chore: fix yml

README.md

@@ -1,25 +1,25 @@
 ---
 annotations_creators:
-- crowdsourced
-  language_creators:
-- crowdsourced
-  language:
-- en
-  license:
-- cc-by-4.0
-  multilinguality:
-- monolingual
-  size_categories:
-- 100K<n<1M
-  source_datasets:
-- extended|snli
-  task_categories:
-- text-classification
-  task_ids:
-- natural-language-inference
-- multi-input-text-classification
-  pretty_name: Counterfactual Instances for Stanford Natural Language Inference
-  dataset_info:
+  - crowdsourced
+language_creators:
+  - crowdsourced 
+language:
+  - en 
+license:
+  - cc-by-4.0 
+multilinguality:
+  - monolingual 
+size_categories:
+  - 1K<n<10K 
+source_datasets:
+  - extended|snli 
+task_categories:
+  - text-classification 
+task_ids:
+  - natural-language-inference
+  - multi-input-text-classification 
+pretty_name: Counterfactual Instances for Stanford Natural Language Inference 
+dataset_info:
   features:
     - name: premise
       dtype: string
@@ -28,19 +28,17 @@ annotations_creators:
     - name: label
       dtype: string
   splits:
-    - name: test
-      num_bytes: 1263912
-      num_examples: 10000
     - name: train
-      num_bytes: 66159510
-      num_examples: 550152
-    - name: validation
-      num_bytes: 1268044
-      num_examples: 10000
-      download_size: 94550081
-      dataset_size: 68691466
+      num_bytes: 1771712
+      num_examples: 8300
+    - name: dev
+      num_bytes: 217479
+      num_examples: 1000
+    - name: test
+      num_bytes: 437468
+      num_examples: 2000
 ---
-# Dataset Card for SNLI
+# Dataset Card for Counterfactually Augmented SNLI
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)
@@ -60,7 +58,7 @@ annotations_creators:
 
 ### Dataset Summary
 
-The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE). In the paper [Learning the Difference that Makes a Difference with Counterfactually-Augmented Data](https://openreview.net/forum?id=Sklgs0NFvr), Kaushik et. al. provided a dataset with counterfactual perturbations on the SNLI and IMDB data. This repository contains the original and counterfactual perturbations for the SNLI data, which was generated after processing the original data from [here](https://github.com/acmi-lab/counterfactually-augmented-data).
+The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE). In the ICLR 2020 paper [Learning the Difference that Makes a Difference with Counterfactually-Augmented Data](https://openreview.net/forum?id=Sklgs0NFvr), Kaushik et. al. provided a dataset with counterfactual perturbations on the SNLI and IMDB data. This repository contains the original and counterfactual perturbations for the SNLI data, which was generated after processing the original data from [here](https://github.com/acmi-lab/counterfactually-augmented-data).
 
 ### Languages