update

README.md

@@ -26,7 +26,7 @@ dataset_info:
   - name: test
     num_bytes: 2967746
     num_examples: 2338
-  download_size: 0
+  download_size: 25566918
   dataset_size: 24732178
 - config_name: NLI_with_context
   features:
@@ -40,7 +40,7 @@ dataset_info:
   - name: train
     num_bytes: 2977929
     num_examples: 2551
-  download_size: 0
+  download_size: 3042193
   dataset_size: 2977929
 - config_name: NLI_without_context
   features:
@@ -52,7 +52,7 @@ dataset_info:
   - name: train
     num_bytes: 1095335
     num_examples: 2551
-  download_size: 0
+  download_size: 1146864
   dataset_size: 1095335
 - config_name: PIR_first
   features:
@@ -72,7 +72,7 @@ dataset_info:
   - name: test
     num_bytes: 687605
     num_examples: 668
-  download_size: 0
+  download_size: 4366468
   dataset_size: 4291965
 - config_name: PIR_second
   features:
@@ -96,7 +96,7 @@ dataset_info:
   - name: test
     num_bytes: 1890798
     num_examples: 1062
-  download_size: 0
+  download_size: 11740508
   dataset_size: 11553833
 ---
 # The Dataset Presented Here is NOT Ready, please download from the [website](https://diplomat-dataset.github.io)
@@ -116,8 +116,8 @@ The **DiPlomat** dataset owns 4,177 data and covers a vocabulary of 48,900 words
 More than that, human-annotated answers reach an amount of 6,494, 
 hold a vocabulary size of 20,000, and cover 5 types of reasoning. 
 Along with the dataset, we propose two tasks:  
-Pragmatic Identification and Reasoning (PIR) and Conversational Question Answering. Furthermore, we provide the
-data that we use for zero-NLI in the [paper](https://arxiv.org/abs/2306.09030).
+**Pragmatic Identification and Reasoning (PIR)** and **Conversational Question Answering**. Furthermore, we provide the
+data that we use for **zero-NLI** in the [paper](https://arxiv.org/abs/2306.09030).
 
 
 - **Language(s) (NLP):** [English]
@@ -135,10 +135,13 @@ data that we use for zero-NLI in the [paper](https://arxiv.org/abs/2306.09030).
 ## Dataset Structure
 
 <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-We provide 5 fields. ``PIR_first_subtask_dataset``, ``PIR_second_subtask_dataset``is for the pragmatic 
-identification and reasoning (PIR) task, and ``CQA_task_dataset`` is for the conversational question answering (CQA)
-task. As for ``NLI_task_dataset`` and ``NLI_dataset_without_context``, they are data for zero-NLI, while the latter
-is a context-removed version.
+| Field | Task|
+| PIR_first | PIR Subtask 1|
+| PIR_second | PIR Subtask 2|
+| CQA | CQA|
+| NLI_with_context | Zero-Shot NLI|
+| NLI_without_context | Zero-Shot NLI|
+