Add task category, library name and link to paper

README.md

@@ -1,5 +1,8 @@
 ---
 license: apache-2.0
+task_categories:
+- image-classification
+library_name: datasets
 configs:
 - config_name: default
   data_files:
@@ -9,48 +12,6 @@ configs:
     path: test-*.tar
   - split: unlabeled
     path: unlabeled-*.tar
-# tags:
-# - vision
-# - image_classification
-# - stl10
-# - deduplicated
-# pretty_name: "STL-10 Cleaned (Deduplicated Training Set)"
-# dataset_infos:
-#  - config_name: default
-#    features:
-#      - name: image
-#        dtype: image
-#      - name: label
-#        dtype:
-#          class_label:
-#            names:
-#              '0': airplane
-#              '1': bird
-#              '2': car
-#              '3': cat
-#              '4': deer
-#              '5': dog
-#              '6': horse
-#              '7': monkey
-#              '8': ship
-#              '9': truck
-#      - name: file_name
-#        dtype: string
-#    splits:
-#      train:
-#        name: train
-#        num_bytes: 88987357
-#        num_examples: 5000
-#      test:
-#        name: test
-#        num_bytes: 142097288
-#        num_examples: 8000 # Original STL-10 test set size
-#      unlabeled:
-#        name: unlabeled
-#        num_bytes: 1642318927
-#        num_examples: 92455
-#    download_size: 1888369532
-#    dataset_size: 1873403572
 ---
 
 # Dataset Card for STL-10 Cleaned (Deduplicated Training Set)
@@ -230,3 +191,5 @@ This cleaned version of the STL-10 dataset was prepared by [https://github.com/S
 
 This modified dataset (including the cleaned training set, original test set prepared in this format, and associated metadata files) is released under the **Apache License 2.0**.
 The original STL-10 images are sourced from ImageNet. While STL-10 is widely used for research, be mindful of the original image sources if your use case extends beyond typical research applications.
+
+This dataset was used in the paper [ViTSGMM: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels](https://huggingface.co/papers/2506.03582).