Add dataset card

README.md

@@ -2,6 +2,8 @@
 language:
 - cmn
 multilinguality: monolingual
+source_datasets:
+- C-MTEB/ATEC
 task_categories:
 - sentence-similarity
 task_ids:
@@ -56,6 +58,8 @@ A Chinese dataset for textual relatedness
 | Reference     | https://aclanthology.org/2021.emnlp-main.357 |
 
 
+
+
 ## How to evaluate on this task
 
 You can evaluate an embedding model on this dataset using the following code:
@@ -63,15 +67,15 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_tasks(["ATEC"])
-evaluator = mteb.MTEB(task)
+task = mteb.get_task("ATEC")
+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
 
@@ -112,7 +116,7 @@ Yih, Scott Wen-tau},
 }
 
 @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},
@@ -137,40 +141,7 @@ desc_stats = task.metadata.descriptive_stats
 ```
 
 ```json
-{
-    "validation": {
-        "num_samples": 20000,
-        "number_of_characters": 536573,
-        "unique_pairs": 20000,
-        "min_sentence1_length": 5,
-        "average_sentence1_len": 13.4172,
-        "max_sentence1_length": 84,
-        "unique_sentence1": 19909,
-        "min_sentence2_length": 5,
-        "average_sentence2_len": 13.41145,
-        "max_sentence2_length": 82,
-        "unique_sentence2": 19882,
-        "min_score": 0,
-        "avg_score": 0.1844,
-        "max_score": 1
-    },
-    "test": {
-        "num_samples": 20000,
-        "number_of_characters": 536531,
-        "unique_pairs": 20000,
-        "min_sentence1_length": 5,
-        "average_sentence1_len": 13.40835,
-        "max_sentence1_length": 97,
-        "unique_sentence1": 19911,
-        "min_sentence2_length": 5,
-        "average_sentence2_len": 13.4182,
-        "max_sentence2_length": 88,
-        "unique_sentence2": 19907,
-        "min_score": 0,
-        "avg_score": 0.1805,
-        "max_score": 1
-    }
-}
+{}
 ```
 
 </details>