---
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).

Data statistics:
- add

Proposed Prompts:
- add