Datasets:

Flaglab
/

esnlir-human-validated

license: cc-by-4.0
language:
  - es
task_categories:
  - text-classification
task_ids:
  - natural-language-inference
pretty_name: ESNLIR — human-validated subset
size_categories:
  - n<1K
tags:
  - nli
  - spanish
  - causal
  - esnlir
  - human-annotated
configs:
  - config_name: default
    data_files:
      - split: validation
        path: labeled_final_dataset.jsonl

ESNLIR — human-validated subset

972 sentence pairs from ESNLIR whose label was confirmed by human annotators — the validated subset used in An Analysis of the Performance of Large Language Models in Spanish NLI Datasets with Causal Relationships (IBERAMIA 2026, to appear). Part of the ESNLIR-LLM collection, used in Pacolas/NLI-via-LLM.

A random sample of 2,136 instances from the full corpus was labeled by 27 Spanish-speaking university students, one label per pair. Only pairs whose majority-annotated label matched the original connector-derived label were kept.

Format

JSON Lines — one object per line. Same fields as Flaglab/esnlir-test, plus dataset_connector and connection_type.

The gold label is connection_type (the human-validated label), not connector_type. Both columns are present, which is what makes this subset useful: where they disagree, the connector-derived label did not survive human review.

Label distribution

label	n
`neutral`	362
`entailment`	219
`reasoning`	207
`contrasting`	184

Unbalanced, unlike the test split.

Genre distribution

genre	n
`theses`	427
`books`	217
`legal`	71
`news`	71
`comments`	67
`clinical`	51
`articles`	34
`talks`	34

Usage

from huggingface_hub import hf_hub_download
import json

p = hf_hub_download("Flaglab/esnlir-human-validated",
                    "labeled_final_dataset.jsonl", repo_type="dataset")
rows = [json.loads(l) for l in open(p) if l.strip()]

Citation

These splits were packaged for the following paper:

@InProceedings{perez2026llmspanishnlicausal,
  author    = {P{\'e}rez, Nicol{\'a}s and Portela, Johan R. and Manrique, Ruben},
  title     = {An Analysis of the Performance of Large Language Models in Spanish
               NLI Datasets with Causal Relationships},
  booktitle = {Advances in Artificial Intelligence -- IBERAMIA 2026},
  year      = {2026},
  publisher = {Springer Nature Switzerland},
  address   = {Cham},
  note      = {To appear},
}

The data itself is ESNLIR, released here under CC BY 4.0 — attribution to the source corpus is a condition of that licence:

@InProceedings{portela2025esnlirspanishmultigenredataset,
  author    = {Portela, Johan R. and P{\'e}rez-Ter{\'a}n, Nicol{\'a}s and Manrique, Rub{\'e}n},
  editor    = {Florez, Hector and Peluffo-Ordo{\~{n}}ez, Diego},
  title     = {{ESNLIR}: Expanding Spanish {NLI} Benchmarks with Multi-genre and Causal Annotation},
  booktitle = {Applied Informatics},
  year      = {2026},
  publisher = {Springer Nature Switzerland},
  address   = {Cham},
  pages     = {345--361},
  isbn      = {978-3-032-07175-0},
  doi       = {10.1007/978-3-032-07175-0_23},
}