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---
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path: data/pharo_Intent-*
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dataset_info:
features:
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dtype: int64
- name: class
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- name: comment_sentence
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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](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_********************')
```
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