README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: java_Pointer
        path: data/java_Pointer-*
      - split: java_Expand
        path: data/java_Expand-*
      - split: java_Ownership
        path: data/java_Ownership-*
      - split: java_deprecation
        path: data/java_deprecation-*
      - split: java_rational
        path: data/java_rational-*
      - split: java_summary
        path: data/java_summary-*
      - split: java_usage
        path: data/java_usage-*
      - split: python_Expand
        path: data/python_Expand-*
      - split: python_Summary
        path: data/python_Summary-*
      - split: python_DevelopmentNotes
        path: data/python_DevelopmentNotes-*
      - split: python_Parameters
        path: data/python_Parameters-*
      - split: python_Usage
        path: data/python_Usage-*
      - split: pharo_Example
        path: data/pharo_Example-*
      - split: pharo_Keymessages
        path: data/pharo_Keymessages-*
      - split: pharo_Responsibilities
        path: data/pharo_Responsibilities-*
      - split: pharo_Keyimplementationpoints
        path: data/pharo_Keyimplementationpoints-*
      - split: pharo_Collaborators
        path: data/pharo_Collaborators-*
      - split: pharo_Intent
        path: data/pharo_Intent-*
      - split: pharo_Classreferences
        path: data/pharo_Classreferences-*
dataset_info:
  features:
    - name: comment_sentence_id
      dtype: int64
    - name: class
      dtype: string
    - name: comment_sentence
      dtype: string
    - name: partition
      dtype: int64
    - name: instance_type
      dtype: int64
    - name: category
      dtype: string
    - name: label
      dtype: int64
    - name: combo
      dtype: string
    - name: __index_level_0__
      dtype: int64
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      num_examples: 2418
    - name: java_Expand
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      num_examples: 2418
    - name: java_Ownership
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    - name: java_deprecation
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    - name: java_rational
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      num_examples: 2418
    - name: java_summary
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      num_examples: 2418
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    - name: python_Expand
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    - name: python_Summary
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    - name: python_DevelopmentNotes
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    - name: python_Usage
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    - name: pharo_Example
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      num_examples: 1765
    - name: pharo_Keymessages
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      num_examples: 1765
    - name: pharo_Responsibilities
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      num_examples: 1765
    - name: pharo_Keyimplementationpoints
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      num_examples: 1765
    - name: pharo_Collaborators
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      num_examples: 1765
    - name: pharo_Intent
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      num_examples: 1765
    - name: pharo_Classreferences
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      num_examples: 1765
  download_size: 3231436
  dataset_size: 8186989
task_categories:
  - text-classification
size_categories:
  - 10K<n<100K

Dataset Card for "nlbse_ccc"

A dataset object for the NLBSE'23 Code Comment Classification competition. Please refer to the original Github repo for more details.

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_********************')