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