---
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: text
    dtype: string
  - name: choices
    sequence: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 460376
    num_examples: 5837
  - name: test
    num_bytes: 1203852
    num_examples: 14560
  download_size: 466009
  dataset_size: 1664228
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# Presupposed Taxonomies: Evaluating Neural Network Semantics (PreTENS) 

Original Paper: https://aclanthology.org/2022.semeval-1.29.pdf

This dataset comes from SemEVAL-2022 shared tasks.

The PreTENS task aims at focusing on semantic competence with specific attention on the evaluation of language models with respect to the  recognition of appropriate taxonomic relations between two nominal arguments.

We collected the Italian part of the original dataset, and more specifically only the first sub-task: **acceptability sentence classification**.

## Example

Here you can see the structure of the single sample in the present dataset.

```json
{
  "text": string, # sample's text
  "label": int, # 0: non ha senso, 1: ha senso
}
```

## Statitics

| PRETENS | 0 | 1 |
| :--------: | :----: | :----: |
| Training | 3029 | 2808 |
| Test | 7707 | 6853 |

## Proposed Prompts

Here we will describe the prompt given to the model over which we will compute the perplexity score, as model's answer we will chose the prompt with lower perplexity.
Moreover, for each subtask, we define a description that is prepended to the prompts, needed by the model to understand the task.

Description of the task: "Indica se le seguenti frasi hanno senso a livello semantico.\n\n"

### Cloze Style:

Label (**non ha senso**): "{{text}}\nLa frase precedente non ha senso"

Label (**ha senso**): "{{text}}\nLa frase precedente ha senso"

### Cloze Style:

```txt
{{text}}\nDomanda: La frase precedente ha semanticamente senso (Rispondi sì o no)?
```

## Some Results

| PRETENS | ACCURACY (15-shots) |
| :-----: | :--: |
| Gemma-2B | 53.5 |
| QWEN2-1.5B | 56.47 |
| Mistral-7B | 66.5 |
| ZEFIRO | 62 |
| Llama-3-8B | 72.34 |
| Llama-3-8B-IT | 65.58 |
| ANITA | 66.1 |

## Aknowledgement

We want to thanks the authors of this resource to publicly release such interesting benchmark.

The data can be freely downloaded form this [link](https://github.com/shammur/SemEval2022Task3)

## License

The data come under [MIT](https://opensource.org/license/mit) license.