Update README.md

README.md

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 ---
 license: apache-2.0
+size_categories:
+- 1M<n<10M
+language:
+ - cs 
+ - de 
+ - en 
+ - hr 
+ - ja 
+ - liv 
+ - ru 
+ - sah 
+ - uk 
+ - zh
+tags:
+- mt-evaluation
+- WMT
+- 12-lang-pairs
 ---
+
+# Dataset Summary
+
+In 2022, several changes were made to the annotation procedure used in the WMT Translation task. In contrast to the standard DA (sliding scale from 0-100) used in previous years, in 2022 annotators performed DA+SQM (Direct Assessment + Scalar Quality Metric). In DA+SQM, the annotators still provide a raw score between 0 and 100, but also are presented with seven labeled tick marks. DA+SQM helps to stabilize scores across annotators (as compared to DA).
+
+The data is organised into 8 columns:
+- lp: language pair
+- src: input text
+- mt: translation
+- ref: reference translation
+- score: direct assessment
+- system: MT engine that produced the `mt`
+- annotators: number of annotators
+- domain: domain of the input text (e.g. news)
+- year: collection year
+  
+You can also find the original data [here](https://www.statmt.org/wmt22/results.html)
+
+## Python usage:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("RicardoRei/wmt-sqm-human-evaluation", split="train")
+```
+
+There is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :
+
+```python
+# split by year
+data = dataset.filter(lambda example: example["year"] == 2022)
+
+# split by LP
+data = dataset.filter(lambda example: example["lp"] == "en-de")
+
+# split by domain
+data = dataset.filter(lambda example: example["domain"] == "news")
+```
+
+Note that, so far, all data is from [2022 General Translation task](https://www.statmt.org/wmt22/translation-task.html)
+
+## Citation Information
+
+If you use this data please cite the WMT findings:
+- [Findings of the 2022 Conference on Machine Translation (WMT22)](https://aclanthology.org/2022.wmt-1.1.pdf)
+