Skip to content

Instantly share code, notes, and snippets.

@player1537

player1537/experiment-with-using-embeddings-to-find-tvcg-papers.ipynb Secret

Created September 20, 2023 19:04
Show Gist options
  • Save player1537/c5970698349ec635c361e92321f2ca1c to your computer and use it in GitHub Desktop.
Save player1537/c5970698349ec635c361e92321f2ca1c to your computer and use it in GitHub Desktop.
Download ZIP
Experiment with Using Embeddings to Find TVCG Papers.ipynb
Display the source blob
Display the rendered blob
Raw
experiment-with-using-embeddings-to-find-tvcg-papers.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4",
"authorship_tag": "ABX9TyNIUrp9MU1V9DCx/o6MBuGu",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU",
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"3813a0c2126b4fb99edc2b0751116269": {
"model_module": "@jupyter-widgets/output",
"model_name": "OutputModel",
"model_module_version": "1.0.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/output",
"_model_module_version": "1.0.0",
"_model_name": "OutputModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/output",
"_view_module_version": "1.0.0",
"_view_name": "OutputView",
"layout": "IPY_MODEL_23ae52e044d14be6bd59e82a2a64fc3e",
"msg_id": "",
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": " \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;249;38;114m╸\u001b[0m\u001b[38;5;237m━\u001b[0m \u001b[32m1.9/2.0 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
"text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"> <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span> <span style=\"color: #008000; text-decoration-color: #008000\">1.9/2.0 MB</span> <span style=\"color: #800000; text-decoration-color: #800000\">8.4 MB/s</span> eta <span style=\"color: #008080; text-decoration-color: #008080\">0:00:01</span>\n</pre>\n"
},
"metadata": {}
}
]
}
},
"23ae52e044d14be6bd59e82a2a64fc3e": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"ed6488ee7c794c76831765caba9c903a": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_58edb3a1e2934b12a5c4b3fec605d8bb",
"IPY_MODEL_432f2efc3eb640d08fd2518047109d77",
"IPY_MODEL_83a3f3171b144013b3fcce6c15d8c01a"
],
"layout": "IPY_MODEL_ea879fd2863f4ba793a08401e43fe899"
}
},
"58edb3a1e2934b12a5c4b3fec605d8bb": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_5529f0d87a0a4dcaa18705450ceae33a",
"placeholder": "​",
"style": "IPY_MODEL_b5f8760019324929b2ec9112bb2ea388",
"value": "Downloading (…)okenizer_config.json: 100%"
}
},
"432f2efc3eb640d08fd2518047109d77": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_7aa34be6f7134121a37f370f173d5215",
"max": 366,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_5090b417a63c44e3905a41c5a621e5b1",
"value": 366
}
},
"83a3f3171b144013b3fcce6c15d8c01a": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_27f395ed041748e7a82239621cd592ae",
"placeholder": "​",
"style": "IPY_MODEL_a861e67d9e7f426f8524e9441b29ea65",
"value": " 366/366 [00:00&lt;00:00, 17.2kB/s]"
}
},
"ea879fd2863f4ba793a08401e43fe899": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"5529f0d87a0a4dcaa18705450ceae33a": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"b5f8760019324929b2ec9112bb2ea388": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"7aa34be6f7134121a37f370f173d5215": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"5090b417a63c44e3905a41c5a621e5b1": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"27f395ed041748e7a82239621cd592ae": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"a861e67d9e7f426f8524e9441b29ea65": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"aa14078d9bfa4286a81a87b3dc0c07dd": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_061421ffb2144dd9b7808591c9d3c0a2",
"IPY_MODEL_d78fed41af7442e2a4e7c65bd3c2255c",
"IPY_MODEL_33d8d7510a854bf0a6982b08a39c9694"
],
"layout": "IPY_MODEL_062c208b25f74840b8d6e73eb3a3a6dd"
}
},
"061421ffb2144dd9b7808591c9d3c0a2": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_321ffe3f96d246afa11bede552b8d01d",
"placeholder": "​",
"style": "IPY_MODEL_c1004976ed63445ab9cf35b4917d2019",
"value": "Downloading (…)solve/main/vocab.txt: 100%"
}
},
"d78fed41af7442e2a4e7c65bd3c2255c": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_03703453a84a4e35bc9c0a5a6ce9facb",
"max": 231508,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_d46d137be0424f088dc143f41ac0c858",
"value": 231508
}
},
"33d8d7510a854bf0a6982b08a39c9694": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_f152b78804284a3b9e32f3b190792c5c",
"placeholder": "​",
"style": "IPY_MODEL_51989641b5e24a4f853fa4c8aac0a487",
"value": " 232k/232k [00:00&lt;00:00, 6.04MB/s]"
}
},
"062c208b25f74840b8d6e73eb3a3a6dd": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"321ffe3f96d246afa11bede552b8d01d": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"c1004976ed63445ab9cf35b4917d2019": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"03703453a84a4e35bc9c0a5a6ce9facb": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"d46d137be0424f088dc143f41ac0c858": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"f152b78804284a3b9e32f3b190792c5c": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"51989641b5e24a4f853fa4c8aac0a487": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"3d3f4eac1dcc4565aa4e17a2c83867a0": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_37a35c2dc60f43ffa75b1a186134eeeb",
"IPY_MODEL_6e5f9ab1855a4d439112bee01c677a14",
"IPY_MODEL_542254ea336b4f10a06b5132152abe0f"
],
"layout": "IPY_MODEL_a8b8026e1b344c889cd145cd4fd1002b"
}
},
"37a35c2dc60f43ffa75b1a186134eeeb": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_37128c544991433b8a0efa787a07a415",
"placeholder": "​",
"style": "IPY_MODEL_79f0627e399d457bb9e0324413f609dc",
"value": "Downloading (…)/main/tokenizer.json: 100%"
}
},
"6e5f9ab1855a4d439112bee01c677a14": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_8d62b059567f481ebee190c0eae953b6",
"max": 711396,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_347f507bf63d41eca7ddbb140ae77b4f",
"value": 711396
}
},
"542254ea336b4f10a06b5132152abe0f": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_847b1927f8774fe785da11238c5029f7",
"placeholder": "​",
"style": "IPY_MODEL_a96dce0da6d84e3991b8f815762323e6",
"value": " 711k/711k [00:00&lt;00:00, 32.3MB/s]"
}
},
"a8b8026e1b344c889cd145cd4fd1002b": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"37128c544991433b8a0efa787a07a415": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"79f0627e399d457bb9e0324413f609dc": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"8d62b059567f481ebee190c0eae953b6": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"347f507bf63d41eca7ddbb140ae77b4f": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"847b1927f8774fe785da11238c5029f7": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"a96dce0da6d84e3991b8f815762323e6": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"2756c4e9c1974205aa829b66d58e4a19": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_b0a889297d3b49b895985f97225871d9",
"IPY_MODEL_b2298ce334c04a859fdeaf1c5a71cb85",
"IPY_MODEL_12b56e4387c649c4a53c660c5e2ea8f2"
],
"layout": "IPY_MODEL_daf4382df096456bab5352c9670a01e7"
}
},
"b0a889297d3b49b895985f97225871d9": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_021e8ea3af8645ffb2b2df30f3e85763",
"placeholder": "​",
"style": "IPY_MODEL_3e8c1bbd26574097bd8643a5b76fbfa6",
"value": "Downloading (…)cial_tokens_map.json: 100%"
}
},
"b2298ce334c04a859fdeaf1c5a71cb85": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_f4617a42072c4be0a6fd6a57db7a493b",
"max": 125,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_e28223962c5b4a0cbcea80107a1c25ad",
"value": 125
}
},
"12b56e4387c649c4a53c660c5e2ea8f2": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_07f012629e9c4ef4b1252ac41f916749",
"placeholder": "​",
"style": "IPY_MODEL_77ead34bb7664e0a894a8cafc530be87",
"value": " 125/125 [00:00&lt;00:00, 8.16kB/s]"
}
},
"daf4382df096456bab5352c9670a01e7": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"021e8ea3af8645ffb2b2df30f3e85763": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"3e8c1bbd26574097bd8643a5b76fbfa6": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"f4617a42072c4be0a6fd6a57db7a493b": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"e28223962c5b4a0cbcea80107a1c25ad": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"07f012629e9c4ef4b1252ac41f916749": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"77ead34bb7664e0a894a8cafc530be87": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"384a767f3c8b4e7c90e47efb767142da": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_fd2be40473b6412e924aff0b038a8ebf",
"IPY_MODEL_4fc491869bbc44afbff85d2be27a5b08",
"IPY_MODEL_c7f5106a4970425fab02ce6c06ba2a7c"
],
"layout": "IPY_MODEL_d0b98da0a7e44250a10a7f7dc93395f7"
}
},
"fd2be40473b6412e924aff0b038a8ebf": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_ddeb1ce0f47a4656911361422c7f4135",
"placeholder": "​",
"style": "IPY_MODEL_bf3358a3bf464827bfd393dcf406762b",
"value": "Downloading (…)lve/main/config.json: 100%"
}
},
"4fc491869bbc44afbff85d2be27a5b08": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_063ec492ac97496a9452a0be181eab75",
"max": 684,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_51089b61309d47308f1298419ecc3daa",
"value": 684
}
},
"c7f5106a4970425fab02ce6c06ba2a7c": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_de26356269a6461d8146d45ff0509993",
"placeholder": "​",
"style": "IPY_MODEL_3b6c0f648e6f4869ac505c684ab55731",
"value": " 684/684 [00:00&lt;00:00, 15.5kB/s]"
}
},
"d0b98da0a7e44250a10a7f7dc93395f7": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"ddeb1ce0f47a4656911361422c7f4135": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"bf3358a3bf464827bfd393dcf406762b": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"063ec492ac97496a9452a0be181eab75": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"51089b61309d47308f1298419ecc3daa": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"de26356269a6461d8146d45ff0509993": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"3b6c0f648e6f4869ac505c684ab55731": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"1c52308f001247f48296730e81198530": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_84f12af4d3db416587c40c79069cefdc",
"IPY_MODEL_0e06a44d0b2240468f3394c8c5731f9b",
"IPY_MODEL_209eee7b74be42bf9675f91219e3d59f"
],
"layout": "IPY_MODEL_0e4ce251f1c2400fb6383108a34eb5ed"
}
},
"84f12af4d3db416587c40c79069cefdc": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_82554417aa26426bb8f8c2bddbf4b8d0",
"placeholder": "​",
"style": "IPY_MODEL_9737d50fbf204424950233e9865aa066",
"value": "Downloading model.safetensors: 100%"
}
},
"0e06a44d0b2240468f3394c8c5731f9b": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_b4b0adc75e9246bba5ac1b7c9178af4f",
"max": 133466304,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_d9ae90dcfc014af99d757a83682fd8d6",
"value": 133466304
}
},
"209eee7b74be42bf9675f91219e3d59f": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_6f7c7bd8cdba4ccb9ee37a1bb007c625",
"placeholder": "​",
"style": "IPY_MODEL_462fe15ca4f94cf0875e5588927726f1",
"value": " 133M/133M [00:00&lt;00:00, 181MB/s]"
}
},
"0e4ce251f1c2400fb6383108a34eb5ed": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"82554417aa26426bb8f8c2bddbf4b8d0": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"9737d50fbf204424950233e9865aa066": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"b4b0adc75e9246bba5ac1b7c9178af4f": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"d9ae90dcfc014af99d757a83682fd8d6": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"6f7c7bd8cdba4ccb9ee37a1bb007c625": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"462fe15ca4f94cf0875e5588927726f1": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
}
}
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/player1537/c5970698349ec635c361e92321f2ca1c/experiment-with-using-embeddings-to-find-tvcg-papers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "l33aiSEUgIpt",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4c7b1b81-8052-42e1-e099-64f1aaf79600"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting mediocreatbest@ git+https://gist.github.com/player1537/3457b026ed6ef6696d758517f55a58df.git\n",
" Cloning https://gist.github.com/player1537/3457b026ed6ef6696d758517f55a58df.git to /tmp/pip-install-xn1tw8k5/mediocreatbest_cd52e3637fde4ee1b4cde6000739ad9c\n",
" Running command git clone --filter=blob:none --quiet https://gist.github.com/player1537/3457b026ed6ef6696d758517f55a58df.git /tmp/pip-install-xn1tw8k5/mediocreatbest_cd52e3637fde4ee1b4cde6000739ad9c\n",
" Resolved https://gist.github.com/player1537/3457b026ed6ef6696d758517f55a58df.git to commit dc68403b6505fbe515b83692138a62b4bfa43c0d\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"Building wheels for collected packages: mediocreatbest\n",
" Building wheel for mediocreatbest (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for mediocreatbest: filename=mediocreatbest-0.2.11-py3-none-any.whl size=7675 sha256=4d5dfe415b4364f235f2475acd2fd237c6ab90cd735c742592208fb73c5d0eec\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-cgofwt2b/wheels/cc/6d/dd/d4137c3485df0cb8773c136ecc231252590bf217127892aaa5\n",
"Successfully built mediocreatbest\n",
"Installing collected packages: mediocreatbest\n",
"Successfully installed mediocreatbest-0.2.11\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"from __future__ import annotations\n",
"try:\n",
" from mediocreatbest import auto, run\n",
"except ImportError:\n",
" %pip install --quiet --upgrade pip\n",
" %pip install --upgrade --force-reinstall \\\n",
" mediocreatbest@git+https://gist.github.com/player1537/3457b026ed6ef6696d758517f55a58df.git\n",
" from mediocreatbest import auto, run\n"
]
},
{
"cell_type": "code",
"source": [
"df = auto.pd.read_csv('/content/export2023.09.19-13.32.16.csv')\n",
"print(df.columns)\n",
"df.head()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 898
},
"id": "sMi6DawRgP2Z",
"outputId": "36647adc-046c-4fb7-dcbf-81f05b6d06c0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Index(['Document Title', 'Authors', 'Author Affiliations', 'Publication Title',\n",
" 'Date Added To Xplore', 'Publication Year', 'Volume', 'Issue',\n",
" 'Start Page', 'End Page', 'Abstract', 'ISSN', 'ISBNs', 'DOI',\n",
" 'Funding Information', 'PDF Link', 'Author Keywords', 'IEEE Terms',\n",
" 'INSPEC Controlled Terms', 'INSPEC Non-Controlled Terms', 'Mesh_Terms',\n",
" 'Article Citation Count', 'Patent Citation Count', 'Reference Count',\n",
" 'License', 'Online Date', 'Issue Date', 'Meeting Date', 'Publisher',\n",
" 'Document Identifier'],\n",
" dtype='object')\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Document Title \\\n",
"0 AdaVis: Adaptive and Explainable Visualization... \n",
"1 Image Inpainting via Correlated Multi-resoluti... \n",
"2 A Topological Distance Between Multi-Fields Ba... \n",
"3 Reinforced Labels: Multi-Agent Deep Reinforcem... \n",
"4 LoCoMoTe – a Framework for Classification of N... \n",
"\n",
" Authors \\\n",
"0 S. Zhang; Y. Wang; H. Li; H. Qu \n",
"1 S. S. Phutke; S. Murala \n",
"2 Y. Ramamurthi; A. Chattopadhyay \n",
"3 P. Bobák; L. Čmolík; M. Čadík \n",
"4 C. Croucher; W. Powell; B. Stevens; M. Miller-... \n",
"\n",
" Author Affiliations \\\n",
"0 School of Computing and Information System, Si... \n",
"1 Computer Vision and Pattern Recognition Labora... \n",
"2 International Institute of Information Technol... \n",
"3 Faculty of Information Technology, Brno Univer... \n",
"4 Department of Cognitive Science and Artificial... \n",
"\n",
" Publication Title Date Added To Xplore \\\n",
"0 IEEE Transactions on Visualization and Compute... NaN \n",
"1 IEEE Transactions on Visualization and Compute... NaN \n",
"2 IEEE Transactions on Visualization and Compute... NaN \n",
"3 IEEE Transactions on Visualization and Compute... NaN \n",
"4 IEEE Transactions on Visualization and Compute... NaN \n",
"\n",
" Publication Year Volume Issue Start Page End Page ... Mesh_Terms \\\n",
"0 2023 PP 99 1 14 ... NaN \n",
"1 2023 PP 99 1 12 ... NaN \n",
"2 2023 PP 99 1 14 ... NaN \n",
"3 2023 PP 99 1 14 ... NaN \n",
"4 2023 PP 99 1 19 ... NaN \n",
"\n",
" Article Citation Count Patent Citation Count Reference Count License \\\n",
"0 NaN NaN NaN IEEE \n",
"1 NaN NaN NaN IEEE \n",
"2 NaN NaN NaN IEEE \n",
"3 NaN NaN NaN IEEE \n",
"4 NaN NaN NaN CCBY \n",
"\n",
" Online Date Issue Date Meeting Date Publisher Document Identifier \n",
"0 18 Sep 2023 NaN NaN IEEE IEEE Early Access Articles \n",
"1 13 Sep 2023 NaN NaN IEEE IEEE Early Access Articles \n",
"2 13 Sep 2023 NaN NaN IEEE IEEE Early Access Articles \n",
"3 11 Sep 2023 NaN NaN IEEE IEEE Early Access Articles \n",
"4 11 Sep 2023 NaN NaN IEEE IEEE Early Access Articles \n",
"\n",
"[5 rows x 30 columns]"
],
"text/html": [
"\n",
" <div id=\"df-ba9c653e-43a0-44fd-8a66-24773df9b5dc\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Document Title</th>\n",
" <th>Authors</th>\n",
" <th>Author Affiliations</th>\n",
" <th>Publication Title</th>\n",
" <th>Date Added To Xplore</th>\n",
" <th>Publication Year</th>\n",
" <th>Volume</th>\n",
" <th>Issue</th>\n",
" <th>Start Page</th>\n",
" <th>End Page</th>\n",
" <th>...</th>\n",
" <th>Mesh_Terms</th>\n",
" <th>Article Citation Count</th>\n",
" <th>Patent Citation Count</th>\n",
" <th>Reference Count</th>\n",
" <th>License</th>\n",
" <th>Online Date</th>\n",
" <th>Issue Date</th>\n",
" <th>Meeting Date</th>\n",
" <th>Publisher</th>\n",
" <th>Document Identifier</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AdaVis: Adaptive and Explainable Visualization...</td>\n",
" <td>S. Zhang; Y. Wang; H. Li; H. Qu</td>\n",
" <td>School of Computing and Information System, Si...</td>\n",
" <td>IEEE Transactions on Visualization and Compute...</td>\n",
" <td>NaN</td>\n",
" <td>2023</td>\n",
" <td>PP</td>\n",
" <td>99</td>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>IEEE</td>\n",
" <td>18 Sep 2023</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>IEEE</td>\n",
" <td>IEEE Early Access Articles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Image Inpainting via Correlated Multi-resoluti...</td>\n",
" <td>S. S. Phutke; S. Murala</td>\n",
" <td>Computer Vision and Pattern Recognition Labora...</td>\n",
" <td>IEEE Transactions on Visualization and Compute...</td>\n",
" <td>NaN</td>\n",
" <td>2023</td>\n",
" <td>PP</td>\n",
" <td>99</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>IEEE</td>\n",
" <td>13 Sep 2023</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>IEEE</td>\n",
" <td>IEEE Early Access Articles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A Topological Distance Between Multi-Fields Ba...</td>\n",
" <td>Y. Ramamurthi; A. Chattopadhyay</td>\n",
" <td>International Institute of Information Technol...</td>\n",
" <td>IEEE Transactions on Visualization and Compute...</td>\n",
" <td>NaN</td>\n",
" <td>2023</td>\n",
" <td>PP</td>\n",
" <td>99</td>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>IEEE</td>\n",
" <td>13 Sep 2023</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>IEEE</td>\n",
" <td>IEEE Early Access Articles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Reinforced Labels: Multi-Agent Deep Reinforcem...</td>\n",
" <td>P. Bobák; L. Čmolík; M. Čadík</td>\n",
" <td>Faculty of Information Technology, Brno Univer...</td>\n",
" <td>IEEE Transactions on Visualization and Compute...</td>\n",
" <td>NaN</td>\n",
" <td>2023</td>\n",
" <td>PP</td>\n",
" <td>99</td>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>IEEE</td>\n",
" <td>11 Sep 2023</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>IEEE</td>\n",
" <td>IEEE Early Access Articles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LoCoMoTe – a Framework for Classification of N...</td>\n",
" <td>C. Croucher; W. Powell; B. Stevens; M. Miller-...</td>\n",
" <td>Department of Cognitive Science and Artificial...</td>\n",
" <td>IEEE Transactions on Visualization and Compute...</td>\n",
" <td>NaN</td>\n",
" <td>2023</td>\n",
" <td>PP</td>\n",
" <td>99</td>\n",
" <td>1</td>\n",
" <td>19</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CCBY</td>\n",
" <td>11 Sep 2023</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>IEEE</td>\n",
" <td>IEEE Early Access Articles</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 30 columns</p>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba9c653e-43a0-44fd-8a66-24773df9b5dc')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-ba9c653e-43a0-44fd-8a66-24773df9b5dc button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-ba9c653e-43a0-44fd-8a66-24773df9b5dc');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-e91ba3f9-9802-4a61-98d0-b96db471fbaa\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e91ba3f9-9802-4a61-98d0-b96db471fbaa')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-e91ba3f9-9802-4a61-98d0-b96db471fbaa button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"source": [
"@auto.dataclasses.dataclass\n",
"class Paper:\n",
" index: int\n",
" title: str\n",
" authors: str\n",
" abstract: str\n",
" keywords: str\n",
" url: str\n"
],
"metadata": {
"id": "L4bjGPKOoitb"
},
"execution_count": 59,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def Fill(s: any, /) -> str:\n",
" return auto.textwrap.fill(str(s), width=72)\n"
],
"metadata": {
"id": "qgmSH-K6ooC_"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%%scope -i df -o papers\n",
"papers = []\n",
"for i, (_, row) in enumerate(df.iterrows()):\n",
" index = i\n",
" title = row['Document Title']\n",
" authors = row['Authors']\n",
" abstract = row['Abstract']\n",
" keywords = ';'.join([\n",
" str(row['Author Keywords']),\n",
" str(row['IEEE Terms']),\n",
" # row['INSPEC Controlled Terms'],\n",
" # row['INSPEC Non-Controlled Terms'],\n",
" ])\n",
" url = row['PDF Link']\n",
"\n",
" paper = Paper(\n",
" index=index,\n",
" title=title,\n",
" authors=authors,\n",
" abstract=abstract,\n",
" keywords=keywords,\n",
" url=url,\n",
" )\n",
" papers.append(paper)\n",
"\n",
"print(Fill( repr(papers[0]) ))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WobPhVvgnGrq",
"outputId": "5b4096c7-4fbb-4c03-bced-badf3cdf9818"
},
"execution_count": 60,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Paper(index=0, title='AdaVis: Adaptive and Explainable Visualization\n",
"Recommendation for Tabular Data', authors='S. Zhang; Y. Wang; H. Li; H.\n",
"Qu', abstract='Automated visualization recommendation facilitates the\n",
"rapid creation of effective visualizations, which is especially\n",
"beneficial for users with limited time and limited knowledge of data\n",
"visualization. There is an increasing trend in leveraging machine\n",
"learning (ML) techniques to achieve an end-to-end visualization\n",
"recommendation. However, existing ML-based approaches implicitly assume\n",
"that there is only one appropriate visualization for a specific dataset,\n",
"which is often not true for real applications. Also, they often work\n",
"like a black box, and are difficult for users to understand the reasons\n",
"for recommending specific visualizations. To fill the research gap, we\n",
"propose AdaVis, an adaptive and explainable approach to recommend one or\n",
"multiple appropriate visualizations for a tabular dataset. It leverages\n",
"a box embedding-based knowledge graph to well model the possible one-to-\n",
"many mapping relations among different entities (i.e., data features,\n",
"dataset columns, datasets, and visualization choices). The embeddings of\n",
"the entities and relations can be learned from dataset-visualization\n",
"pairs. Also, AdaVis incorporates the attention mechanism into the\n",
"inference framework. Attention can indicate the relative importance of\n",
"data features for a dataset and provide fine-grained explainability. Our\n",
"extensive evaluations through quantitative metric evaluations, case\n",
"studies, and user interviews demonstrate the effectiveness of AdaVis.',\n",
"keywords='Visualization Recommendation;Logical Reasoning;Data\n",
"Visualization;Knowledge Graph;Data visualization;Knowledge\n",
"graphs;Tail;Adaptation models;Visualization;Feature extraction;Magnetic\n",
"heads',\n",
"url='https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254497')\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%scope -o pos,neg\n",
"pos: list[Paper] = []\n",
"neg: list[Paper] = []\n",
"\n",
"for paper in papers:\n",
" if 'Explaining Neural Networks' in paper.title \\\n",
" or 'AdaVis' in paper.title \\\n",
" or 'AutoTitle' in paper.title \\\n",
" or 'Automatic Scatterplot' in paper.title \\\n",
" or 'How Does Automation' in paper.title \\\n",
" or 'Towards Visualization Thumbnail' in paper.title \\\n",
" or 'Personalized Language' in paper.title:\n",
" pos.append(paper)\n",
" else:\n",
" neg.append(paper)\n",
"\n",
"print(len(pos))\n",
"print(len(neg))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Z6s6CZJHgmIQ",
"outputId": "cc409084-d615-4e09-e88b-59fd64c68a1b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"5\n",
"273\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"encoding = auto.tiktoken.encoding_for_model('gpt-3.5-turbo')\n",
"encoding\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175,
"referenced_widgets": [
"3813a0c2126b4fb99edc2b0751116269",
"23ae52e044d14be6bd59e82a2a64fc3e"
]
},
"id": "TcJtaP2oh5q2",
"outputId": "70fc51ee-5a45-4cfe-9e99-e85681aa525b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.\n",
" warnings.warn(\"Setuptools is replacing distutils.\")\n",
"WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n",
"Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n",
"To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Output()"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3813a0c2126b4fb99edc2b0751116269"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"\n"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
"</pre>\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #808000; text-decoration-color: #808000\">WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n",
"</span></pre>\n"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Encoding 'cl100k_base'>"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"def Cost(x: str, /) -> int:\n",
" return len(encoding.encode(x))\n"
],
"metadata": {
"id": "tA5GC8UepCFK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def Text(x: Paper, /) -> str:\n",
" return f'{x.title}\\n{x.authors}\\n{x.abstract}\\n{x.keywords}'\n",
"\n",
"print(Fill( Text(pos[0]) ))\n",
"print(Cost( Text(pos[0]) ))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "viHlsUL1jCs_",
"outputId": "cb6bfd45-f45a-43fa-f8eb-b17216fb6820"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"AdaVis: Adaptive and Explainable Visualization Recommendation for\n",
"Tabular Data S. Zhang; Y. Wang; H. Li; H. Qu Automated visualization\n",
"recommendation facilitates the rapid creation of effective\n",
"visualizations, which is especially beneficial for users with limited\n",
"time and limited knowledge of data visualization. There is an increasing\n",
"trend in leveraging machine learning (ML) techniques to achieve an end-\n",
"to-end visualization recommendation. However, existing ML-based\n",
"approaches implicitly assume that there is only one appropriate\n",
"visualization for a specific dataset, which is often not true for real\n",
"applications. Also, they often work like a black box, and are difficult\n",
"for users to understand the reasons for recommending specific\n",
"visualizations. To fill the research gap, we propose AdaVis, an adaptive\n",
"and explainable approach to recommend one or multiple appropriate\n",
"visualizations for a tabular dataset. It leverages a box embedding-based\n",
"knowledge graph to well model the possible one-to-many mapping relations\n",
"among different entities (i.e., data features, dataset columns,\n",
"datasets, and visualization choices). The embeddings of the entities and\n",
"relations can be learned from dataset-visualization pairs. Also, AdaVis\n",
"incorporates the attention mechanism into the inference framework.\n",
"Attention can indicate the relative importance of data features for a\n",
"dataset and provide fine-grained explainability. Our extensive\n",
"evaluations through quantitative metric evaluations, case studies, and\n",
"user interviews demonstrate the effectiveness of AdaVis. Visualization\n",
"Recommendation;Logical Reasoning;Data Visualization;Knowledge Graph;Data\n",
"visualization;Knowledge graphs;Tail;Adaptation\n",
"models;Visualization;Feature extraction;Magnetic heads\n",
"308\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"device = auto.torch.device(\n",
" 'cuda'\n",
" if auto.torch.cuda.is_available() else\n",
" 'cpu'\n",
")\n",
"device\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QCGnAeecvMeU",
"outputId": "263cadc4-f8cd-4898-8623-8e4fd790cbee"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"device(type='cuda')"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"tokenizer = auto.transformers.AutoTokenizer.from_pretrained(\n",
" 'BAAI/bge-small-en',\n",
")\n"
],
"metadata": {
"id": "iaH9hNqRko0J",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
"referenced_widgets": [
"ed6488ee7c794c76831765caba9c903a",
"58edb3a1e2934b12a5c4b3fec605d8bb",
"432f2efc3eb640d08fd2518047109d77",
"83a3f3171b144013b3fcce6c15d8c01a",
"ea879fd2863f4ba793a08401e43fe899",
"5529f0d87a0a4dcaa18705450ceae33a",
"b5f8760019324929b2ec9112bb2ea388",
"7aa34be6f7134121a37f370f173d5215",
"5090b417a63c44e3905a41c5a621e5b1",
"27f395ed041748e7a82239621cd592ae",
"a861e67d9e7f426f8524e9441b29ea65",
"aa14078d9bfa4286a81a87b3dc0c07dd",
"061421ffb2144dd9b7808591c9d3c0a2",
"d78fed41af7442e2a4e7c65bd3c2255c",
"33d8d7510a854bf0a6982b08a39c9694",
"062c208b25f74840b8d6e73eb3a3a6dd",
"321ffe3f96d246afa11bede552b8d01d",
"c1004976ed63445ab9cf35b4917d2019",
"03703453a84a4e35bc9c0a5a6ce9facb",
"d46d137be0424f088dc143f41ac0c858",
"f152b78804284a3b9e32f3b190792c5c",
"51989641b5e24a4f853fa4c8aac0a487",
"3d3f4eac1dcc4565aa4e17a2c83867a0",
"37a35c2dc60f43ffa75b1a186134eeeb",
"6e5f9ab1855a4d439112bee01c677a14",
"542254ea336b4f10a06b5132152abe0f",
"a8b8026e1b344c889cd145cd4fd1002b",
"37128c544991433b8a0efa787a07a415",
"79f0627e399d457bb9e0324413f609dc",
"8d62b059567f481ebee190c0eae953b6",
"347f507bf63d41eca7ddbb140ae77b4f",
"847b1927f8774fe785da11238c5029f7",
"a96dce0da6d84e3991b8f815762323e6",
"2756c4e9c1974205aa829b66d58e4a19",
"b0a889297d3b49b895985f97225871d9",
"b2298ce334c04a859fdeaf1c5a71cb85",
"12b56e4387c649c4a53c660c5e2ea8f2",
"daf4382df096456bab5352c9670a01e7",
"021e8ea3af8645ffb2b2df30f3e85763",
"3e8c1bbd26574097bd8643a5b76fbfa6",
"f4617a42072c4be0a6fd6a57db7a493b",
"e28223962c5b4a0cbcea80107a1c25ad",
"07f012629e9c4ef4b1252ac41f916749",
"77ead34bb7664e0a894a8cafc530be87"
]
},
"outputId": "253aec1a-2800-44fd-d155-a5585cde2423"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)okenizer_config.json: 0%| | 0.00/366 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ed6488ee7c794c76831765caba9c903a"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)solve/main/vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "aa14078d9bfa4286a81a87b3dc0c07dd"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)/main/tokenizer.json: 0%| | 0.00/711k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3d3f4eac1dcc4565aa4e17a2c83867a0"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)cial_tokens_map.json: 0%| | 0.00/125 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "2756c4e9c1974205aa829b66d58e4a19"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"model = auto.transformers.AutoModel.from_pretrained(\n",
" 'BAAI/bge-small-en',\n",
").to(device)\n"
],
"metadata": {
"id": "nfn07rnFltHg",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"384a767f3c8b4e7c90e47efb767142da",
"fd2be40473b6412e924aff0b038a8ebf",
"4fc491869bbc44afbff85d2be27a5b08",
"c7f5106a4970425fab02ce6c06ba2a7c",
"d0b98da0a7e44250a10a7f7dc93395f7",
"ddeb1ce0f47a4656911361422c7f4135",
"bf3358a3bf464827bfd393dcf406762b",
"063ec492ac97496a9452a0be181eab75",
"51089b61309d47308f1298419ecc3daa",
"de26356269a6461d8146d45ff0509993",
"3b6c0f648e6f4869ac505c684ab55731",
"1c52308f001247f48296730e81198530",
"84f12af4d3db416587c40c79069cefdc",
"0e06a44d0b2240468f3394c8c5731f9b",
"209eee7b74be42bf9675f91219e3d59f",
"0e4ce251f1c2400fb6383108a34eb5ed",
"82554417aa26426bb8f8c2bddbf4b8d0",
"9737d50fbf204424950233e9865aa066",
"b4b0adc75e9246bba5ac1b7c9178af4f",
"d9ae90dcfc014af99d757a83682fd8d6",
"6f7c7bd8cdba4ccb9ee37a1bb007c625",
"462fe15ca4f94cf0875e5588927726f1"
]
},
"outputId": "d64f21c3-5830-4276-cabb-93692d84535c"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/684 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "384a767f3c8b4e7c90e47efb767142da"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading model.safetensors: 0%| | 0.00/133M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "1c52308f001247f48296730e81198530"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"%%scope -o Embed\n",
"def Embed(arg: str | list[str], /, **kwargs) -> auto.torch.Tensor:\n",
" inp = tokenizer(arg, return_tensors='pt', **kwargs)\n",
" inp = inp.to(device)\n",
" out = model(**inp)\n",
" out = out.pooler_output\n",
" out = out.cpu()\n",
" out = out.detach().numpy()\n",
" if isinstance(arg, str):\n",
" out = out[0, :]\n",
"\n",
" return out\n",
"\n",
"%time print( Embed(Text(pos[0])).shape )\n",
"%time Embed(Text(pos[0])).shape\n",
"%time Embed(Text(pos[0])).shape\n",
"\n",
"faker = auto.faker.Faker()\n",
"\n",
"%time Embed(faker.name())\n",
"print(Embed([\n",
" faker.name(),\n",
" faker.name(),\n",
"], padding=True).shape)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dHL2lHSgluJb",
"outputId": "4b979f15-df4b-4a6f-b1a4-6eaf9ebd1362"
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(384,)\n",
"CPU times: user 25.2 ms, sys: 0 ns, total: 25.2 ms\n",
"Wall time: 25.4 ms\n",
"CPU times: user 21 ms, sys: 0 ns, total: 21 ms\n",
"Wall time: 21.1 ms\n",
"CPU times: user 20.2 ms, sys: 0 ns, total: 20.2 ms\n",
"Wall time: 20.3 ms\n",
"CPU times: user 18.2 ms, sys: 1 µs, total: 18.2 ms\n",
"Wall time: 19.2 ms\n",
"(2, 384)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"Chunk = auto.collections.namedtuple('Chunk', 'offset length text')\n",
"\n",
"def Chunks(text: str, /, size: int=64) -> list[Chunk]:\n",
" text: bytes = text.encode('ascii', errors='ignore')\n",
"\n",
" chunks = []\n",
" auto.register('fastcdc', import_names=[\n",
" 'fastcdc', 'fastcdc.fastcdc_py',\n",
" ])\n",
"\n",
" for chunk in auto.fastcdc.fastcdc_py.chunk_generator(\n",
" stream=auto.io.BytesIO(text),\n",
" min_size=size//2,\n",
" avg_size=size,\n",
" max_size=size*2,\n",
" fat=True,\n",
" hf=None,\n",
" ):\n",
" chunks.append(Chunk(\n",
" offset=chunk.offset,\n",
" length=chunk.length,\n",
" text=chunk.data.decode('ascii', errors='ignore')\n",
" ))\n",
"\n",
" return chunks\n",
"\n",
"Chunks(Text(pos[0]))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sH7p7dtFphFd",
"outputId": "8a1cf038-bc62-4918-cd17-cb3f9e6c8cf6"
},
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[Chunk(offset=0, length=128, text='AdaVis: Adaptive and Explainable Visualization Recommendation for Tabular Data\\nS. Zhang; Y. Wang; H. Li; H. Qu\\nAutomated visuali'),\n",
" Chunk(offset=128, length=34, text='zation recommendation facilitates '),\n",
" Chunk(offset=162, length=38, text='the rapid creation of effective visual'),\n",
" Chunk(offset=200, length=52, text='izations, which is especially beneficial for users w'),\n",
" Chunk(offset=252, length=51, text='ith limited time and limited knowledge of data visu'),\n",
" Chunk(offset=303, length=48, text='alization. There is an increasing trend in lever'),\n",
" Chunk(offset=351, length=107, text='aging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, exis'),\n",
" Chunk(offset=458, length=50, text='ting ML-based approaches implicitly assume that th'),\n",
" Chunk(offset=508, length=103, text='ere is only one appropriate visualization for a specific dataset, which is often not true for real appl'),\n",
" Chunk(offset=611, length=73, text='ications. Also, they often work like a black box, and are difficult for u'),\n",
" Chunk(offset=684, length=128, text='sers to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptiv'),\n",
" Chunk(offset=812, length=85, text='e and explainable approach to recommend one or multiple appropriate visualizations fo'),\n",
" Chunk(offset=897, length=34, text='r a tabular dataset. It leverages '),\n",
" Chunk(offset=931, length=51, text='a box embedding-based knowledge graph to well model'),\n",
" Chunk(offset=982, length=49, text=' the possible one-to-many mapping relations among'),\n",
" Chunk(offset=1031, length=60, text=' different entities (i.e., data features, dataset columns, d'),\n",
" Chunk(offset=1091, length=84, text='atasets, and visualization choices). The embeddings of the entities and relations ca'),\n",
" Chunk(offset=1175, length=60, text='n be learned from dataset-visualization pairs. Also, AdaVis '),\n",
" Chunk(offset=1235, length=62, text='incorporates the attention mechanism into the inference framew'),\n",
" Chunk(offset=1297, length=44, text='ork. Attention can indicate the relative imp'),\n",
" Chunk(offset=1341, length=43, text='ortance of data features for a dataset and '),\n",
" Chunk(offset=1384, length=42, text='provide fine-grained explainability. Our e'),\n",
" Chunk(offset=1426, length=52, text='xtensive evaluations through quantitative metric eva'),\n",
" Chunk(offset=1478, length=44, text='luations, case studies, and user interviews '),\n",
" Chunk(offset=1522, length=82, text='demonstrate the effectiveness of AdaVis.\\nVisualization Recommendation;Logical Reas'),\n",
" Chunk(offset=1604, length=53, text='oning;Data Visualization;Knowledge Graph;Data visuali'),\n",
" Chunk(offset=1657, length=37, text='zation;Knowledge graphs;Tail;Adaptati'),\n",
" Chunk(offset=1694, length=35, text='on models;Visualization;Feature ext'),\n",
" Chunk(offset=1729, length=32, text='raction;Magnetic heads')]"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"source": [
"%%scope -o X,Y,W,I,T,POS,NEG\n",
"\n",
"# texts, paper indices, inputs, outputs, weights\n",
"T: list[str] = []\n",
"I: list[auto.np.ndarray] = []\n",
"X: list[auto.np.ndarray] = []\n",
"Y: list[auto.np.ndarray] = []\n",
"W: list[auto.np.ndarray] = []\n",
"\n",
"def Add(papers: list[Paper], label: int, /) -> None:\n",
" def Add(x: Paper, y: int, /, *, weight: float, size: int) -> None:\n",
" for chunk in Chunks(Text(x), size=size):\n",
" I.append(auto.np.array([x.index]))\n",
" T.append(chunk.text)\n",
" Y.append(auto.np.array([y]))\n",
" W.append(auto.np.array([weight]))\n",
"\n",
" for paper in papers:\n",
" Add(paper, label, weight=0.25, size=64)\n",
" Add(paper, label, weight=0.50, size=128)\n",
" Add(paper, label, weight=0.75, size=256)\n",
" Add(paper, label, weight=1.00, size=512)\n",
"\n",
"Add(pos, (POS := 0))\n",
"Add(neg, (NEG := 1))\n",
"\n",
"for t in auto.tqdm.tqdm(auto.more_itertools.chunked(T, 64), total=(len(T)+63)//64):\n",
" X.append( Embed(t, padding=True) )\n",
"\n",
"# Flatten\n",
"I = auto.np.concatenate(I)\n",
"X = auto.np.concatenate(X)\n",
"Y = auto.np.concatenate(Y)\n",
"W = auto.np.concatenate(W)\n",
"\n",
"print(I.shape)\n",
"print(len(T))\n",
"print(X.shape)\n",
"print(Y.shape)\n",
"print(W.shape)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yhHYi6gHtREF",
"outputId": "8477bb9f-cdc7-44f1-8f7b-5a06f685b075"
},
"execution_count": 65,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(14580,)\n",
"14580\n",
"(14580,)\n",
"(14580,)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%scope -o classifier\n",
"classifier = auto.sklearn.neighbors.KNeighborsClassifier(\n",
" n_neighbors=5,\n",
")\n",
"\n",
"classifier.fit(X, Y)\n",
"print(classifier.predict_proba(X[[0], :]))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5guAUXK53PPn",
"outputId": "951cc76c-d8bc-4a33-ef33-bbd7394acbe0"
},
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[1. 0.]]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%scope -i X -o XY\n",
"\n",
"reduce = auto.sklearn.decomposition.PCA(\n",
" n_components=10,\n",
" random_state=1337,\n",
")\n",
"%time reduce.fit(X[:1024, ...])\n",
"%time X = reduce.transform(X)\n",
"\n",
"embed = (\n",
" # auto.sklearn.manifold.TSNE(\n",
" # n_components=2,\n",
" # init='pca',\n",
" # random_state=1337,\n",
" # )\n",
" auto.sklearn.decomposition.PCA(\n",
" n_components=2,\n",
" random_state=1337,\n",
" )\n",
")\n",
"\n",
"%time XY = embed.fit_transform(X)\n",
"print(XY.shape)\n"
],
"metadata": {
"id": "k8hKRXo6qWjP",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1746b42c-ea79-4343-b319-54606c9c0ad3"
},
"execution_count": 40,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CPU times: user 16.3 ms, sys: 949 µs, total: 17.3 ms\n",
"Wall time: 20.3 ms\n",
"CPU times: user 13.5 ms, sys: 0 ns, total: 13.5 ms\n",
"Wall time: 12.3 ms\n",
"CPU times: user 111 ms, sys: 996 µs, total: 112 ms\n",
"Wall time: 110 ms\n",
"(14580, 2)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%scope -o P\n",
"_, indices = classifier.kneighbors(X)\n",
"print(indices.shape)\n",
"print(indices.dtype)\n",
"\n",
"weights = W[indices]\n",
"labels = Y[indices]\n",
"\n",
"P = (weights * labels).sum(axis=1) / indices.shape[1]\n",
"# P is between 0 (POS) and 1 (NEG)\n",
"# I want P between -1 (NEG) and 1 (POS)\n",
"P = -2 * P + 1\n",
"print(P.shape)\n",
"\n",
"# PXY = classifier.predict_proba(X)\n",
"# print(PXY.shape)\n",
"\n",
"# P = PXY[:, POS] - PXY[:, NEG]\n",
"# print(P.shape)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_FAE2JUGyVbS",
"outputId": "1a10989f-b760-4d65-e551-c59e7fab45c1"
},
"execution_count": 55,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(14580, 5)\n",
"int64\n",
"(14580,)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%scope\n",
"# Histogram of values in P\n",
"\n",
"fig = auto.plt.figure()\n",
"\n",
"ax = fig.add_subplot(111)\n",
"ax.hist(P, bins=100)\n",
"ax.set_title('Histogram of values in P (classifier labels; -1 = NEG, 1 = POS)')\n",
"\n",
"fig.tight_layout()\n",
"fig.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 487
},
"id": "5GzvqPAF0Z7j",
"outputId": "995b289c-8ded-4049-84f6-b5040d904760"
},
"execution_count": 66,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"%%scope -i XY,P\n",
"I = P > 0.55\n",
"XY = XY[I, ...]\n",
"P = P[I, ...]\n",
"\n",
"fig = auto.plt.figure()\n",
"\n",
"ax = fig.add_subplot(111)\n",
"im = ax.scatter(XY[:, 0], XY[:, 1], c=P)\n",
"\n",
"# colorbar of the scatter plot\n",
"ax.figure.colorbar(im, ax=ax)\n",
"\n",
"ax.set_title('T-SNE plot of embeddings and classifier labels (-1 = NEG, 1 = POS)')\n",
"\n",
"fig.tight_layout()\n",
"fig.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 487
},
"id": "yw3V4NugyGRi",
"outputId": "ce43d15a-1dae-44bd-af60-f5b39f629ca0"
},
"execution_count": 67,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"%%scope -i I,P\n",
"#@title Ordered list of similar papers to sample set\n",
"mask = P > 0.55\n",
"I = I[mask, ...]\n",
"P = P[mask, ...]\n",
"\n",
"order = auto.np.argsort(P)\n",
"order = order[::-1]\n",
"I = I[order, ...]\n",
"P = P[order, ...]\n",
"\n",
"seen = set()\n",
"\n",
"for i, p in zip(I, P):\n",
" if i in seen: continue\n",
" else: seen.add(i)\n",
"\n",
" paper = papers[i]\n",
" print(paper.url + '\\n' + Fill( f'{i}: {p:.3f} {paper.title}') )\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6RjmI5Cj3UBx",
"outputId": "7b35d369-de43-4418-e14b-a22a980bf00c"
},
"execution_count": 68,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130316\n",
"76: 1.000 Towards Visualization Thumbnail Designs That Entice Reading\n",
"Data-Driven Articles\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081398\n",
"103: 1.000 How Does Automation Shape the Process of Narrative\n",
"Visualization: A Survey of Tools\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254497\n",
"0: 1.000 AdaVis: Adaptive and Explainable Visualization Recommendation\n",
"for Tabular Data\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10194961\n",
"25: 1.000 Personalized Language Model Selection through Gamified\n",
"Elicitation of Contrastive Concept Preferences\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10168257\n",
"45: 1.000 AutoTitle: An Interactive Title Generator for Visualizations\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9916137\n",
"230: 0.800 Revisiting the Design Patterns of Composite Visualizations\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10038574\n",
"143: 0.800 Scanpath Prediction on Information Visualisations\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9904859\n",
"238: 0.800 Sensemaking Sans Power: Interactive Data Visualization Using\n",
"Color-Changing Ink\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10246431\n",
"3: 0.800 Reinforced Labels: Multi-Agent Deep Reinforcement Learning for\n",
"Point-Feature Label Placement\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9956753\n",
"208: 0.700 EVNet: An Explainable Deep Network for Dimension Reduction\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10008084\n",
"169: 0.700 Tasks and Visualizations Used for Data Profiling: A Survey\n",
"and Interview Study\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10041726\n",
"142: 0.700 Visual Diagnostics of Parallel Performance in Training Large-\n",
"Scale DNN Models\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9984928\n",
"192: 0.700 V-Mail: 3D-Enabled Correspondence about Spatial Data on\n",
"(Almost) All Your Devices\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128738\n",
"82: 0.700 Audio2Gestures: Generating Diverse Gestures From Audio\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10018537\n",
"163: 0.700 Regenerating Arbitrary Video Sequences with Distillation\n",
"Path-Finding\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9552198\n",
"277: 0.700 LoopGrafter: Visual Support for the Grafting Workflow of\n",
"Protein Loops\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057010\n",
"126: 0.700 Magic Furniture: Design Paradigm of Multi-function Assembly\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9875213\n",
"253: 0.700 The Effects of Spatial Complexity on Narrative Experience in\n",
"Space-Adaptive AR Storytelling\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10179136\n",
"38: 0.700 A Collaborative, Interactive and Context-Aware Drawing Agent\n",
"for Co-Creative Design\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9839681\n",
"271: 0.700 SSRNet: Scalable 3D Surface Reconstruction Network\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10147241\n",
"62: 0.700 Optimally Ordered Orthogonal Neighbor Joining Trees for\n",
"Hierarchical Cluster Analysis\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10143702\n",
"67: 0.700 MediVizor: Visual Mediation Analysis of Nominal Variables\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10143227\n",
"69: 0.700 PanVA: Pangenomic Variant Analysis\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9887905\n",
"250: 0.700 ScrollyVis: Interactive Visual Authoring of Guided Dynamic\n",
"Narratives for Scientific Scrollytelling\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049688\n",
"131: 0.700 Accurate Registration of Cross-Modality Geometry via\n",
"Consistent Clustering\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10054065\n",
"129: 0.700 Attitudinal effects of data visualizations and illustrations\n",
"in data stories\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9947006\n",
"211: 0.600 Explore Contextual Information for 3D Scene Graph Generation\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9905473\n",
"237: 0.600 Electromechanical Coupling in Electroactive Polymers – a\n",
"Visual Analysis of a Third-Order Tensor Field\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9839572\n",
"270: 0.600 Evaluating Graphical Perception of Visual Motion for\n",
"Quantitative Data Encoding\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9965773\n",
"205: 0.600 3D Question Answering\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9968044\n",
"203: 0.600 Marching Windows: Scalable Mesh Generation for Volumetric\n",
"Data with Multiple Materials\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9978713\n",
"196: 0.600 Revisiting Walking-in-Place by Introducing Step-Height\n",
"Control, Elastic Input, and Pseudo-Haptic Feedback\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9942350\n",
"214: 0.600 A Survey of Smooth Vector Graphics: Recent Advances in\n",
"Representation, Creation, Rasterization and Image Vectorization\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9893375\n",
"247: 0.600 A Systematic Literature Review of Virtual Reality Locomotion\n",
"Taxonomies\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9895311\n",
"241: 0.600 DOMINO: Visual Causal Reasoning With Time-Dependent Phenomena\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9904441\n",
"240: 0.600 RankFIRST: Visual Analysis for Factor Investment By Ranking\n",
"Stock Timeseries\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9866555\n",
"258: 0.600 Systematic Review of Augmented Reality Training Systems\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9852696\n",
"261: 0.600 CreatureShop: Interactive 3D Character Modeling and Texturing\n",
"from a Single Color Drawing\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9847102\n",
"268: 0.600 <italic>MD-Cave</italic>: An Immersive Visualization\n",
"Workbench for Radiologists\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9930144\n",
"221: 0.600 Out of the Plane: Flower Vs. Star Glyphs to Support High-\n",
"Dimensional Exploration in Two-Dimensional Embeddings\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9844860\n",
"269: 0.600 Analysis of the Saliency of Color-Based Dichoptic Cues in\n",
"Optical See-Through Augmented Reality\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9937064\n",
"217: 0.600 Visual Exploration of Machine Learning Model Behavior with\n",
"Hierarchical Surrogate Rule Sets\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9911682\n",
"232: 0.600 Effect of Vibrations on Impression of Walking and Embodiment\n",
"With First- and Third-Person Avatar\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9939115\n",
"216: 0.600 DocFlow: A Visual Analytics System for Question-based\n",
"Document Retrieval and Categorization\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124828\n",
"83: 0.600 Toward More Comprehensive Evaluations of 3D Immersive\n",
"Sketching, Drawing, and Painting\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9984953\n",
"191: 0.600 VISAtlas: An Image-based Exploration and Query System for\n",
"Large Visualization Collections via Neural Image Embedding\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075482\n",
"113: 0.600 An Evaluation of View Rotation Techniques for Seated\n",
"Navigation in Virtual Reality\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123076\n",
"85: 0.600 PlanNet: A Generative Model for Component-Based Plan Synthesis\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122175\n",
"87: 0.600 A Scalable Method for Readable Tree Layouts\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106472\n",
"93: 0.600 SceneDirector: Interactive Scene Synthesis by Simultaneously\n",
"Editing Multiple Objects in Real-Time\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097564\n",
"97: 0.600 A Visual Analytics Conceptual Framework for Explorable and\n",
"Steerable Partial Dependence Analysis\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081386\n",
"102: 0.600 Image Collage on Arbitrary Shape via Shape-Aware Slicing and\n",
"Optimization\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076832\n",
"107: 0.600 StyleVR: Stylizing Character Animations with Normalizing\n",
"Flows\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068257\n",
"117: 0.600 Interactive Subspace Cluster Analysis Guided by Semantic\n",
"Attribute Associations\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10056593\n",
"128: 0.600 Visual Explanation for Open-domain Question Answering with\n",
"BERT\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057994\n",
"124: 0.600 DMiner: Dashboard Design Mining and Recommendation\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057127\n",
"127: 0.600 MoReVis: A Visual Summary for Spatiotemporal Moving Regions\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10239303\n",
"6: 0.600 Visualizing and Comparing Machine Learning Predictions to\n",
"Improve Human-AI Teaming on the Example of Cell Lineage\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10234572\n",
"7: 0.600 On Network Structural and Temporal Encodings: A Space and Time\n",
"Odyssey\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10232889\n",
"11: 0.600 Synthesize Personalized Training for Robot-assisted Upper Limb\n",
"Rehabilitation with Diversity Enhancement\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10221769\n",
"20: 0.600 Neural Novel Actor: Learning a Generalized Animatable Neural\n",
"Representation for Human Actors\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130114\n",
"79: 0.600 Tailorable Sampling for Progressive Visual Analytics\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132494\n",
"73: 0.600 Does Multi-Actuator Vibrotactile Feedback Within Tangible\n",
"Objects Enrich VR Manipulation?\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10143242\n",
"68: 0.600 Shading-guided Manga Screening from Reference\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10153661\n",
"57: 0.600 Pose Guided Person Image Generation via Dual-task Correlation\n",
"and Affinity Learning\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10154019\n",
"56: 0.600 A Visual Environment for Data Driven Protein Modeling and\n",
"Validation\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10155253\n",
"55: 0.600 Practical Integer-Constrained Cone Construction for Conformal\n",
"Parameterizations\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10158055\n",
"53: 0.600 Characteristic-preserving Latent Space for Unpaired Cross-\n",
"domain Translation of 3D Point Clouds\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10163898\n",
"50: 0.600 Local Geometric Indexing of High Resolution Data for Facial\n",
"Reconstruction From Sparse Markers\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10168294\n",
"46: 0.600 HDhuman: High-quality Human Novel-view Rendering from Sparse\n",
"Views\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10173597\n",
"42: 0.600 Improving Knowledge Retention and Perceived Control through\n",
"Serious Games: a Study about Assisted Emergency Evacuation\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10173631\n",
"41: 0.600 Investigating the Visual Utility of Differentially Private\n",
"Scatterplots\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10175377\n",
"40: 0.600 Interactive Volume Visualization Via Multi-Resolution Hash\n",
"Encoding Based Neural Representation\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10182398\n",
"36: 0.600 Adaptive color transfer from images to terrain visualizations\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10183683\n",
"35: 0.600 To Stick or Not to Stick? Studying the Impact of Offset\n",
"Recovery Techniques During Mid-Air Interactions\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10216926\n",
"21: 0.600 Stylizing Ribbons: Computing Surface Contours With Temporally\n",
"Coherent Orientations\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9993758\n",
"182: 0.600 A Visual Comparison of Silent Error Propagation\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10004748\n",
"173: 0.600 Comparative Study and Evaluation of Hybrid Visualizations of\n",
"Graphs\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10002893\n",
"175: 0.600 Reasoning Affordances with Tables and Bar Charts\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9999502\n",
"178: 0.600 Spatio-Temporal Visual Analysis of Turbulent Superstructures\n",
"in Unsteady Flow\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9998319\n",
"181: 0.600 From Invisible to Visible: Impacts of Metadata in\n",
"Communicative Data Visualization\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9993762\n",
"183: 0.600 PoseCoach: A Customizable Analysis and Visualization System\n",
"for Video-based Running Coaching\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9992117\n",
"186: 0.600 GPU Accelerated 3D Tomographic Reconstruction and\n",
"Visualization from Noisy Electron Microscopy Tilt-Series\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9987696\n",
"190: 0.600 Automorphism Faithfulness Metrics for Symmetric Graph\n",
"Drawings\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048575\n",
"133: 0.600 Sensory Attenuation with a Virtual Robotic Arm Controlled\n",
"Using Facial Movements\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10045801\n",
"134: 0.600 Anchorage: Visual Analysis of Satisfaction in Customer\n",
"Service Videos Via Anchor Events\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10045018\n",
"136: 0.600 Monocular Depth Decomposition of Semi-Transparent Volume\n",
"Renderings\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10043789\n",
"138: 0.600 A Parametric Design Method for Engraving Patterns on Thin\n",
"Shells\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10041718\n",
"141: 0.600 A Visual Interface for Exploring Hypotheses about Neural\n",
"Circuits\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9834145\n",
"273: 0.600 Visual Cue Effects on a Classification Accuracy Estimation\n",
"Task in Immersive Scatterplots\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10026499\n",
"148: 0.600 XNLI: Explaining and Diagnosing NLI-based Visual Data\n",
"Analysis\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10024310\n",
"154: 0.600 Path Tracing in 2D, 3D, and Physicalized Networks\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10024388\n",
"155: 0.600 Graph Exploration with Embedding-Guided Layouts\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10023999\n",
"158: 0.600 VR Blowing: A Physically Plausible Interaction Method for\n",
"Blowing Air in Virtual Reality\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10021890\n",
"160: 0.600 STTAR: Surgical Tool Tracking using Off-the-Shelf Augmented\n",
"Reality Head-Mounted Displays\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10021888\n",
"161: 0.600 Continuous Scatterplot Operators for Bivariate Analysis and\n",
"Study of Electronic Transitions\n",
"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10021892\n",
"162: 0.600 Discrete Morse Sandwich: Fast Computation of Persistence\n",
"Diagrams for Scalar Data – An Algorithm and A Benchmark\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "le1jtRFg98ce"
},
"execution_count": null,
"outputs": []
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment