|
--- |
|
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 | |