README.md

metadata

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.

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")