Dataset with sentences regarding professions, half of the translations are to feminine and half for masculine sentences.

How to use it: 
```
from datasets import load_dataset
remote_dataset = load_dataset("VanessaSchenkel/handmade-dataset", field="data")
remote_dataset
```
Output:
```
DatasetDict({
    train: Dataset({
        features: ['id', 'translation'],
        num_rows: 388
    })
})
```
Exemple: 
```
remote_dataset["train"][5]
```
Output:
```
{'id': '5',
 'translation': {'english': 'the postman finished her work .',
  'portuguese': 'A carteira terminou seu trabalho .'}} 
```
task_categories:translation annotations_creators:found language_creators:found multilinguality:translation size_categories:n<1K source_datasets:original language:en language:pt license:afl-3.0 region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en", "pt"], "license": ["afl-3.0"], "multilinguality": ["translation"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "VanessaSchenkel/handmade-dataset", "tags": []}
TAGS
#task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-n<1K #source_datasets-original #language-English #language-Portuguese #license-afl-3.0 #region-us 

Dataset with sentences regarding professions, half of the translations are to feminine and half for masculine sentences.

How to use it: 

Output:

Exemple: 

Output:

TAGS
#task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-n<1K #source_datasets-original #language-English #language-Portuguese #license-afl-3.0 #region-us 
