metadata

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:

Proposed Prompts: