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README.md

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----
-annotations_creators: 
-- expert-generated 
-language_creators: 
-- other
-language: 
-- pl
-license: 
-- cc-by-sa-4.0
-multilinguality: 
-- monolingual
-pretty_name: 'PolEmo2.0-IN'
-size_categories: 
-- 1K<n<10K
-source_datasets: 
-- original
-task_categories: 
-- text-classification
-task_ids: 
-- sentiment-classification
----
-
-# klej-polemo2-in
-
-## Description
-
-The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. It comprises over 8000 reviews, about 85% from the medicine and hotel domains.
-
-We use the PolEmo2.0 dataset to form two tasks. Both use the same training dataset, i.e., reviews from medicine and hotel domains, but are evaluated on a different test set. 
-
-**In-Domain** is the first task, and we use accuracy to evaluate model performance within the in-domain context, i.e., on a test set of reviews from medicine and hotels domains. 
-
-## Tasks (input, output, and metrics)
-
-The task is to predict the correct label of the review.
-
-**Input** ('*text'* column): sentence
-
-**Output** ('*target'* column): label for sentence sentiment ('zero': neutral, 'minus': negative, 'plus': positive, 'amb': ambiguous)
-
-**Domain**: Online reviews
-
-**Measurements**: Accuracy
-
-**Example**: 
-
-Input: `Lekarz zalecił mi kurację alternatywną do dotychczasowej , więc jeszcze nie daję najwyższej oceny ( zobaczymy na ile okaże się skuteczna ) . Do Pana doktora nie mam zastrzeżeń : bardzo profesjonalny i kulturalny . Jedyny minus dotyczy gabinetu , który nie jest nowoczesny , co może zniechęcać pacjentki .`
-
-Input (translated by DeepL): `The doctor recommended me an alternative treatment to the current one , so I do not yet give the highest rating ( we will see how effective it turns out to be ) . To the doctor I have no reservations : very professional and cultured . The only minus is about the office , which is not modern , which may discourage patients .`
-
-Output: `amb` (ambiguous)
-
-## Data splits
-
-| Subset     |   Cardinality |
-|:-----------|--------------:|
-| train      |          5783 |
-| test       |           722 |
-| validation |           723 |
-
-## Class distribution in train
-
-| Class | Sentiment | train | validation |  test |
-|:------|:----------|------:|-----------:|------:|
-| minus | positive  | 0.379 |      0.375 | 0.416 |
-| plus  | negative  | 0.271 |      0.289 | 0.273 |
-| amb   | ambiguous | 0.182 |      0.160 | 0.150 |
-| zero  | neutral   | 0.168 |      0.176 | 0.162 |
-
-## Citation
-
-```
-@inproceedings{kocon-etal-2019-multi,
-    title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
-    author = "Koco{\'n}, Jan  and
-      Mi{\l}kowski, Piotr  and
-      Za{\'s}ko-Zieli{\'n}ska, Monika",
-    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
-    month = nov,
-    year = "2019",
-    address = "Hong Kong, China",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/K19-1092",
-    doi = "10.18653/v1/K19-1092",
-    pages = "980--991",
-    abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).",
-}
-```
-
-## License
-
-```
-Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
-```
-
-## Links
-
-[HuggingFace](https://huggingface.co/datasets/allegro/klej-polemo2-in)
-
-[Source](https://clarin-pl.eu/dspace/handle/11321/710)
-
-[Paper](https://aclanthology.org/K19-1092/)
-
-## Examples
-
-### Loading
-
-```python
-from pprint import pprint
-
-from datasets import load_dataset
-
-dataset = load_dataset("allegro/klej-polemo2-in")
-pprint(dataset['train'][0])
-
-# {'sentence': 'Super lekarz i człowiek przez duże C . Bardzo duże doświadczenie '
-#              'i trafne diagnozy . Wielka cierpliwość do ludzi starszych . Od '
-#              'lat opiekuje się moją Mamą staruszką , i twierdzę , że mamy duże '
-#              'szczęście , że mamy takiego lekarza . Naprawdę nie wiem cobyśmy '
-#              'zrobili , gdyby nie Pan doktor . Dzięki temu , moja mama żyje . '
-#              'Każda wizyta u specjalisty jest u niego konsultowana i uważam , '
-#              'że jest lepszy od każdego z nich . Mamy do Niego prawie '
-#              'nieograniczone zaufanie . Można wiele dobrego o Panu doktorze '
-#              'jeszcze napisać . Niestety , ma bardzo dużo pacjentów , jest '
-#              'przepracowany ( z tego powodu nawet obawiam się o jego zdrowie ) '
-#              'i dostęp do niego jest trudny , ale zawsze możliwy .',
-#  'target': '__label__meta_plus_m'}
-```
-
-### Evaluation
-
-```python
-import random
-from pprint import pprint
-
-from datasets import load_dataset, load_metric
-
-dataset = load_dataset("allegro/klej-polemo2-in")
-dataset = dataset.class_encode_column("target")
-references = dataset["test"]["target"]
-
-# generate random predictions
-predictions = [random.randrange(max(references) + 1) for _ in range(len(references))]
-
-acc = load_metric("accuracy")
-f1 = load_metric("f1")
-
-acc_score = acc.compute(predictions=predictions, references=references)
-f1_score = f1.compute(predictions=predictions, references=references, average="macro")
-
-pprint(acc_score)
-pprint(f1_score)
-
-# {'accuracy': 0.25069252077562326}
-# {'f1': 0.23760962219870274}
-```

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