Initial data import

README.md

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 ---
-license: apache-2.0
+language:
+- en
+- lt
+license: mit
+task_categories:
+- text-classification
+dataset_info:
+  config_name: simplified_ekman
+  features:
+  - name: lt_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: 71995184.8176143
+    num_examples: 333447
+  - name: validation
+    num_bytes: 8999425.091192849
+    num_examples: 41681
+  - name: test
+    num_bytes: 8999425.091192849
+    num_examples: 41681
+  download_size: 55584192
+  dataset_size: 89994035.0
+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 Lithuanian 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="lt")
+
+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["lt_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)
+```

simplified_ekman/test-00000-of-00001.parquet

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simplified_ekman/train-00000-of-00001.parquet

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simplified_ekman/validation-00000-of-00001.parquet

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