add README.md

README.md

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+---
+annotations_creators: 
+- expert-generated 
+language_creators: 
+- other
+languages: 
+- pl
+licenses: 
+- cc-by-sa-4.0
+multilinguality: 
+- monolingual
+pretty_name: 'Polemo2'
+size_categories: 
+- 8K
+- 1K<n<10K
+source_datasets: 
+- original
+task_categories: 
+- text-classification
+task_ids: 
+- sentiment-classification
+---
+
+# Polemo2
+
+## 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. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in the 2+1 scheme, which gives a total of 197,046 annotations. About 85% of the reviews are from the medicine and hotel domains. Each review is annotated with four labels: positive, negative, neutral, or ambiguous.
+
+## 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, F1 Macro
+
+**Example**: 
+*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 .* → **1 (negative)**
+
+## Data splits
+
+| Subset | Cardinality |
+|--------|------------:|
+| train  |        6573 |
+| val    |         823 |
+| test   |         820 |
+
+## Class distribution in train
+
+| Class | Fraction |
+|-------|---------:|
+| zero  | 0.147726 |
+| minus | 0.375628 |
+| plus  | 0.277499 |
+| amb   | 0.199148 |
+
+## 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/clarin-pl/polemo2-official)
+
+[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("clarin-pl/polemo2-official")
+pprint(dataset['train'][0])
+
+# {'target': 1,
+#  'text': '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 .'}
+```
+
+### Evaluation
+
+```python
+import random
+from pprint import pprint
+
+from datasets import load_dataset, load_metric
+
+dataset = load_dataset("clarin-pl/polemo2-official")
+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': 0.2475609756097561}
+# {'f1': 0.23747048177471738}
+```