Add dataset card

README.md

@@ -1,4 +1,10 @@
 ---
+language:
+- cmn
+multilinguality: monolingual
+task_categories:
+- text-classification
+task_ids: []
 dataset_info:
   features:
   - name: text
@@ -26,4 +32,875 @@ configs:
     path: data/train-*
   - split: validation
     path: data/validation-*
+tags:
+- mteb
+- text
 ---
+<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
+
+<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
+  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">IFlyTek</h1>
+  <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
+  <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
+</div>
+
+Long Text classification for the description of Apps
+
+|               |                                             |
+|---------------|---------------------------------------------|
+| Task category | t2c                              |
+| Domains       | None                               |
+| Reference     | https://www.cluebenchmarks.com/introduce.html |
+
+
+## How to evaluate on this task
+
+You can evaluate an embedding model on this dataset using the following code:
+
+```python
+import mteb
+
+task = mteb.get_tasks(["IFlyTek"])
+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). 
+
+## Citation
+
+If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
+
+```bibtex
+
+@inproceedings{xu-etal-2020-clue,
+  abstract = {The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages. To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark. CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total). We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU. Our benchmark is released at https://www.cluebenchmarks.com},
+  address = {Barcelona, Spain (Online)},
+  author = {Xu, Liang  and
+Hu, Hai and
+Zhang, Xuanwei and
+Li, Lu and
+Cao, Chenjie and
+Li, Yudong and
+Xu, Yechen and
+Sun, Kai and
+Yu, Dian and
+Yu, Cong and
+Tian, Yin and
+Dong, Qianqian and
+Liu, Weitang and
+Shi, Bo and
+Cui, Yiming and
+Li, Junyi and
+Zeng, Jun and
+Wang, Rongzhao and
+Xie, Weijian and
+Li, Yanting and
+Patterson, Yina and
+Tian, Zuoyu and
+Zhang, Yiwen and
+Zhou, He and
+Liu, Shaoweihua and
+Zhao, Zhe and
+Zhao, Qipeng and
+Yue, Cong and
+Zhang, Xinrui and
+Yang, Zhengliang and
+Richardson, Kyle and
+Lan, Zhenzhong },
+  booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
+  doi = {10.18653/v1/2020.coling-main.419},
+  month = dec,
+  pages = {4762--4772},
+  publisher = {International Committee on Computational Linguistics},
+  title = {{CLUE}: A {C}hinese Language Understanding Evaluation Benchmark},
+  url = {https://aclanthology.org/2020.coling-main.419},
+  year = {2020},
+}
+
+
+@article{enevoldsen2025mmtebmassivemultilingualtext,
+  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
+  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
+  publisher = {arXiv},
+  journal={arXiv preprint arXiv:2502.13595},
+  year={2025},
+  url={https://arxiv.org/abs/2502.13595},
+  doi = {10.48550/arXiv.2502.13595},
+}
+
+@article{muennighoff2022mteb,
+  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},
+  year = {2022}
+  url = {https://arxiv.org/abs/2210.07316},
+  doi = {10.48550/ARXIV.2210.07316},
+}
+```
+
+# Dataset Statistics
+<details>
+  <summary> Dataset Statistics</summary>
+
+The following code contains the descriptive statistics from the task. These can also be obtained using:
+
+```python
+import mteb
+
+task = mteb.get_task("IFlyTek")
+
+desc_stats = task.metadata.descriptive_stats
+```
+
+```json
+{
+    "validation": {
+        "num_samples": 2599,
+        "number_of_characters": 753272,
+        "number_texts_intersect_with_train": 270,
+        "min_text_length": 11,
+        "average_text_length": 289.8314736437091,
+        "max_text_length": 1755,
+        "unique_text": 2549,
+        "unique_labels": 119,
+        "labels": {
+            "110": {
+                "count": 3
+            },
+            "70": {
+                "count": 388
+            },
+            "10": {
+                "count": 22
+            },
+            "18": {
+                "count": 79
+            },
+            "17": {
+                "count": 192
+            },
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+            },
+            "71": {
+                "count": 123
+            },
+            "104": {
+                "count": 4
+            },
+            "49": {
+                "count": 38
+            },
+            "20": {
+                "count": 53
+            },
+            "44": {
+                "count": 7
+            },
+            "24": {
+                "count": 27
+            },
+            "95": {
+                "count": 79
+            },
+            "21": {
+                "count": 56
+            },
+            "66": {
+                "count": 2
+            },
+            "83": {
+                "count": 7
+            },
+            "94": {
+                "count": 25
+            },
+            "19": {
+                "count": 36
+            },
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+                "count": 52
+            },
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+                "count": 51
+            },
+            "113": {
+                "count": 32
+            },
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+                "count": 54
+            },
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+                "count": 6
+            },
+            "106": {
+                "count": 68
+            },
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+            },
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+            },
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+        }
+    },
+    "train": {
+        "num_samples": 12133,
+        "number_of_characters": 3506882,
+        "number_texts_intersect_with_train": null,
+        "min_text_length": 10,
+        "average_text_length": 289.0366768317811,
+        "max_text_length": 4282,
+        "unique_text": 11425,
+        "unique_labels": 119,
+        "labels": {
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+            },
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+            "74": {
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+                "count": 3
+            },
+            "69": {
+                "count": 3
+            },
+            "68": {
+                "count": 1
+            }
+        }
+    }
+}
+```
+
+</details>
+
+---
+*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*