Datasets:
Tasks:
Text Classification
Formats:
json
Sub-tasks:
natural-language-inference
Languages:
Spanish
Size:
< 1K
License:
| 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}, | |
| } | |
| ``` | |