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---
dataset_info:
- config_name: Behaviour
features:
- name: text
dtype: string
- name: choices
sequence: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 883966
num_examples: 5000
- name: test
num_bytes: 183067
num_examples: 1000
download_size: 458408
dataset_size: 1067033
- config_name: Synth
features:
- name: text
dtype: string
- name: choices
sequence: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 800941
num_examples: 7014
- name: test
num_bytes: 248483
num_examples: 2908
download_size: 502169
dataset_size: 1049424
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."
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"
### Synth
Description of the task: "Indica se i seguenti tweets presentano caratteristiche o elementi misogini."
Label (**Non Misogino**): "Tweet: '{{text}}'.\nIl tweet non presenta alcun elemento misogino"
Label (**Misogino**): "Tweet: '{{text}}'.\nIl tweet presenta caratteristiche misogine"
## 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 | |