Create README.md

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-OUT'
+size_categories: 
+- 1K<n<10K
+source_datasets: 
+- original
+task_categories: 
+- text-classification
+task_ids: 
+- sentiment-classification
+---
+
+# klej-polemo2-out
+
+## 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. 
+
+**Out-of-Domain** is the second task, and we test the model on out-of-domain reviews, i.e., from product and university domains. Since the original test sets for those domains are scarce (50 reviews each), we decided to use the original out-of-domain training set of 900 reviews for testing purposes and create a new split of development and test sets. As a result, the task consists of 1000 reviews, comparable in size to the in-domain test dataset of 1400 reviews.
+
+## 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***: 
+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 .* → *__label__meta_amb*
+
+## Data splits
+
+| Subset     |   Cardinality |
+|:-----------|--------------:|
+| train      |          5783 |
+| test       |           722 |
+| validation |           723 |
+
+## Class distribution
+
+| Class | Sentiment | train | validation |  test |
+|:------|:----------|------:|-----------:|------:|
+| minus | positive  | 0.379 |      0.334 | 0.368 |
+| plus  | negative  | 0.271 |      0.332 | 0.302 |
+| amb   | ambiguous | 0.182 |      0.332 | 0.328 |
+| zero  | neutral   | 0.168 |      0.002 | 0.002 |
+
+## 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-out)
+
+[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-out")
+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 napisa�� . 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-out")
+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': 0.2894736842105263}
+# {'f1': 0.2484406098784191}
+```