Update README.md

README.md

@@ -36,7 +36,7 @@ tags:
 
 # 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/).
+This dataset contains all DA human annotations from previous WMT News Translation shared tasks.
 
 The data is organised into 8 columns:
 - lp: language pair
@@ -49,14 +49,13 @@ The data is organised into 8 columns:
 - 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.
-
+You can also find the original data for each year in the results section https://www.statmt.org/wmt{YEAR}/results.html e.g: for 2020 data: [https://www.statmt.org/wmt20/results.html](https://www.statmt.org/wmt20/results.html)
 
 ## Python usage:
 
 ```python
 from datasets import load_dataset
-dataset = load_dataset("RicardoRei/wmt-mqm-human-evaluation", split="train")
+dataset = load_dataset("RicardoRei/wmt-da-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. :
@@ -69,5 +68,7 @@ data = dataset.filter(lambda example: example["year"] == 2022)
 data = dataset.filter(lambda example: example["lp"] == "en-de")
 
 # split by domain
-data = dataset.filter(lambda example: example["domain"] == "ted")
-```
+data = dataset.filter(lambda example: example["domain"] == "news")
+```
+
+Note that most data is from News domain.