Add dataset card

README.md

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+---
+license: cc-by-4.0
+language:
+- es
+task_categories:
+- text-classification
+task_ids:
+- natural-language-inference
+pretty_name: ESNLIR — human-annotated AL evaluation set (1,695 pairs)
+size_categories:
+- 1K<n<10K
+tags:
+- nli
+- spanish
+- causal
+- esnlir
+- active-learning
+- human-annotated
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: test_full.jsonl
+---
+
+# ESNLIR — human-annotated evaluation set
+
+**1,695 human-annotated premise–hypothesis pairs** spanning 24 domains and 8 genres, built as the
+evaluation set for active-learning experiments on the ESNLIR corpus.
+
+> **Active Learning for Spanish Natural Language Inference**
+> Diego Ortiz, Johan R. Portela, Ruben Manrique — Universidad de los Andes, Bogotá
+
+Code: [`jd-rodriguezp1234/esnlir-active-learning`](https://github.com/jd-rodriguezp1234/esnlir-active-learning) ·
+Corpus: [`Flaglab/ESNLIR-dataset`](https://huggingface.co/datasets/Flaglab/ESNLIR-dataset)
+
+## Labels
+
+Four inference relations. Each instance is a pair of **adjacent sentences from a single document**:
+the premise precedes a discourse connector, the hypothesis follows it, and the connector is removed
+so the relation has to be recovered from the two sentences alone.
+
+| label | n | share |
+|---|---|---|
+| `neutral` | 634 | 37.4% |
+| `entailment` | 379 | 22.4% |
+| `reasoning` | 370 | 21.8% |
+| `contrasting` | 312 | 18.4% |
+
+Classes are **uneven by design** — this is a natural sample, not a balanced one. Report **macro F1**
+rather than accuracy.
+
+`reasoning` (cause–effect, e.g. *en consecuencia*, *por consiguiente*) is ESNLIR's departure from
+the usual three-class schema, and empirically the hardest of the four.
+
+## Genres and domains
+
+| genre | n | | genre | n |
+|---|---|---|---|---|
+| `theses` | 614 | | `articles` | 132 |
+| `books` | 315 | | `legal` | 112 |
+| `comments` | 234 | | `clinical` | 50 |
+| `news` | 198 | | `talks` | 40 |
+
+24 distinct domains. The five largest: literature (17.2%), web comments (13.8%), Spanish
+public-domain news (8.0%), encyclopedia (7.8%), engineering theses (7.0%). The set deliberately
+spans formal registers (legal, clinical) and informal ones (web comments, tweets).
+
+## Fields
+
+```json
+{"sentence_1": "se pueden retener 10 pasaportes alemanes al mismo tiempo",
+ "sentence_2": "estos pasaportes adicionales son válidos por solo 6 años, ...",
+ "connector": "sin embargo,", "connector_type": "contrasting", "connection_type": "contrasting",
+ "extraction_strategy": "linking_phrase", "distance": 1.0,
+ "sentence_1_paragraph": 1, "sentence_1_position": 55,
+ "sentence_2_paragraph": 1, "sentence_2_position": 56,
+ "id": "esarticles__eswiki__...", "dataset": "esarticles__eswiki",
+ "genre": "articles", "domain": "encyclopedia"}
+```
+
+`connector_type` (connector-derived) and `connection_type` (human) are **identical on all 1,695
+rows** — the released set contains only pairs where annotation and connector agreed, so either
+column can be used as the gold label.
+
+Note `id` identifies the source article, not the pair, and is not unique. Join on
+`(sentence_1, sentence_2)`.
+
+## How candidates were chosen
+
+Candidates were sampled by **model confidence** before annotation: a preliminary pass with the
+released ESNLIR XLM-RoBERTa checkpoint binned them into four confidence strata (high, medium, low,
+none), concentrating annotation effort on uncertain instances.
+
+This has a consequence worth stating plainly. The set is **enriched for instances that checkpoint
+finds hard**, so that model's own score on it is biased downward. It still ranks highest among all
+systems evaluated, which is what makes the comparison robust to the selection effect — but this is
+not an i.i.d. sample of ESNLIR, and absolute numbers on it are not comparable to numbers on the
+full ESNLIR test split.
+
+## Reported results
+
+Macro F1 on this set, from the active-learning paper:
+
+| encoder | method | labels used | macro F1 |
+|---|---|---|---|
+| XLM-R | full supervision | 1,000k | **0.822** |
+| XLM-R | NegE (best, iter 5) | 610k | 0.796 |
+| XLM-R | LER (final, iter 7) | 800k | 0.789 |
+| Bertin | full supervision | 1,000k | 0.811 |
+| Bertin | NegE (best, iter 6) | 710k | 0.764 |
+| Bertin | LER (final, iter 7) | 800k | 0.754 |
+
+Active learning reaches 95.5% of full supervision with 61% of the labels, and NegE and LER are
+indistinguishable — since LER acquires uniformly at random from a filtered pool, energy-based
+acquisition shows no measurable advantage over near-random selection.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Flaglab/esnlir-al-annotated-test", split="test")
+print(ds[0]["sentence_1"], "|", ds[0]["connection_type"])
+```
+
+## Related
+
+| | |
+|---|---|
+| [`Flaglab/ESNLIR-dataset`](https://huggingface.co/datasets/Flaglab/ESNLIR-dataset) | the corpus this is drawn from |
+| [`Flaglab/esnlir-al-trajectories`](https://huggingface.co/datasets/Flaglab/esnlir-al-trajectories) | per-round AL metrics and pool indices |
+| [`Flaglab/ESNLIR-AL-XLM-RoBERTa-NegE`](https://huggingface.co/Flaglab/ESNLIR-AL-XLM-RoBERTa-NegE) | best AL checkpoint |
+
+## Citation
+
+```bibtex
+@misc{ortiz_esnlir_active_learning,
+  author = {Ortiz, Diego and Portela, Johan R. and Manrique, Ruben},
+  title  = {Active Learning for Spanish Natural Language Inference},
+  school = {Universidad de los Andes, Bogot{\'a}, Colombia},
+  year   = {2026},
+}
+```
+
+The corpus is ESNLIR ([Portela, Pérez-Terán & Manrique, 2026](https://doi.org/10.1007/978-3-032-07175-0_23)),
+a separate project with its own code and paper.