---
language:
- en
- ru
license: mit
task_categories:
- text-classification
dataset_info:
  config_name: simplified_ekman
  features:
  - name: ru_text
    dtype: string
  - name: text
    dtype: string
  - name: labels
    dtype:
      class_label:
        names:
          '0': sadness
          '1': joy
          '2': love
          '3': anger
          '4': fear
          '5': surprise
  - name: labels_ekman
    dtype:
      class_label:
        names:
          '0': anger
          '1': disgust
          '2': fear
          '3': joy
          '4': sadness
          '5': surprise
          '6': neutral
  splits:
  - name: train
    num_bytes: 103759867.36530161
    num_examples: 333447
  - name: validation
    num_bytes: 12970022.317349194
    num_examples: 41681
  - name: test
    num_bytes: 12970022.317349194
    num_examples: 41681
  download_size: 68831057
  dataset_size: 129699912.0
configs:
- config_name: simplified_ekman
  data_files:
  - split: train
    path: simplified_ekman/train-*
  - split: validation
    path: simplified_ekman/validation-*
  - split: test
    path: simplified_ekman/test-*
---
The original dataset: https://www.kaggle.com/datasets/nelgiriyewithana/emotions

The derived dataset was machine translated from English into Russian using the free Google Translate API (with [deep-translator](https://pypi.org/project/deep-translator/)). The translation script:

```python
import pandas as pd
from deep_translator import GoogleTranslator
from deep_translator.exceptions import TranslationNotFound

# Load dataset and drop the ID column
df = pd.read_csv("path_to_your_downloaded_file/text.csv").iloc[:, 1:]

translator = GoogleTranslator(source="en", target="ru")

def translate_samples(samples):
    texts = samples["text"].tolist()

    while True:
        try:
            translated = translator.translate_batch(texts)
            break
        except TranslationNotFound:
            print(f"Translation failed for '{texts}', retrying...")

    # Replace None with original text if translation is not applicable
    translated = [
        t if t is not None else orig
        for t, orig in zip(translated, texts)
    ]

    # Print replacements
    for t, orig in zip(translated, texts):
        if t == orig:
            print(f"Replaced {orig} with {t}")

    samples["ru_text"] = translated
    return samples

# Apply batch translation
batch_size = 500
translated_dataset = df.groupby(df.index // batch_size, group_keys=False).apply(translate_samples)
```

Column `labels` contain the following classes:
```yaml
0: sadness
1: joy
2: love
3: anger
4: fear
5: surprise
```

Column `labels_ekman` contains the Ekman emotion classes:
```yaml
0: anger
1: disgust - omitted in this dataset
2: fear
3: joy
4: sadness
5: surprise
6: neutral - omitted in this dataset
```
which were mapped from the original classes as follows:
```yaml
Original -> Ekman
sadness (0) -> sadness (4)
joy (1) -> joy (3)
love (2) -> joy (3)
anger (3) -> anger (0)
fear (4) -> fear (2)
surprise (5) -> surprise (5)
```