Update README

README.md

@@ -35,7 +35,7 @@ dataset_info:
     dtype: string
   - name: title
     dtype: string
-  - name: sent_ids
+  - name: ids
     sequence: string
   - name: sentences
     sequence: string
@@ -46,26 +46,26 @@ dataset_info:
       class_label:
         names:
           '0': semantic-continuity
-          '1': semantic-break
+          '1': semantic-shift
   splits:
   - name: train
-    num_bytes: 99374519
+    num_bytes: 105236889
     num_examples: 13679
   - name: validation
-    num_bytes: 14819645
+    num_bytes: 15693016
     num_examples: 1953
   - name: test
-    num_bytes: 29405354
+    num_bytes: 31140798
     num_examples: 3907
   download_size: 94042594
-  dataset_size: 143599518
+  dataset_size: 152070703
 - config_name: en_disease
   features:
   - name: id
     dtype: string
   - name: title
     dtype: string
-  - name: sent_ids
+  - name: ids
     sequence: string
   - name: sentences
     sequence: string
@@ -76,18 +76,61 @@ dataset_info:
       class_label:
         names:
           '0': semantic-continuity
-          '1': semantic-break
+          '1': semantic-shift
   splits:
   - name: train
-    num_bytes: 21279366
+    num_bytes: 22409988
     num_examples: 2513
   - name: validation
-    num_bytes: 3029140
+    num_bytes: 3190201
     num_examples: 359
   - name: test
-    num_bytes: 5778276
+    num_bytes: 6088470
     num_examples: 718
   download_size: 94042594
-  dataset_size: 30086782
+  dataset_size: 31688659
 ---
-# Dataset Card for WikiSection (en_city, en_disease) Dataset
+# Dataset Card for WikiSection (en_city, en_disease) Dataset
+
+The WikiSection dataset is a collection of segmented Wikipedia articles related to cities and diseases, structured in this repository for a sentence-level document segmentation task.
+
+## Dataset Overview
+
+WikiSection contains two English subsets:
+- **en_city**: 19.5k Wikipedia articles about cities and city-related topics.
+- **en_disease**: 3.6k articles on diseases and health-related scientific information.
+
+Each subset provides segmented articles, where the task is to classify sentence boundaries as either "semantic-continuity" or "semantic-shift."
+
+## Features
+
+The dataset provides the following features:
+
+- **id**: `string` - A unique identifier for each document.
+- **title**: `string` - The title of the document.
+- **ids**: `list[string]` - The sentence ids within the document
+- **sentences**: `list[string]` - The sentences within the document.
+- **titles_mask**: `list[uint8]` - A binary mask to indicate which sentences are titles.
+- **labels**: `list[int]` - Binary labels for each sentence, where `0` represents "semantic-continuity" and `1` represents "semantic-shift."
+
+## Usage
+
+The dataset can be easily loaded using the HuggingFace `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# en_city
+titled_en_city = load_dataset('saeedabc/wikisection', 'en_city', trust_remote_code=True)
+
+untitled_en_city = load_dataset('saeedabc/wikisection', 'en_city', drop_titles=True, trust_remote_code=True)
+
+# en_disease
+titled_en_disease = load_dataset('saeedabc/wikisection', 'en_disease', trust_remote_code=True)
+
+untitled_en_disease = load_dataset('saeedabc/wikisection', 'en_disease', drop_titles=True, trust_remote_code=True)
+```
+
+## Dataset Details
+
+- **Homepage**: [WikiSection on GitHub](https://github.com/sebastianarnold/WikiSection)

wikisection.py

@@ -18,8 +18,8 @@ See https://github.com/sebastianarnold/WikiSection for more information.
 
 Usage:
     >>> from datasets import load_dataset
-    >>> dataset = load_dataset('saeedabc/wikisection', 'en_city', trust_remote_code=True, num_proc=8)
-    >>> dataset = load_dataset('saeedabc/wikisection', 'en_disease', trust_remote_code=True, num_proc=8)
+    >>> dataset = load_dataset('saeedabc/wikisection', 'en_city', trust_remote_code=True)
+    >>> dataset = load_dataset('saeedabc/wikisection', 'en_disease', trust_remote_code=True)
 """
 
 
@@ -120,7 +120,7 @@ class WikiSection(datasets.GeneratorBasedBuilder):
                     datasets.Value("uint8")
                 ),
                 "labels": datasets.Sequence(
-                    datasets.ClassLabel(num_classes=2, names=['semantic-continuity', 'semantic-break'])
+                    datasets.ClassLabel(num_classes=2, names=['semantic-continuity', 'semantic-shift'])
                 ),
             }
         )