dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: instruction
dtype: string
- name: label_name
dtype: string
splits:
- name: train
num_bytes: 254483428
num_examples: 785404
- name: test
num_bytes: 6297986
num_examples: 19630
download_size: 54354034
dataset_size: 260781414
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
Dataset Card for "mnli-contrast"
This dataset is the mnli-3way dataset with an additional instruction feature.
This new feature along with its related label_name expresses how the premise and hypothesis features are related in the original dataset.
The following explains how the mapping is done:
If the original example was of class entailment
Two data points will be related to that example.
One is the positive example (i.e., label_name == "positive") which assign to it the folowing instruction: "The meaning of the hypothesis is logically inferred from the meaning of the premise."
The other is the negative example (i.e., label_name == "negative") which assign to it the folowing instruction: "The meaning of the hypothesis either contradicts the meaning of the premise, is unrelated to it, or does not provide sufficient information to infer the meaning of the premise."
If the original example was of class contradiction or neutral
Two data points will be related to that example.
One is the positive example (i.e., label_name == "positive") which assign to it the folowing instruction: "The meaning of the hypothesis either contradicts the meaning of the premise, is unrelated to it, or does not provide sufficient information to infer the meaning of the premise."
The other is the negative example (i.e., label_name == "negative") which assign to it the folowing instruction: "The meaning of the hypothesis is logically inferred from the meaning of the premise."
This dataset is double the size of this original dataset because each is related to a positive and negative instruction.