Update README.md

README.md

@@ -1,7 +1,19 @@
 ---
 license: apache-2.0
+language:
+- en
+- de
+- ru
+- zh
+tags:
+- mt-evaluation
+- WMT
+size_categories:
+- 100K<n<1M
 ---
 
+# Dataset Summary
+
 This dataset contains all MQM human annotations from previous [WMT Metrics shared tasks](https://wmt-metrics-task.github.io/) and the MQM annotations from [Experts, Errors, and Context](https://aclanthology.org/2021.tacl-1.87/).
 
 The data is organised into 8 columns:
@@ -13,6 +25,34 @@ The data is organised into 8 columns:
 - system: MT Engine that produced the translation
 - 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://github.com/google/wmt-mqm-human-evaluation). We recommend using the original repo if you are interested in annotation spans and not just the final score.
 
+
+## Python usage:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("RicardoRei/wmt-mqm-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"] == "ted")
+```
+
+## Citation Information
+
+If you use this data please cite the following works:
+- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://aclanthology.org/2021.tacl-1.87/)
+- [Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain](https://aclanthology.org/2021.wmt-1.73/)
+- [Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust](https://aclanthology.org/2022.wmt-1.2/)