNCATED)
|
Final Answer:
| ["2015 Wimbledon Championships – Boys' Singles","2016 Charlottesville Men's Pro Challenger – sin(...TRUNCATED)
|
qampari_128k
| 256
|
"You will be given a list of documents. You need to read carefully and understand all of them. Then (...TRUNCATED)
| "====== Now let's start! ======\nBased on the documents above, can you answer the following query? P(...TRUNCATED)
|
Final Answer:
|
[
"Christian Peter",
"Howard Barbieri",
"Jeff Kunkel",
"Knowshon Moreno",
"Melanie McGuire"
] |
qampari_128k
| 256
|
"You will be given a list of documents. You need to read carefully and understand all of them. Then (...TRUNCATED)
| "====== Now let's start! ======\nBased on the documents above, can you answer the following query? P(...TRUNCATED)
|
Final Answer:
| ["2015 Città di Caltanissetta – singles","2016 Open Città della Disfida – singles","2016 Stock(...TRUNCATED)
|
qampari_128k
| 256
|
End of preview. Expand
in Data Studio
LOFT RAG - Qampari (128k)
Dataset Description
This dataset is part of the LOFT (Long-context Open Foundation Tasks) benchmark, specifically the RAG (Retrieval-Augmented Generation) task.
- Dataset: Qampari
- Context Length: 128k
- Task Type: RAG (Retrieval-Augmented Generation)
- Language: English
- Source: LOFT Benchmark (Google DeepMind)
Dataset Structure
Data Fields
context(string): Full prompt context including corpus documents and few-shot examplesquestion(string): Query separator + query format + query textanswer_prefix(string): Prefix for answer generation ("Final Answer: ")answers(list[string]): Ground truth answerstask(string): Task identifier (e.g., "qampari_128k")max_new_tokens(int64): Maximum tokens for generation (256)
Data Splits
dev: Development set (10 examples)test: Test set (100 examples)
Usage
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("loft-rag-qampari-128k")
# Access splits
dev_data = dataset["dev"]
df_dev = dev_data.to_pandas()
test_data = dataset["test"]
df_test = test_data.to_pandas()
# Example usage
sample = dataset["dev"][0] if "dev" in dataset else dataset["test"][0]
context = sample["context"]
question = sample["question"]
answers = sample["answers"]
Dataset Creation
This dataset was converted from LOFT's original format to HuggingFace format using exact LOFT prompt construction to ensure 100% fidelity.
- Prompt Construction: Uses LOFT's
PromptRegistryandconcatenate_chunks()for exact prompt matching - Few-shot Examples: Preserved exactly as in LOFT (5 examples)
- Corpus Documents: Full corpus included in context (corpus-in-context approach)
- Verification: All prompts verified to match LOFT originals exactly
Related Datasets
All LOFT RAG datasets are available under the loft-rag-* namespace:
- Main Index - Overview of all datasets
Citation
@article{{loft2024,
title={{LOFT: Long-context Open Foundation Tasks}},
author={{Google DeepMind}},
year={{2024}},
url={{https://github.com/google-deepmind/loft}}
}}
License
Apache 2.0
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