Add dataset card

README.md

@@ -7,10 +7,14 @@ license: other
 multilinguality: translated
 source_datasets:
 - mteb/msmarco
+- mteb/MSMARCO-PL
 task_categories:
 - text-retrieval
+- multiple-choice-qa
+- question-answering
 task_ids:
 - multiple-choice-qa
+- question-answering
 dataset_info:
 - config_name: corpus
   features:
@@ -103,6 +107,10 @@ MS MARCO is a collection of datasets focused on deep learning in search
 | Domains       | Web, Written                               |
 | Reference     | https://microsoft.github.io/msmarco/ |
 
+Source datasets:
+- [mteb/msmarco](https://huggingface.co/datasets/mteb/msmarco)
+- [mteb/MSMARCO-PL](https://huggingface.co/datasets/mteb/MSMARCO-PL)
+
 
 ## How to evaluate on this task
 
@@ -111,15 +119,15 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_tasks(["MSMARCO-PL"])
-evaluator = mteb.MTEB(task)
+task = mteb.get_task("MSMARCO-PL")
+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
 
@@ -148,7 +156,7 @@ 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},