size_categories:100K<n<1M format:parquet modality:text library:datasets library:dask library:mlcroissant library:polars region:us
---
dataset_info:
  features:
  - name: query
    dtype: string
  - name: pos
    dtype: string
  splits:
  - name: train
    num_bytes: 729187184.8374316
    num_examples: 903218
  download_size: 204019551
  dataset_size: 729187184.8374316
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# nli pairs
j = datasets.load_dataset("andersonbcdefg/jina_negation_v2", split="train").select_columns(["query", "pos"])
syn = datasets.load_dataset("andersonbcdefg/synthetic_nli_combined_mnli_filtered", split="train").select_columns(["query", "pos"])
a = datasets.load_dataset("andersonbcdefg/anli_triples", split="train").select_columns(["query", "pos"])
sim = datasets.load_dataset("andersonbcdefg/simcse_nli", split="train").select_columns(["query", "pos"])
doc = datasets.load_dataset("andersonbcdefg/doc_nli_pos_pairs", split="train")
fever = datasets.load_dataset("pietrolesci/nli_fever", split="train").filter(lambda x: x["label"] == 0).map(
    lambda x: {"query": x["premise"], "pos": x["hypothesis"]}
).select_columns(["query", "pos"])
ling = datasets.load_dataset("metaeval/lingnli", split="train").filter(lambda x: x["label"] == "entailment").map(
    lambda x: {"query": x["premise"], "pos": x["hypothesis"]}
).select_columns(["query", "pos"])