Update README.md

README.md

@@ -44,4 +44,51 @@ configs:
     path: data/test-*
   - split: validation
     path: data/validation-*
+language:
+- en
+pretty_name: open-jev-refactored
+task_categories:
+- text-classification
+tags:
+- Jev
+- Decision-modelling
+- Curated-Dataset
 ---
+
+### Sample script to tokenize the dataset:
+```
+import datasets
+from nltk.tokenize import wordpunct_tokenize
+
+hf_openjev_dataset_id = "Man1103/OpenJev-Refactored"
+hf_openjev_dataset = datasets.load_dataset(path=hf_openjev_dataset_id, cache_dir="/content/data/jev/curated")
+
+def get_corpus(dataset):
+    total_corpus_list = []
+    for data in dataset:
+        for qno_target in data["qno-target"]:
+            question = qno_target["question"]
+            options = ",".join([option for option in qno_target["options"]])
+            total_corpus_list.append(question + options)
+    return total_corpus_list
+
+curated_dataset_train = hf_openjev_dataset["train"]
+train_list = get_corpus(curated_dataset_train)
+
+curated_dataset_test = hf_openjev_dataset["test"]
+test_list = get_corpus(curated_dataset_test)
+
+curated_dataset_val = hf_openjev_dataset["validation"]
+val_list = get_corpus(curated_dataset_val)
+
+total_corpus = train_list + test_list + val_list
+total_corpus = ",".join(total_corpus)
+
+tokens = wordpunct_tokenize(total_corpus)
+vocab = set(tokens)
+
+token2idx = {token: idx for idx, token in enumerate(vocab)}
+idx2token = {idx: token for idx, token in enumerate(vocab)}
+
+print(f"Token count: {len(tokens)}, Vocab size: {len(vocab)}")
+```