Add dataset card

README.md

@@ -5,6 +5,8 @@ language:
 - dan
 license: cc-by-4.0
 multilinguality: monolingual
+source_datasets:
+- sorenmulli/da-hashtag-twitterhjerne
 task_categories:
 - text-retrieval
 task_ids:
@@ -84,6 +86,8 @@ Danish question asked on Twitter with the Hashtag #Twitterhjerne ('Twitter brain
 | Reference     | https://huggingface.co/datasets/sorenmulli/da-hashtag-twitterhjerne |
 
 
+
+
 ## How to evaluate on this task
 
 You can evaluate an embedding model on this dataset using the following code:
@@ -91,15 +95,15 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_task("TwitterHjerneRetrieval")
-evaluator = mteb.MTEB([task])
+task = mteb.get_tasks(["TwitterHjerneRetrieval"])
+evaluator = mteb.MTEB(task)
 
 model = mteb.get_model(YOUR_MODEL)
 evaluator.run(model)
 ```
 
 <!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
-To learn more about how to run models on `mteb` task check out the [GitHub repository](https://github.com/embeddings-benchmark/mteb).
+To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
 
 ## Citation
 
@@ -125,7 +129,7 @@ If you use this dataset, please cite the dataset as well as [mteb](https://githu
 }
 
 @article{muennighoff2022mteb,
-  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
+  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
   title = {MTEB: Massive Text Embedding Benchmark},
   publisher = {arXiv},
   journal={arXiv preprint arXiv:2210.07316},