---
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**](https://huggingface.co/collections/Flaglab/esnlir-llm-6a74a6b9ce149918f48ebba5) collection, used in
[`Pacolas/NLI-via-LLM`](https://github.com/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`](https://huggingface.co/datasets/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

```python
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:

```bibtex
@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:

```bibtex
@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},
}
```