---
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:
- fr
---


# 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
    })
})
```