Update README.md

README.md

@@ -120,7 +120,64 @@ dataset_info:
     num_examples: 1765
   download_size: 3231436
   dataset_size: 8186989
+task_categories:
+- text-classification
+size_categories:
+- 10K<n<100K
 ---
 # Dataset Card for "nlbse_ccc"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+A dataset object for the NLBSE'23 Code Comment Classification competition. Please refer to the original [Github repo for more details](https://github.com/nlbse2023/code-comment-classification).
+
+## Category distribution in the training and test sets
+
+The table below shows the distribution of positive/negative sentences for each category in the training and testing sets.
+
+| Language | Category           | Training | Training | Testing  | Testing  | Total  |
+|----------|--------------------|---------:|---------:|---------:|---------:|-------:|
+|          |                    | **Positive** | **Negative** | **Positive** | **Negative** |        |
+| Java     | Expand             |      505 |     1426 |      127 |      360 |   2418 |
+| Java     | Ownership          |       90 |     1839 |       25 |      464 |   2418 |
+| Java     | Deprecation        |      100 |     1831 |       27 |      460 |   2418 |
+| Java     | Rational           |      223 |     1707 |       57 |      431 |   2418 |
+| Java     | Summary            |      328 |     1600 |       87 |      403 |   2418 |
+| Java     | Pointer            |      289 |     1640 |       75 |      414 |   2418 |
+| Java     | Usage              |      728 |     1203 |      184 |      303 |   2418 |
+|          |                    	 | **Positive** | **Negative** | **Positive** | **Negative** |        |
+| Pharo    | Responsibilities   	 |      267 |     1139 |       69 |      290 |   1765 |
+| Pharo    | Keymessages        	 |      242 |     1165 |       63 |      295 |   1765 |
+| Pharo    | Keyimplementationpoints |      184 |     1222 |       48 |      311 |   1765 |
+| Pharo    | Collaborators      	 |       99 |     1307 |       28 |      331 |   1765 |
+| Pharo    | Example            	 |      596 |      812 |      152 |      205 |   1765 |
+| Pharo    | Classreferences    	 |       60 |     1348 |       17 |      340 |   1765 |
+| Pharo    | Intent             	 |      173 |     1236 |       45 |      311 |   1765 |
+|          |                    | **Positive** | **Negative** | **Positive** | **Negative** |        |
+| Python   | Expand             |      402 |     1637 |      102 |      414 |   2555 |
+| Python   | Parameters         |      633 |     1404 |      161 |      357 |   2555 |
+| Python   | Summary            |      361 |     1678 |       93 |      423 |   2555 |
+| Python   | Developmentnotes   |      247 |     1792 |       65 |      451 |   2555 |
+| Python   | Usage              |      637 |     1401 |      163 |      354 |   2555 |
+
+## Code
+
+The following code snippet was used to create the dataset:
+
+```
+# !git clone https://github.com/nlbse2023/code-comment-classification.git 
+from datasets import DatasetDict
+
+langs = ['java', 'python', 'pharo']
+lan_cats = []
+dataset_dict = DatasetDict()
+for lan in langs: # for each language
+    df = pd.read_csv(f'./code-comment-classification/{lan}/input/{lan}.csv')
+    df['label'] = df.instance_type
+    df['combo'] = df[['comment_sentence', 'class']].agg(' | '.join, axis=1)
+    print(df.columns)
+    cats = list(map(lambda x: lan + '_' + x, list(set(df.category))))
+    lan_cats = lan_cats + cats
+    for cat in list(set(df.category)): # for each category
+        filtered =  df[df.category == cat]
+        dataset_dict[f'{lan}_{cat}'] = Dataset.from_pandas(filtered)
+dataset_dict.push_to_hub("AISE-TUDelft/nlbse_ccc", token='hf_********************')
+```