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--- |
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license: mit |
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dataset_info: |
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config_name: google_translator |
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features: |
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- name: lt_text |
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dtype: string |
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- name: text |
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dtype: string |
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- name: labels |
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sequence: |
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class_label: |
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names: |
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'0': admiration |
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'1': amusement |
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'2': anger |
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'3': annoyance |
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'4': approval |
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'5': caring |
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'6': confusion |
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'7': curiosity |
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'8': desire |
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'9': disappointment |
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'10': disapproval |
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'11': disgust |
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'12': embarrassment |
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'13': excitement |
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'14': fear |
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'15': gratitude |
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'16': grief |
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'17': joy |
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'18': love |
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'19': nervousness |
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'20': optimism |
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'21': pride |
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'22': realization |
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'23': relief |
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'24': remorse |
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'25': sadness |
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'26': surprise |
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'27': neutral |
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- name: labels_ekman |
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sequence: |
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class_label: |
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names: |
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'0': anger |
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'1': disgust |
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'2': fear |
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'3': joy |
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'4': sadness |
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'5': surprise |
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'6': neutral |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 7095238 |
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num_examples: 43410 |
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- name: validation |
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num_bytes: 885284 |
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num_examples: 5426 |
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- name: test |
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num_bytes: 882333 |
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num_examples: 5427 |
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download_size: 6057071 |
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dataset_size: 8862855 |
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configs: |
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- config_name: google_translator |
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data_files: |
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- split: train |
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path: google_translator/train-* |
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- split: validation |
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path: google_translator/validation-* |
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- split: test |
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path: google_translator/test-* |
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task_categories: |
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- text-classification |
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language: |
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- en |
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- lt |
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--- |
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Original dataset: [GoEmotions dataset](https://huggingface.co/datasets/google-research-datasets/go_emotions) |
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The dataset was machine translated to Lithuanian using free Google Translate API. |
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Tool used for translation: [deep-translator](https://pypi.org/project/deep-translator/) |
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Translation script: |
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```python |
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from datasets import load_dataset |
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from deep_translator import GoogleTranslator |
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from deep_translator.exceptions import TranslationNotFound |
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original_dataset = load_dataset("go_emotions", name="simplified") |
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translator = GoogleTranslator(source="en", target="lt") |
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def translate_batch(batch): |
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original_text = batch["text"] |
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while True: |
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try: |
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translated_batch = translator.translate_batch(original_text) |
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break |
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except TranslationNotFound: |
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# Translation can fail due to API limits, so we retry until it works |
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print(f"Translation failed. Retrying...") |
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# We fix untranslated entries (None values) by replacing them with the original text |
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for i in range(len(translated_batch)): |
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if not translated_batch[i]: |
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translated_batch[i] = original_text[i] |
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print(f"Replaced {original_text[i]} vs {translated_batch[i]}") |
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batch["lt_text"] = translated_batch |
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return batch |
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translated_dataset = original_dataset.map( |
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translate_batch, batched=True, batch_size=500 |
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) |
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``` |
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Column `labels` contains multi-label emotion annotations with 28 emotion labels as per GoEmotion dataset: |
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```yaml |
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0: admiration |
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1: amusement |
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2: anger |
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3: annoyance |
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4: approval |
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5: caring |
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6: confusion |
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7: curiosity |
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8: desire |
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9: disappointment |
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10: disapproval |
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11: disgust |
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12: embarrassment |
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13: excitement |
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14: fear |
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15: gratitude |
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16: grief |
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17: joy |
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18: love |
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19: nervousness |
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20: optimism |
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21: pride |
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22: realization |
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23: relief |
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24: remorse |
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25: sadness |
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26: surprise |
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27: neutral |
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``` |
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Column `labels_ekman` contains multi-label emotion annotations with 7 base emotions as per Dr. Ekman theory: |
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```yaml |
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0: anger |
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1: disgust |
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2: fear |
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3: joy |
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4: sadness |
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5: surprise |
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6: neutral |
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``` |
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Label mapping from 28 emotions from GoEmotion to 7 base emotions as per Dr. Ekman theory: |
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|GoEmotion|Ekman| |
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|---|---| |
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| admiration | joy| |
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| amusement | joy| |
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| anger | anger| |
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| annoyance | anger| |
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| approval | joy| |
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| caring | joy| |
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| confusion | surprise| |
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| curiosity | surprise| |
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| desire | joy| |
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| disappointment | sadness| |
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| disapproval | anger| |
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| disgust | disgust| |
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| embarrassment | sadness| |
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| excitement | joy| |
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| fear | fear| |
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| gratitude | joy| |
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| grief | sadness| |
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| joy | joy| |
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| love | joy| |
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| nervousness | fear| |
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| optimism | joy| |
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| pride | joy| |
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| realization | surprise| |
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| relief | joy| |
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| remorse | sadness| |
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| sadness | sadness| |
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| surprise | surprise| |
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| neutral | neutral| |
