Add dataset card

README.md

@@ -6,7 +6,7 @@ language:
 license: unknown
 multilinguality: monolingual
 source_datasets:
-- PNLPhub/DigiMag
+- mteb/DigikalamagClassification
 task_categories:
 - text-classification
 task_ids: []
@@ -58,7 +58,7 @@ A total of 8,515 articles scraped from Digikala Online Magazine. This dataset in
 | Reference     | https://hooshvare.github.io/docs/datasets/tc |
 
 Source datasets:
-- [PNLPhub/DigiMag](https://huggingface.co/datasets/PNLPhub/DigiMag)
+- [mteb/DigikalamagClassification](https://huggingface.co/datasets/mteb/DigikalamagClassification)
 
 
 ## How to evaluate on this task
@@ -68,15 +68,15 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_tasks(["DigikalamagClassification"])
-evaluator = mteb.MTEB(task)
+task = mteb.get_task("DigikalamagClassification")
+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
 
@@ -96,7 +96,7 @@ 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},
@@ -121,146 +121,7 @@ desc_stats = task.metadata.descriptive_stats
 ```
 
 ```json
-{
-    "test": {
-        "num_samples": 852,
-        "number_of_characters": 2935825,
-        "number_texts_intersect_with_train": 0,
-        "min_text_length": 40,
-        "average_text_length": 3445.8039906103286,
-        "max_text_length": 36911,
-        "unique_texts": 852,
-        "min_labels_per_text": 5,
-        "average_label_per_text": 12.762910798122066,
-        "max_labels_per_text": 14,
-        "unique_labels": 18,
-        "labels": {
-            "\u0633": {
-                "count": 328
-            },
-            "\u0644": {
-                "count": 715
-            },
-            "\u0627": {
-                "count": 787
-            },
-            "\u0645": {
-                "count": 642
-            },
-            "\u062a": {
-                "count": 488
-            },
-            " ": {
-                "count": 1470
-            },
-            "\u0648": {
-                "count": 1590
-            },
-            "\u0632": {
-                "count": 358
-            },
-            "\u06cc": {
-                "count": 1975
-            },
-            "\u0628": {
-                "count": 408
-            },
-            "\u0639": {
-                "count": 289
-            },
-            "\u06a9": {
-                "count": 302
-            },
-            "\u0646": {
-                "count": 624
-            },
-            "\u0698": {
-                "count": 277
-            },
-            "\u0647": {
-                "count": 180
-            },
-            "\u0631": {
-                "count": 193
-            },
-            "\u062f": {
-                "count": 235
-            },
-            "\u062e": {
-                "count": 13
-            }
-        }
-    },
-    "train": {
-        "num_samples": 6896,
-        "number_of_characters": 23218475,
-        "number_texts_intersect_with_train": null,
-        "min_text_length": 114,
-        "average_text_length": 3366.948230858469,
-        "max_text_length": 53321,
-        "unique_texts": 6896,
-        "min_labels_per_text": 5,
-        "average_label_per_text": 12.764936194895592,
-        "max_labels_per_text": 14,
-        "unique_labels": 18,
-        "labels": {
-            "\u0639": {
-                "count": 2342
-            },
-            "\u0644": {
-                "count": 5794
-            },
-            "\u0645": {
-                "count": 5194
-            },
-            " ": {
-                "count": 11904
-            },
-            "\u0648": {
-                "count": 12878
-            },
-            "\u062a": {
-                "count": 3961
-            },
-            "\u06a9": {
-                "count": 2451
-            },
-            "\u0646": {
-                "count": 5046
-            },
-            "\u0698": {
-                "count": 2245
-            },
-            "\u06cc": {
-                "count": 15977
-            },
-            "\u0647": {
-                "count": 1451
-            },
-            "\u0631": {
-                "count": 1552
-            },
-            "\u0633": {
-                "count": 2654
-            },
-            "\u0627": {
-                "count": 6371
-            },
-            "\u0628": {
-                "count": 3309
-            },
-            "\u0632": {
-                "count": 2897
-            },
-            "\u062f": {
-                "count": 1900
-            },
-            "\u062e": {
-                "count": 101
-            }
-        }
-    }
-}
+{}
 ```
 
 </details>