Add dataset card

README.md

@@ -5,6 +5,8 @@ language:
 - dan
 license: cc-by-4.0
 multilinguality: monolingual
+source_datasets:
+- strombergnlp/bornholmsk_parallel
 task_categories:
 - translation
 task_ids: []
@@ -55,6 +57,9 @@ Danish Bornholmsk Parallel Corpus. Bornholmsk is a Danish dialect spoken on the
 | Domains       | Web, Social, Fiction, Written                               |
 | Reference     | https://aclanthology.org/W19-6138/ |
 
+Source datasets:
+- [strombergnlp/bornholmsk_parallel](https://huggingface.co/datasets/strombergnlp/bornholmsk_parallel)
+
 
 ## How to evaluate on this task
 
@@ -63,15 +68,15 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_tasks(["BornholmBitextMining"])
-evaluator = mteb.MTEB(task)
+task = mteb.get_task("BornholmBitextMining")
+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 repitory](https://github.com/embeddings-benchmark/mteb). 
+To learn more about how to run models on `mteb` task check out the [GitHub repository](https://github.com/embeddings-benchmark/mteb).
 
 ## Citation
 
@@ -104,7 +109,7 @@ 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, Lo{\"\i}c and Reimers, Nils},
+  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
   title = {MTEB: Massive Text Embedding Benchmark},
   publisher = {arXiv},
   journal={arXiv preprint arXiv:2210.07316},
@@ -129,21 +134,7 @@ desc_stats = task.metadata.descriptive_stats
 ```
 
 ```json
-{
-    "test": {
-        "num_samples": 500,
-        "number_of_characters": 44361,
-        "unique_pairs": 500,
-        "min_sentence1_length": 1,
-        "average_sentence1_length": 49.834,
-        "max_sentence1_length": 555,
-        "unique_sentence1": 497,
-        "min_sentence2_length": 5,
-        "average_sentence2_length": 38.888,
-        "max_sentence2_length": 453,
-        "unique_sentence2": 491
-    }
-}
+{}
 ```
 
 </details>