Test

README.md

@@ -1,11 +1,4 @@
 ---
-language:
-- ar
-- en
-license: cc-by-4.0
-task_categories:
-- question-answering
-pretty_name: Quran Question Answer with Context
 dataset_info:
   features:
   - name: q_id
@@ -37,48 +30,45 @@ dataset_info:
   - name: chapter_no
     dtype: int64
   - name: verse
+    sequence: string
+  - name: question_en
     dtype: string
   - name: answer_en
     dtype: string
-  - name: class_en
+  - name: q_word_en
+    dtype: string
+  - name: q_topic_en
     dtype: string
   - name: fine_class_en
     dtype: string
-  - name: ontology_concept2_en
+  - name: class_en
     dtype: string
   - name: ontology_concept_en
     dtype: string
-  - name: q_topic_en
-    dtype: string
-  - name: q_word_en
-    dtype: string
-  - name: question_en
-    dtype: string
   - name: chapter_name_en
     dtype: string
-  - name: verse_list
-    sequence: int64
   - name: context
     dtype: string
-  - name: context_data
-    dtype: string
-  - name: context_missing_verses
-    dtype: string
   splits:
   - name: train
-    num_bytes: 3534771
-    num_examples: 1224
-  download_size: 1858762
-  dataset_size: 3534771
+    num_bytes: 2226830.0310711367
+    num_examples: 978
+  - name: test
+    num_bytes: 557845.9689288634
+    num_examples: 245
+  download_size: 1515128
+  dataset_size: 2784676.0
+license: cc-by-4.0
+task_categories:
+- question-answering
+pretty_name: Quran Question Answer with Context
+language:
+- ar
+- en
 tags:
 - islam
 - quran
 - arabic
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
 ---
 # Dataset Card for "quran-question-answer-context"
 
@@ -94,11 +84,16 @@ from datasets import load_dataset
 dataset = load_dataset("nazimali/quran-question-answer-context")
 ```
 
+
 ```python
 DatasetDict({
     train: Dataset({
-        features: ['q_id', 'question', 'answer', 'q_word', 'q_topic', 'fine_class', 'class', 'ontology_concept', 'ontology_concept2', 'source', 'q_src_id', 'quetion_type', 'chapter_name', 'chapter_no', 'verse', 'answer_en', 'class_en', 'fine_class_en', 'ontology_concept2_en', 'ontology_concept_en', 'q_topic_en', 'q_word_en', 'question_en', 'chapter_name_en', 'verse_list', 'context', 'context_data', 'context_missing_verses'],
-        num_rows: 1224
+        features: ['q_id', 'question', 'answer', 'q_word', 'q_topic', 'fine_class', 'class', 'ontology_concept', 'ontology_concept2', 'source', 'q_src_id', 'quetion_type', 'chapter_name', 'chapter_no', 'verse', 'question_en', 'answer_en', 'q_word_en', 'q_topic_en', 'fine_class_en', 'class_en', 'ontology_concept_en', 'chapter_name_en', 'context'],
+        num_rows: 978
+    })
+    test: Dataset({
+        features: ['q_id', 'question', 'answer', 'q_word', 'q_topic', 'fine_class', 'class', 'ontology_concept', 'ontology_concept2', 'source', 'q_src_id', 'quetion_type', 'chapter_name', 'chapter_no', 'verse', 'question_en', 'answer_en', 'q_word_en', 'q_topic_en', 'fine_class_en', 'class_en', 'ontology_concept_en', 'chapter_name_en', 'context'],
+        num_rows: 245
     })
 })
 ```

data/{test-00000-of-00001.parquet → test-00000-of-00001-cdd117c8335dd993.parquet}

@@ -1,3 +1,3 @@
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data/{train-00000-of-00004.parquet → train-00000-of-00001-71ba37266bea7b82.parquet}

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