|
--- |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: train |
|
path: data/train-* |
|
- split: dev |
|
path: data/dev-* |
|
- split: test |
|
path: data/test-* |
|
- split: gen |
|
path: data/gen-* |
|
- split: train_100 |
|
path: data/train_100-* |
|
dataset_info: |
|
features: |
|
- name: input |
|
dtype: string |
|
- name: output |
|
dtype: string |
|
- name: domain |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 4363969 |
|
num_examples: 24155 |
|
- name: dev |
|
num_bytes: 549121 |
|
num_examples: 3000 |
|
- name: test |
|
num_bytes: 548111 |
|
num_examples: 3000 |
|
- name: gen |
|
num_bytes: 5721102 |
|
num_examples: 21000 |
|
- name: train_100 |
|
num_bytes: 5592847 |
|
num_examples: 39500 |
|
download_size: 5220150 |
|
dataset_size: 16775150 |
|
--- |
|
# Dataset Card for "COGS" |
|
|
|
It contains the dataset used in the paper [COGS: A Compositional Generalization Challenge Based on Semantic Interpretation.](https://aclanthology.org/2020.emnlp-main.731.pdf) |
|
|
|
It has four splits, where **gen** refers to the generalization split and **train_100** refers to the training version with 100 primitive exposure examples. |
|
|
|
You can use it by calling: |
|
``` |
|
train_data = datasets.load_dataset("Punchwe/COGS", split="train") |
|
train100_data = datasets.load_dataset("Punchwe/COGS", split="train_100") |
|
gen_data = datasets.load_dataset("Punchwe/COGS", split="gen") |
|
``` |
