| --- |
| dataset_info: |
| features: |
| - name: review |
| dtype: string |
| - name: label |
| dtype: int64 |
| splits: |
| - name: train |
| num_bytes: 91153465 |
| num_examples: 159443 |
| - name: validation |
| num_bytes: 11526130 |
| num_examples: 19933 |
| - name: test |
| num_bytes: 11522522 |
| num_examples: 19928 |
| download_size: 75005133 |
| dataset_size: 114202117 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: validation |
| path: data/validation-* |
| - split: test |
| path: data/test-* |
| task_categories: |
| - text-classification |
| language: |
| - fra |
| --- |
| |
|
|
| # Allocine_clean |
| |
| In the [allocine](https://huggingface.co/datasets/tblard/allocine) dataset there are leaks and duplicated data: |
| - Leakage between train split and test split: 23 |
| - Leakage between validation split and test split: 15 |
| - Duplicated lines in the train split: 534 |
| - Duplicated lines in the validation split: 52 |
| - Duplicated lines in the test split: 72 |
| |
| In all, this means 0.6% of test data are biased. |
| |
| |
| So this version is a cleaned version of the allocine dataset, i.e. without leaks and duplicated data. |
| It is likely that the resulting dataset is still imperfect, with annotation problems requiring further proofreading/correction. |
| |
| ``` |
| DatasetDict({ |
| train: Dataset({ |
| features: ['review', 'label'], |
| num_rows: 159443 #160000 before |
| }) |
| validation: Dataset({ |
| features: ['review', 'label'], |
| num_rows: 19933 #20000 before |
| }) |
| test: Dataset({ |
| features: ['review', 'label'], |
| num_rows: 19928 #20000 before |
| }) |
| }) |
| ``` |
