README.md

---
dataset_info:
- config_name: Behaviour
  features:
  - name: text
    dtype: string
  - name: choices
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  - name: label
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  - name: test
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- config_name: Synth
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  - name: label
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configs:
- config_name: Behaviour
  data_files:
  - split: train
    path: Behaviour/train-*
  - split: test
    path: Behaviour/test-*
- config_name: Synth
  data_files:
  - split: train
    path: Synth/train-*
  - split: test
    path: Synth/test-*
---

# Automatic Misogyny Identification (AMI)

Original Paper: https://amievalita2020.github.io

Task presented at EVALITA-2020

This task consists of tweet classification, specifically, categorization of the level of misogyny in a given text.

We taken both subtasks, *raw_dataset* uploaded as *Behaviour* (3 class classification) and *synthetic* uploaded as *Synth* (2 class classification).

## Example

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

### Behaviour

```json
{
  "text": string, # text of the tweet
  "label": int, # 0: Non Misogino, 1: Misogino, 2: Misogino Aggressivo
}
```

### Synth

```json
{
  "text": string, # text of the tweet
  "label": int, # 0: Non Misogino, 1: Misogino
}
```

## Statitics

| AMI Behaviour | Non Misogino | Misogino | Misogino Aggressivo |
| :--------: | :----: | :----: | :----: |
| Training | 2663 | 554 | 1783 |
| Test | 500 | 324 | 176 |

| AMI Synth | Non Misogino | Misogino |
| :--------: | :----: | :----: |
| Training | 3670 | 3344 |
| Test | 1454 | 1454 |


## 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.

### Behaviour

Description of the task: "Indica il livello di misoginia presente nei seguenti tweets."


#### Cloze Style:

Label (**Non Misogino**): "Tweet: '{{text}}'.\nIl tweet non presenta alcun elemento misogino"

Label (**Misogino**): "Tweet: '{{text}}'.\nIl tweet presenta caratteristiche misogine"

Label (**Misogino Aggressivo**): "Tweet: '{{text}}'.\nIl tweet presenta caratteristiche misogine aggressive"

#### MCQA Style:

```txt
Tweet: '{{text}}'.\nDomanda: che livello di misoginia è presente nel tweet?\nA. Nessuno\nB. Misogino\nC. Misogino Aggressivo\nRisposta:
```

### Synth
Description of the task: "Indica se i seguenti tweets presentano caratteristiche o elementi misogini."

#### Cloze Style:

Label (**Non Misogino**): "Tweet: '{{text}}'.\nIl tweet non presenta alcun elemento misogino"

Label (**Misogino**): "Tweet: '{{text}}'.\nIl tweet presenta caratteristiche misogine"


#### MCQA Style:

```txt
Tweet: '{{text}}'.\nDomanda: Il tweet contiene elementi misogini (Rispondi sì o no)?
```

## Some Results

| AMI Synth | ACCURACY (5-shots) |
| :-----: | :--: |
| Gemma-2B | 53.78 |
| QWEN2-1.5B | 60.72 |
| Mistral-7B | 71.59 |
| ZEFIRO | 74.69 |
| Llama-3-8B | 74.55 |
| Llama-3-8B-IT | 78.47 |
| ANITA | 82.66 |

## Acknowledge

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

Further, We want to thanks the student of [MNLP-2024 course](https://naviglinlp.blogspot.com/), where with their first homework tried different interesting prompting strategies.

The data can be freely downloaded form this [link](https://live.european-language-grid.eu/catalogue/corpus/7005/download/).

## License

The data come under license [Creative Commons Attribution Non Commercial Share Alike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).