Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- en
+- de
+- ru
+- zh
+tags:
+- mt-evaluation
+- WMT
+- MQM
+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/) in a form of error spans.
+
+The data is organised into 8 columns:
+
+- src: input text
+- mt: translation
+- ref: reference translation
+- annotations: List of error spans (dictionaries with 'start', 'end', 'severity', 'text')
+- lp: language pair
+  
+**Note that this is not an official release of the data** and the original data can be found [here](https://github.com/google/wmt-mqm-human-evaluation).
+ 
+Also, while `en-ru` was annotated by Unbabel, `en-de` and `zh-en` was annotated by Google. This means that for en-de and zh-en you will only find minor and major errors while for en-ru you can find a few critical errors.
+
+## Python usage:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("RicardoRei/wmt-mqm-error-spans", 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 LP
+data = dataset.filter(lambda example: example["lp"] == "en-de")
+```
+
+## 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/)