Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-classification
Languages:
Polish
Size:
100K - 1M
License:
Update README.md
Browse files
README.md
CHANGED
@@ -40,7 +40,12 @@ The task is to predict the correct label of the review.
|
|
40 |
**Measurements**: Accuracy, F1 Macro
|
41 |
|
42 |
**Example**:
|
43 |
-
|
|
|
|
|
|
|
|
|
|
|
44 |
|
45 |
## Data splits
|
46 |
|
|
|
40 |
**Measurements**: Accuracy, F1 Macro
|
41 |
|
42 |
**Example**:
|
43 |
+
|
44 |
+
Input: `Na samym wejściu hotel śmierdzi . W pokojach jest pleśń na ścianach , brudny dywan . W łazience śmierdzi chemią , hotel nie grzeje w pokojach panuje chłód . Wyposażenie pokoju jest stare , kran się rusza , drzwi na balkon nie domykają się . Jedzenie jest w małych ilościach i nie smaczne . Nie polecam nikomu tego hotelu .`
|
45 |
+
|
46 |
+
Input (translated by DeepL): `At the very entrance the hotel stinks . In the rooms there is mold on the walls , dirty carpet . The bathroom smells of chemicals , the hotel does not heat in the rooms are cold . The room furnishings are old , the faucet moves , the door to the balcony does not close . The food is in small quantities and not tasty . I would not recommend this hotel to anyone .`
|
47 |
+
|
48 |
+
Output: `1` (negative)
|
49 |
|
50 |
## Data splits
|
51 |
|