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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
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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
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