# Dataset Card for "news_as2_pairs_and_triplets"

This dataset is a modified version of NewsAS2 from [lucadiliello/news_as2](https://huggingface.co/datasets/lucadiliello/news_as2) designed to train/pre-train "passage-retrieval" models.
The dataset is divided into two splits:
+ *pairs* split: Each instance in this split consists of a <u>question-(positive)answer pair</u>.
+ *triplets* split: Each instance in this split consists of a <u>question-(positive)answer-(hard-negative)answer triplet</u>.

For more info check out the original NewsQA dataset in this [repository](https://github.com/lucadiliello/answer-selection)
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#region-us 

# Dataset Card for "news_as2_pairs_and_triplets"

This dataset is a modified version of NewsAS2 from lucadiliello/news_as2 designed to train/pre-train "passage-retrieval" models.
The dataset is divided into two splits:
+ *pairs* split: Each instance in this split consists of a <u>question-(positive)answer pair</u>.
+ *triplets* split: Each instance in this split consists of a <u>question-(positive)answer-(hard-negative)answer triplet</u>.

For more info check out the original NewsQA dataset in this repository
# Dataset Card for "news_as2_pairs_and_triplets"

This dataset is a modified version of NewsAS2 from lucadiliello/news_as2 designed to train/pre-train "passage-retrieval" models.
The dataset is divided into two splits:
+ *pairs* split: Each instance in this split consists of a <u>question-(positive)answer pair</u>.
+ *triplets* split: Each instance in this split consists of a <u>question-(positive)answer-(hard-negative)answer triplet</u>.

For more info check out the original NewsQA dataset in this repository
TAGS
#region-us 
 # Dataset Card for "news_as2_pairs_and_triplets"

This dataset is a modified version of NewsAS2 from lucadiliello/news_as2 designed to train/pre-train "passage-retrieval" models.
The dataset is divided into two splits:
+ *pairs* split: Each instance in this split consists of a <u>question-(positive)answer pair</u>.
+ *triplets* split: Each instance in this split consists of a <u>question-(positive)answer-(hard-negative)answer triplet</u>.

For more info check out the original NewsQA dataset in this repository