---
license: cc-by-4.0
language:
- es
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: ESNLIR — confidence-stratified annotation candidates (2,664 pairs)
size_categories:
- 1K<n<10K
tags:
- nli
- spanish
- esnlir
- active-learning
- annotation
- model-confidence
configs:
- config_name: default
  data_files:
  - split: train
    path: annotation_candidates.jsonl
---

# ESNLIR — confidence-stratified annotation candidates

The **2,664 candidate pairs sent to human annotators**, each carrying the model confidence score and
the stratum it was drawn from. This is the pool *before* annotation; the labelled result is
[`Flaglab/esnlir-al-annotated-test`](https://huggingface.co/datasets/Flaglab/esnlir-al-annotated-test).

> **Active Learning for Spanish Natural Language Inference on a Heterogeneous Multi-Domain Corpus**
> Diego Ortiz, Johan R. Portela, Ruben Manrique — Universidad de los Andes, Bogotá
> *Advances in Artificial Intelligence — IBERAMIA 2026* (to appear)

Selection code: [`jd-rodriguezp1234/esnlir-active-learning`](https://github.com/jd-rodriguezp1234/esnlir-active-learning)
(`notebooks/annotation/`)

## Why this exists

Annotating uniformly at random wastes effort on instances every model already gets right. Candidates
were instead **stratified by model confidence**, so annotation concentrated where the labels were
most informative. Releasing the pool lets you see exactly what was offered for labelling, and
compare it against what survived — useful for anyone studying selection effects in evaluation sets.

## The strata

Confidence is the **mean maximum softmax probability across both ESNLIR baselines** —
[`Flaglab/ESNLIR-RoBERTa`](https://huggingface.co/Flaglab/ESNLIR-RoBERTa) and
[`Flaglab/ESNLIR-XLM-RoBERTa`](https://huggingface.co/Flaglab/ESNLIR-XLM-RoBERTa) — not a single
model's score.

| `confidence_config` | criterion | n | confidence range | mean |
|---|---|---|---|---|
| `high` | both models correct | 1,856 | 0.965 – 0.999 | 0.991 |
| `medium` | both models correct | 284 | 0.600 – 0.749 | 0.688 |
| `low` | both models correct | 248 | 0.298 – 0.494 | 0.398 |
| `no` | **not** both correct | 276 | 0.337 – 0.983 | 0.626 |

**`no` is not a confidence band.** It is defined by the models being *wrong*, so its confidence
spans almost the entire interval — a pair can sit here at 0.98, meaning the models were confidently
mistaken. Those are the most diagnostic instances in the pool, and they are invisible to any scheme
that stratifies on confidence alone.

Labels are balanced at **666 per class** across the four relations.

| genre | n | | genre | n |
|---|---|---|---|---|
| `theses` | 852 | | `articles` | 256 |
| `books` | 620 | | `legal` | 208 |
| `comments` | 380 | | `talks` | 8 |
| `news` | 340 | | | |

## Fields

Standard ESNLIR fields plus two:

| field | meaning |
|---|---|
| `confidence` | mean max-softmax across the two baselines, in [0, 1] |
| `confidence_config` | the stratum: `high`, `medium`, `low` or `no` |

`connector_type` is the connector-derived label used as ground truth when deciding whether the
models were correct. There is **no human label in this file** — that is the point; these are
pre-annotation candidates.

## What happened next

2,664 candidates → annotated by 12 annotators, 3 labels per instance via overlapping assignment
windows → majority vote → **retained only where the majority matched `connector_type`** → 151
disagreements discarded → **1,695** released as
[`Flaglab/esnlir-al-annotated-test`](https://huggingface.co/datasets/Flaglab/esnlir-al-annotated-test).

Not every candidate was annotated; the labelled subset is smaller than this pool. Comparing the two
files shows both which candidates reached annotators and which survived the agreement filter.

## Usage

```python
from datasets import load_dataset

pool = load_dataset("Flaglab/esnlir-annotation-candidates", split="train")

hard = pool.filter(lambda r: r["confidence_config"] == "no")
print(len(hard), "instances both baselines got wrong")

confident_and_wrong = pool.filter(
    lambda r: r["confidence_config"] == "no" and r["confidence"] > 0.9)
print(len(confident_and_wrong), "confidently wrong")
```

## Citation

```bibtex
@InProceedings{ortiz2026activelearningspanishnli,
  author    = {Ortiz, Diego and Portela, Johan R. and Manrique, Ruben},
  title     = {Active Learning for Spanish Natural Language Inference
               on a Heterogeneous Multi-Domain Corpus},
  booktitle = {Advances in Artificial Intelligence -- IBERAMIA 2026},
  year      = {2026},
  publisher = {Springer Nature Switzerland},
  address   = {Cham},
  note      = {To appear},
}
```

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.