diff --git "a/20250328_t2ranking处理.ipynb" "b/20250328_t2ranking处理.ipynb" new file mode 100644--- /dev/null +++ "b/20250328_t2ranking处理.ipynb" @@ -0,0 +1,3859 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "baa06260424c4f079f0250950900c535": { + "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_ef6434ba075c46a185e1c99fae81713c", + "IPY_MODEL_56e2f23e4a4c4afd9c0195c53c53e6ab", + "IPY_MODEL_8f734141c6514beb869ffd4cb08e60f6" + ], + "layout": "IPY_MODEL_a2577001f8274e0281d0b95ded1543ba" + } + }, + "ef6434ba075c46a185e1c99fae81713c": { + "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_178ee68b4d8341c5b42f645177a30c48", + "placeholder": "​", + "style": "IPY_MODEL_d79c30c1ea1d4f0f9fa0b4d0a82f4fc6", + "value": "Uploading the dataset shards: 100%" + } + }, + "56e2f23e4a4c4afd9c0195c53c53e6ab": { + "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_9a1a792d57304ad08c51baadaa54f224", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_b23df70fef024ce2b7b9b9ce1d1c12bb", + "value": 1 + } + }, + "8f734141c6514beb869ffd4cb08e60f6": { + "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_178b2741232e4c62a60c9665bf720579", + "placeholder": "​", + "style": "IPY_MODEL_5e95f559ca63450b8f9678f7404d8de4", + "value": " 1/1 [00:02<00:00,  2.10s/it]" + } + }, + "a2577001f8274e0281d0b95ded1543ba": { + "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 + } + }, + "178ee68b4d8341c5b42f645177a30c48": { + "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 + } + }, + "d79c30c1ea1d4f0f9fa0b4d0a82f4fc6": { + "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": "" + } + }, + "9a1a792d57304ad08c51baadaa54f224": { + "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 + } + }, + "b23df70fef024ce2b7b9b9ce1d1c12bb": { + "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": "" + } + }, + "178b2741232e4c62a60c9665bf720579": { + "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 + } + }, + "5e95f559ca63450b8f9678f7404d8de4": { + "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": "" + } + }, + "9e027dc25688494fb356494fe41d01ea": { + "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_b171e2fb40584d53bb45ca0c62605162", + "IPY_MODEL_ac30d6bab2b343a1a44b0c6511947434", + "IPY_MODEL_a55bd449648745909b6753a124c1910e" + ], + "layout": "IPY_MODEL_99a6b254a63a4d78b9f5957b69aeee65" + } + }, + "b171e2fb40584d53bb45ca0c62605162": { + "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_d72db3e3edb24b2680afc11ad450a279", + "placeholder": "​", + "style": "IPY_MODEL_a0c0cec387ee4b25a8e92ff9f7c64e86", + "value": "Creating parquet from Arrow format: 100%" + } + }, + "ac30d6bab2b343a1a44b0c6511947434": { + "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_90987220b3ad4b7c80b5573710e6ad85", + "max": 5, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_3cf90ac9d04a4b9c9f04fe884f991404", + "value": 5 + } + }, + "a55bd449648745909b6753a124c1910e": { + "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_4be699695c4f485eae90d601111011d2", + "placeholder": "​", + "style": "IPY_MODEL_ba30195bcaa24336a5af29d9280f2dfe", + "value": " 5/5 [00:00<00:00, 15.88ba/s]" + } + }, + "99a6b254a63a4d78b9f5957b69aeee65": { + "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 + } + }, + "d72db3e3edb24b2680afc11ad450a279": { + "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 + } + }, + "a0c0cec387ee4b25a8e92ff9f7c64e86": { + "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": "" + } + }, + "90987220b3ad4b7c80b5573710e6ad85": { + "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 + } + }, + "3cf90ac9d04a4b9c9f04fe884f991404": { + "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": "" + } + }, + "4be699695c4f485eae90d601111011d2": { + "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 + } + }, + "ba30195bcaa24336a5af29d9280f2dfe": { + "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": "" + } + }, + "613dd88aaf684dc0b2f159d48ef4a72f": { + "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_86b72d81f9884ab4892c0a66bb189a35", + "IPY_MODEL_695db22343564105b604f4a7c4dc1d97", + "IPY_MODEL_0601003a6ce14525bc52f451e45d4830" + ], + "layout": "IPY_MODEL_8064e5469a3b4794a67d57c0329e1a83" + } + }, + "86b72d81f9884ab4892c0a66bb189a35": { + "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_ae242adfdeda4138966151a6f4b2a881", + "placeholder": "​", + "style": "IPY_MODEL_b0393fba9ce24c598e5a34feadc56f5f", + "value": "Uploading the dataset shards: 100%" + } + }, + "695db22343564105b604f4a7c4dc1d97": { + "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_397f89172d2e4de4b80cace6b48c6d9b", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_c0eca1e6909c4f91a886a302f820b8b8", + "value": 1 + } + }, + "0601003a6ce14525bc52f451e45d4830": { + "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_e0cbd109f265428abd62e2c06ed9b8e5", + "placeholder": "​", + "style": "IPY_MODEL_04271c6b6f78486aa65db1b2887cbb22", + "value": " 1/1 [00:00<00:00,  1.79it/s]" + } + }, + "8064e5469a3b4794a67d57c0329e1a83": { + "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 + } + }, + "ae242adfdeda4138966151a6f4b2a881": { + "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 + } + }, + "b0393fba9ce24c598e5a34feadc56f5f": { + "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": "" + } + }, + "397f89172d2e4de4b80cace6b48c6d9b": { + "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 + } + }, + "c0eca1e6909c4f91a886a302f820b8b8": { + "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": "" + } + }, + "e0cbd109f265428abd62e2c06ed9b8e5": { + "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 + } + }, + "04271c6b6f78486aa65db1b2887cbb22": { + "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": "" + } + }, + "72afd67e51c748f0927101749cf2b5fd": { + "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_d6668925180e4d61837c097d76fdf863", + "IPY_MODEL_dc5aff585035477c8e9b067455cdeb27", + "IPY_MODEL_89c47ed7c85a48f496a76fd5ae41868d" + ], + "layout": "IPY_MODEL_8b5980b36c1f44d88b55fdb06a1d5d91" + } + }, + "d6668925180e4d61837c097d76fdf863": { + "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_e65abcb51cbf4ddc913ce29165f5f803", + "placeholder": "​", + "style": "IPY_MODEL_c46c9393cd2a4d7ebd1223711b1e13b7", + "value": "Creating parquet from Arrow format: 100%" + } + }, + "dc5aff585035477c8e9b067455cdeb27": { + "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_9f1bd43cf0914272bd64e17007d92638", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_6dafc629ca0449538a2f4164e9a1dde8", + "value": 1 + } + }, + "89c47ed7c85a48f496a76fd5ae41868d": { + "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_e9151b44eb7e494699beb65a21b17ab2", + "placeholder": "​", + "style": "IPY_MODEL_7daf0e71ae6f40518d9a0052117e69d1", + "value": " 1/1 [00:00<00:00, 14.70ba/s]" + } + }, + "8b5980b36c1f44d88b55fdb06a1d5d91": { + "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 + } + }, + "e65abcb51cbf4ddc913ce29165f5f803": { + "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 + } + }, + "c46c9393cd2a4d7ebd1223711b1e13b7": { + "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": "" + } + }, + "9f1bd43cf0914272bd64e17007d92638": { + "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 + } + }, + "6dafc629ca0449538a2f4164e9a1dde8": { + "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": "" + } + }, + "e9151b44eb7e494699beb65a21b17ab2": { + "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 + } + }, + "7daf0e71ae6f40518d9a0052117e69d1": { + "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": "" + } + }, + "391eff8f2a894db08c88baf7c65e94dc": { + "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_4b0b05af8d124d34998448302a568d38", + "IPY_MODEL_ffb9c0c5ca154fc5a7221b6ba4b497ba", + "IPY_MODEL_9176f9accfd94fd2a1e86f30121ad365" + ], + "layout": "IPY_MODEL_ec103b8f4ca449a5ab47999421e08cb4" + } + }, + "4b0b05af8d124d34998448302a568d38": { + "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_eecc36d6419441c5ad970846753c3eb2", + "placeholder": "​", + "style": "IPY_MODEL_a16a6c20de274fffa59e6fa78ba66bb1", + "value": "Uploading the dataset shards: 100%" + } + }, + "ffb9c0c5ca154fc5a7221b6ba4b497ba": { + "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_ba86a4e376c54c6c8dd06278082d8e31", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_8f0b5204839e48f99831ea82478e0e26", + "value": 1 + } + }, + "9176f9accfd94fd2a1e86f30121ad365": { + "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_ed18d4e634dc4c54aa2e6de3939131ac", + "placeholder": "​", + "style": "IPY_MODEL_984b41e36f1647e099e351ad4855c96c", + "value": " 1/1 [00:00<00:00,  1.24it/s]" + } + }, + "ec103b8f4ca449a5ab47999421e08cb4": { + "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 + } + }, + "eecc36d6419441c5ad970846753c3eb2": { + "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 + } + }, + "a16a6c20de274fffa59e6fa78ba66bb1": { + "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": "" + } + }, + "ba86a4e376c54c6c8dd06278082d8e31": { + "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 + } + }, + "8f0b5204839e48f99831ea82478e0e26": { + "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": "" + } + }, + "ed18d4e634dc4c54aa2e6de3939131ac": { + "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 + } + }, + "984b41e36f1647e099e351ad4855c96c": { + "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": "" + } + }, + "bbe37add810247ce96f1bce6948a8dc9": { + "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_7a2d7ed3ac674c0fb45fb938d286ae2a", + "IPY_MODEL_832f1293427342e18a3f4099222bbb61", + "IPY_MODEL_9e59dee564b64b9eb9f7d0a67c43e548" + ], + "layout": "IPY_MODEL_ba1302210765460b9a383dc99e97ef53" + } + }, + "7a2d7ed3ac674c0fb45fb938d286ae2a": { + "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_d7991fe5b7e04a16a3667de2c05f7575", + "placeholder": "​", + "style": "IPY_MODEL_f041098dad4b4df8a5103271ff14f213", + "value": "Creating parquet from Arrow format: 100%" + } + }, + "832f1293427342e18a3f4099222bbb61": { + "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_dd1a1bac484f4c839c10c2ece4f9f911", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_613bcd2618d74d2bb4cc6365a4f83236", + "value": 1 + } + }, + "9e59dee564b64b9eb9f7d0a67c43e548": { + "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_19c3b2f5b5634ac8964d3ce23e2e8390", + "placeholder": "​", + "style": "IPY_MODEL_4084d8d01f824cdfb408bd5d2967faac", + "value": " 1/1 [00:00<00:00,  9.80ba/s]" + } + }, + "ba1302210765460b9a383dc99e97ef53": { + "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 + } + }, + "d7991fe5b7e04a16a3667de2c05f7575": { + "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 + } + }, + "f041098dad4b4df8a5103271ff14f213": { + "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": "" + } + }, + "dd1a1bac484f4c839c10c2ece4f9f911": { + "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 + } + }, + "613bcd2618d74d2bb4cc6365a4f83236": { + "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": "" + } + }, + "19c3b2f5b5634ac8964d3ce23e2e8390": { + "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 + } + }, + "4084d8d01f824cdfb408bd5d2967faac": { + "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": "code", + "execution_count": null, + "metadata": { + "id": "9LrNllwDhJa3" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Login using e.g. `huggingface-cli login` to access this dataset\n", + "df_corpus = pd.read_parquet(\"hf://datasets/mteb/T2Reranking/corpus/dev-00000-of-00001.parquet\")\n", + "\n", + "# Login using e.g. `huggingface-cli login` to access this dataset\n", + "df_qrels = pd.read_parquet(\"hf://datasets/mteb/T2Reranking/data/dev-00000-of-00001.parquet\")\n", + "\n", + "# Login using e.g. `huggingface-cli login` to access this dataset\n", + "df_queries = pd.read_parquet(\"hf://datasets/mteb/T2Reranking/queries/dev-00000-of-00001.parquet\")\n", + "\n", + "df_top_ranked = pd.read_parquet(\"hf://datasets/mteb/T2Reranking/top_ranked/dev-00000-of-00001.parquet\")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 数据集分析" + ], + "metadata": { + "id": "IPAFU4S7iZVR" + } + }, + { + "cell_type": "code", + "source": [ + "df_queries" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "CCCjxOkyid8T", + "outputId": "4765cc05-f54b-49f7-f204-c929c4718dd9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " _id text\n", + "0 dev_query0 蜂巢取快递验证码摁错怎么办\n", + "1 dev_query1 生产过后怎么还有一层肚子\n", + "2 dev_query2 大学怎么网上选宿舍\n", + "3 dev_query3 怎么判断鱼卵是否活着\n", + "4 dev_query4 胆碱重要吗\n", + "... ... ...\n", + "5903 dev_query5903 手机屏幕投屏到电视上怎么操作\n", + "5904 dev_query5904 圆台体积公式面积\n", + "5905 dev_query5905 婆婆性格特别强势,还很现实\n", + "5906 dev_query5906 竞价外包多少钱\n", + "5907 dev_query5907 y=cosx图像\n", + "\n", + "[5908 rows x 2 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
_idtext
0dev_query0蜂巢取快递验证码摁错怎么办
1dev_query1生产过后怎么还有一层肚子
2dev_query2大学怎么网上选宿舍
3dev_query3怎么判断鱼卵是否活着
4dev_query4胆碱重要吗
.........
5903dev_query5903手机屏幕投屏到电视上怎么操作
5904dev_query5904圆台体积公式面积
5905dev_query5905婆婆性格特别强势,还很现实
5906dev_query5906竞价外包多少钱
5907dev_query5907y=cosx图像
\n", + "

5908 rows × 2 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df_queries", + "summary": "{\n \"name\": \"df_queries\",\n \"rows\": 5908,\n \"fields\": [\n {\n \"column\": \"_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5908,\n \"samples\": [\n \"dev_query1539\",\n \"dev_query2607\",\n \"dev_query3685\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5908,\n \"samples\": [\n \"\\u8089\\u6587\\u7684\\u63cf\\u5199\\u6709\\u54ea\\u4e9b\",\n \"\\u5f39\\u4e38\\u8bba\\u7834 \\u83b2\\u84ec\\u5934\\u4ee5\\u5916\\u7684\\u4e1c\\u897f\",\n \"\\u7ed2\\u5154\\u661f\\u7403\\u4ec0\\u4e48\\u610f\\u601d\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 2 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_qrels" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "dEFuGK6mipeG", + "outputId": "780cf7b5-19a2-4248-f4f2-c414018c18b6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " query-id corpus-id score\n", + "0 dev_query0 apositive_dev_query0_00000 1\n", + "1 dev_query0 apositive_dev_query0_00001 1\n", + "2 dev_query0 apositive_dev_query0_00002 1\n", + "3 dev_query0 apositive_dev_query0_00003 1\n", + "4 dev_query0 apositive_dev_query0_00004 1\n", + "... ... ... ...\n", + "97417 dev_query5907 negative_dev_query5907_00002 0\n", + "97418 dev_query5907 negative_dev_query5907_00003 0\n", + "97419 dev_query5907 negative_dev_query5907_00004 0\n", + "97420 dev_query5907 negative_dev_query5907_00005 0\n", + "97421 dev_query5907 negative_dev_query5907_00006 0\n", + "\n", + "[97422 rows x 3 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
query-idcorpus-idscore
0dev_query0apositive_dev_query0_000001
1dev_query0apositive_dev_query0_000011
2dev_query0apositive_dev_query0_000021
3dev_query0apositive_dev_query0_000031
4dev_query0apositive_dev_query0_000041
............
97417dev_query5907negative_dev_query5907_000020
97418dev_query5907negative_dev_query5907_000030
97419dev_query5907negative_dev_query5907_000040
97420dev_query5907negative_dev_query5907_000050
97421dev_query5907negative_dev_query5907_000060
\n", + "

97422 rows × 3 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df_qrels", + "summary": "{\n \"name\": \"df_qrels\",\n \"rows\": 97422,\n \"fields\": [\n {\n \"column\": \"query-id\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5908,\n \"samples\": [\n \"dev_query1539\",\n \"dev_query2607\",\n \"dev_query3685\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"corpus-id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 97422,\n \"samples\": [\n \"negative_dev_query4577_00001\",\n \"negative_dev_query3730_00003\",\n \"apositive_dev_query3107_00003\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_qrels['corpus-id'].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "6PhasTLYir_2", + "outputId": "d6716817-6e7c-4e62-e33b-bbf5b463eaae" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "corpus-id\n", + "negative_dev_query5907_00006 1\n", + "apositive_dev_query0_00000 1\n", + "apositive_dev_query0_00001 1\n", + "apositive_dev_query0_00002 1\n", + "apositive_dev_query0_00003 1\n", + " ..\n", + "apositive_dev_query1_00005 1\n", + "apositive_dev_query1_00004 1\n", + "apositive_dev_query1_00003 1\n", + "apositive_dev_query1_00002 1\n", + "apositive_dev_query1_00001 1\n", + "Name: count, Length: 97422, dtype: int64" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
count
corpus-id
negative_dev_query5907_000061
apositive_dev_query0_000001
apositive_dev_query0_000011
apositive_dev_query0_000021
apositive_dev_query0_000031
......
apositive_dev_query1_000051
apositive_dev_query1_000041
apositive_dev_query1_000031
apositive_dev_query1_000021
apositive_dev_query1_000011
\n", + "

97422 rows × 1 columns

\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_qrels['query-id'].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "GrQ3MtOYiygq", + "outputId": "418ff5b6-95ff-4a2a-e8d2-1fb867529818" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "query-id\n", + "dev_query4652 335\n", + "dev_query5041 155\n", + "dev_query5587 99\n", + "dev_query2782 94\n", + "dev_query281 83\n", + " ... \n", + "dev_query4465 3\n", + "dev_query2611 3\n", + "dev_query1785 3\n", + "dev_query3937 3\n", + "dev_query4600 2\n", + "Name: count, Length: 5908, dtype: int64" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
count
query-id
dev_query4652335
dev_query5041155
dev_query558799
dev_query278294
dev_query28183
......
dev_query44653
dev_query26113
dev_query17853
dev_query39373
dev_query46002
\n", + "

5908 rows × 1 columns

\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_top_ranked.iloc[1,1]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7wAGNcWrszj-", + "outputId": "25418f62-69aa-45ba-fb67-6e12f8eea748" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['apositive_dev_query1_00000', 'apositive_dev_query1_00001',\n", + " 'apositive_dev_query1_00002', 'apositive_dev_query1_00003',\n", + " 'apositive_dev_query1_00004', 'apositive_dev_query1_00005',\n", + " 'apositive_dev_query1_00006', 'apositive_dev_query1_00007',\n", + " 'negative_dev_query1_00000', 'negative_dev_query1_00001',\n", + " 'negative_dev_query1_00002', 'negative_dev_query1_00003'],\n", + " dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 数据集处理" + ], + "metadata": { + "id": "-1TuL-mQi_uY" + } + }, + { + "cell_type": "markdown", + "source": [ + "### 分组方案一\n", + "- query当作条件，\n", + "- corpus做为s1和s2（区相似度在前两条的），\n", + "- 再取s3（作为负样本），\n", + "- 然后s1、s2在此相条件下相似度记为1，\n", + "- s1、s3在此条件下，相似度记为0。\n", + "- 每个groupby（'query-id'）分组中只取1条。\n", + "\n", + "一共会产出5908条数据" + ], + "metadata": { + "id": "9H64ohzzjCoD" + } + }, + { + "cell_type": "code", + "source": [ + "name_group_list = [(name[0], group) for name, group in df_qrels.groupby(['query-id'])]\n", + "print(len(name_group_list))\n", + "print(len(name_group_list[0][1]))\n", + "print(name_group_list[0][1])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fqt3Ar9YjQRS", + "outputId": "9d5102a9-444d-4689-831d-418be3a01961" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "5908\n", + "13\n", + " query-id corpus-id score\n", + "0 dev_query0 apositive_dev_query0_00000 1\n", + "1 dev_query0 apositive_dev_query0_00001 1\n", + "2 dev_query0 apositive_dev_query0_00002 1\n", + "3 dev_query0 apositive_dev_query0_00003 1\n", + "4 dev_query0 apositive_dev_query0_00004 1\n", + "5 dev_query0 negative_dev_query0_00000 0\n", + "6 dev_query0 negative_dev_query0_00001 0\n", + "7 dev_query0 negative_dev_query0_00002 0\n", + "8 dev_query0 negative_dev_query0_00003 0\n", + "9 dev_query0 negative_dev_query0_00004 0\n", + "10 dev_query0 negative_dev_query0_00005 0\n", + "11 dev_query0 negative_dev_query0_00006 0\n", + "12 dev_query0 negative_dev_query0_00007 0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def handle(row):\n", + " query_id, condition = row['_id'], row['text']\n", + " anchor, pos, neg = None, None, None\n", + " pos_df = df_corpus[(df_corpus['_id'] == 'apositive_' + query_id + '_00000') | (df_corpus['_id'] == 'apositive_' + query_id + '_00001')]['text']\n", + " if len(pos_df) >= 1:\n", + " anchor = pos_df.iloc[0]\n", + " if len(pos_df) >= 2:\n", + " pos = pos_df.iloc[1]\n", + "\n", + " neg_df = df_corpus[df_corpus['_id'] == 'negative_'+ query_id + '_00000']['text']\n", + " if len(neg_df) >= 1:\n", + " neg = neg_df.iloc[0]\n", + "\n", + " return (condition, anchor, pos, neg)\n", + "\n", + "print(handle({\"_id\":\"dev_query1\", \"text\":\"生产过后怎么还有一层肚子\"}))\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AUsrAjm1kEyP", + "outputId": "baac4927-7e1b-4ed5-b89d-0175929f9753" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "('生产过后怎么还有一层肚子', '女性生物孩子后很多人肚子会留下厚厚的一层赘肉,对于爱美的女人是不能接受的,那么要怎么做才能在产后消除腹部赘肉呢?下面小编就带你一起来了解一下吧!
一、剖腹产后如何温和消除腹部赘肉?
1、保持耐心
不要忘了你的身体是历经9个月才获得这些重量,所以它不可能一夕消失。慢慢来、保持耐心,这是产后减肥的基础,特别是你刚经历过手术,需要更多时间等身体痊愈。
2、哺育母乳
剖腹产后最快消除腹部脂肪的方式就是哺育宝宝母乳,至少6个月。哺乳可以燃烧额外的卡路里,减少腹部顽固的脂肪。
3、正确吃点心
哺乳期间你会非常容易饥饿,但你必须吃正确的食物,才能避免增加额外的重量。水果、面包、全脂牛奶、酸奶、豆类、瘦肉等,都是非常好的选择。
4、帮助身体排毒
排毒可以提高肾脏和肝功能,尤其在生产后。这也是给你平坦腹部的好方式。你需要采用安全的排毒方式,像是食用整个水果和新鲜蔬菜,或是饮用生姜茶、绿茶和蜂蜜水。
5、使用收腹带
使用腹部的支撑带可以帮助你收紧腹部的肌肉,并让松弛的腹部渐渐平坦。此外,它可以减少背痛、保护手术伤口和改善你的姿势。
6、进行低冲击的运动
怀孕对腹部肌肉和骨盆底造成严重的伤害,所以你需要等待6-8星期之后,才能开始进行低冲击的运动,之后再慢慢增加强度。
二、减少剖腹产后腹部的练习
1、每天步行
步行是剖腹产后开始建立运动习惯的好练习。在平坦的地面行走,不仅低冲击,还会燃烧热量,增加你的新陈代谢。你可以从每天步行500公尺开始。
2、游泳
这是另一个低冲击的运动,可以帮助腹部平坦。游泳锻炼对剖腹产的女性是安全的,而且对关节是温和的。游泳也会增强肌肉,以及心血管耐力。
3、向前弯曲练习
向前弯曲是一个非常简单的肚子锻炼,一整天随时可以进行。这是一个站立运动,可以使你的腹部肌肉更强壮。站立双脚稍微打开,身体往前弯,并保持双腿直立。
4、进行拱桥运动
这个运动可以调整你的肚子却又不施加任何的压力在伤口上。躺在地上,双脚弯曲脚掌贴地。双手放在身侧,手掌贴地,然后再撑起屁股离开地面10秒钟。', '刚刚生完孩子的时候,孕妇妈妈们会遇上种种的烦心事。其中不乏坐月子摊上坏婆婆、产后开奶不及时、身材严重走形、遭遇产后抑郁等等。 但是,所有之中,有一种是每一位孕妇妈妈避之而不及的,那就是产后肚子上那一层又一层的褶皱。 这不,有一种肚子叫刚刚生完孩子…… 小叶今年26岁,熬过孕期之后,她顺产分娩下一枚可爱的儿子了。但是,在产后小叶却迟迟开心不起来,因为她开始嫌弃自己了,嫌弃自己的不仅越来越邋遢,而且肚子也变得面目全非。 肚子变得面目全非?没错的��原来在怀孕的时候,小叶隆起的肚子上就长有一条条红色的纹路,在小叶顺利分娩下宝宝之后,产后这些红色的纹路不仅没有消失,反而变本加厉了,变得皱巴巴的。 这些皱巴巴的纹路,正是令许多孕妇妈妈们烦恼的妊娠纹。 育儿小知识: 妊娠纹,在广义上,它属于萎缩纹的一种,指的是孕妇妈妈在孕中期的时候,肚子的迅速隆起严重破坏皮肤弹性支撑组织,使得皮肤变薄变细,毛细血管逐渐扩张而造成血液颜色外露,从而出现长度宽度各不一样的粉红色或紫红色细纹。 而后,经过时间的积淀,这些细纹会淡化变成或白或银白的疤痕。 对于肚子上这些难看的不速之客,相信每一位妈妈都对之恨之入骨。毕竟看着原本光滑的腹部,变得崎岖不堪,形成了一层又一层的褶皱,以至于妈妈们在夏天连穿露肚脐的衣服都不敢了,及更别说穿泳装了。 那么,对于难看至极的妊娠纹,妈妈们该如何是好呢? 其实,妊娠纹出现之后,一般来说是没有办法完全消除的。', '让产后妈妈最烦恼的是什么?不用多想肯定是产后如何恢复肚子,都说生育是女人的分水岭一点也不错,生完孩子后我的肚子一层一层的像千层糕一样叠在肚皮上,松松垮垮,穿什么衣服都走形,而且怎么减都减不掉,即使因怀孕而发胖的其他部分都在逐渐恢复,但凸起的肚子似乎没有任何动摇。为此老公还调侃我肚子里还有一个,平时出门也有嘴欠的说我“孩子还这么小,肚子里又揣了一个了!”有碎嘴的还会问我肚子里的几个月了。产后五个月,肚子依然像怀孕5个月,妈妈们知道这是为什么吗?
为什么产后肚子还很大?其实大部分妈妈们的肚子减不下去的原因是用错了方法!肚子之所以是产后妈妈最易导致身体变形的地方,为什么产后肚子还是很大原因在于腹部在生育过程中过度伸张,造成产后女性腹部松弛,这种松弛的腹肌及增大的宫腔得不到及时的复原,极易导致脂肪堆积腹部,形成大肚腩,不仅影响美观,更是产后身体变形和诸多疾病的罪魁祸首。其是产后想要恢复肚子很简单,主需要两步:1、调节易瘦体质,越早越好,众所周知孕期肥胖是因为孕产激愫引起的,而体内激素的含量外力是无法改变的,需要国际认可的HICIBI调节体内孕产激愫含量,调节易瘦体质,同时随着身体激愫的恢复,胃、肠子都会一点一点的回到孕前状态,产后肚子大自然就恢复了。2、保持正常心态。要想恢复大肚子,保持心态很重要,根据很多研究得出,正确的心态是减肥成功的有力保证,因为这是一种潜意识的影响,平时可以多听听轻松的音乐,但是不要听着睡觉,否则会造成睡眠质量不高,或者睡眠质量过高都对减肥没有帮助!

产后如何恢复肚子,产后瘦肚子是非常简单的事:
产后为了瘦肚子靠极端方式让自己瘦下来在我看来实在是非常不可取,然而产后子宫还未恢复,剧烈运动容易导致内脏失去支撑很容易下垂。所以宝妈们,请好好爱护你自己的身体,给自己足够的耐心和时间。是,身材需要看起来好看,漂亮小裙子也要穿回去,但这需要采用科学的方法。
1、不节食,不运动,不影响奶水,产后瘦身有方法!
从孕育到生产,女人生孩子尤如打了一场仗一样,虽然瓜熟蒂落,但身体和心理却发生了巨大的变化。色素沉着、代谢瘦慢、产后肥胖等等的问题接踵而来。产后理想瘦身的前提是先养好体质,激活人体代谢平衡的机制。只有让身体处于一个健康年轻的状态,才能稳稳的瘦下来。
如何激活人体代谢平衡呢?
分娩时女性体内的激愫与分娩后有很大的差别,所以在分娩后,身体短时间内需要适应雌孕激愫的调整,这期间,脂肪的代谢也会受到影响,从而导致体重增加。
针对于产后内环境发生变化,通过HICIBI建立产后减脂通道,启动三羧酸循环,三羧酸循环是三大营养素(糖类、脂类、氨基酸)的终代谢通路,又是糖类、脂类、氨基酸代谢联系的枢纽。促使囤积的脂肪被调动起来转化为糖分,填充糖分的缺口,大量脂肪转化成甘由和璘脂进入三羧循环中,为人体供给能源,维持日常的功能。我们身体囤积的脂肪成为我们身体日常基础代谢的消耗源头,不断的分化和代谢脂肪。
2、个性化的制定瘦身方案,保证哺乳需求
对于妈妈来说,除了顺利的瘦下来,“母乳安全”是大家顾虑很多的一个问题。欧洲的产后瘦身师,通过能量平衡的原理使身体处于消耗大于摄入的状态,不但能从本质上瘦去内脂、外脂,还能有助于产后宝妈身体的恢复, 提高代谢, 是不会影响母乳喂养的。
能量平衡的原理是什么呢?
能量平衡的的原理就是过程中能量摄入和能量输出及贮存之间的平衡关系。在各种生理状态下,能量的摄入绝大部分来自食物中所含有的化学能,而支出则包括粪便、尿液和消化道气体包含的能量,和对外作功等所消耗的能量是均衡的。
能量的平衡并不是要求每个人在每天的能量摄取都要做到平衡,而是要求哺乳期女性在7—14天内其消耗的与摄入的热量平均值趋于相等。
在哺乳期体内消耗的热量必须从外界HICIBI孕产期营养群组I摄取才能得以补偿,使机体消耗的和摄取的能量趋于相等,营养学上称为能量的平衡。在评论产后的体重时首先要看他们是否摄入了均衡的营养,后才能看他们代谢出多少热量的比例是否合理,合理的体重应该是保证营养的同时热量也随即代谢。
6个月内妈妈体内脂肪是呈现游离状态,还未形成难减的脂肪,在这时期妈妈的月经已经恢复正常了,也就意味着妈妈的内分泌和能量失衡,所以产后减重就需要外力HICIBI孕产期营养群组I的帮助。

产后瘦肚子错误的方法只会让你越来越胖;
1、束腹带和骨盆带能帮我们恢复身材吗?
很多人认为束缚带、盆骨带可以帮我们防止内脏下垂,这样的话她也信!!怀孕的时候内脏确实都得给孩子腾地方,胃和肠子被顶着往上走,可孩子生出来了,内脏也不会咣当掉到盆腔里去,咱们人体的内脏都是由相应的韧带牵引、有固定位置的,生完孩子之后,随着身体激素的变化,胃、肠子都会一点一点的回到孕前状态,这个都不需要我们操心。
咱们的身体里面,上边是胸腔,下边是腹腔和盆腔,腹腔、盆腔是连在一起的,共同形成一个密闭的空间。绑腹带的时候,大家可能以为绑在小肚子上,能够把盆腔托住,事实上最下边的盆底是绑不住的。如果把盆腹腔这个密闭空间看成一个气球的话,这个腹带并不是托在了气球的底部,而是勒在了气球的中间,这气球中间一勒,气儿是不是都往两头走,向上项着胸腔走,勒紧了,,不光吃不下饭,还喘不上气;向下顶着盆腔,实际上是加重了整个盆底的压力,是不利于产后盆底康复的。
2、传统的腹肌训练有效果吗
传统的腹肌训练包括卷腹(自行车卷腹、触膝卷腹等),或者之前还会存在的仰卧起坐(现已简化为卷腹),然而产后妈妈不知道的是,这些动作恰好就是错误的。很多人认为产后肚子大是因为腹直肌分离,确实有这方面的原因,那为什么大部分妈妈一直坚持这样腹部训练但还是效果甚微呢?
当生完宝宝后,松弛无力的腹肌,尤其是腹横肌没有得到有效恢复,就会直接影响其功能,例如无法托住内部器官或者其他内部组织,导致这些器官或组织任性膨出,除了脂肪,(脂肪可以通过HICIBI调节易瘦体质,同时阻断食物中的热量,调动体内存储的脂肪转化为日常消耗的热量)这些就是造成“妈妈肚”的原因。
欧洲产后瘦身师建议配合哺乳期减肥HICIBI:HICIBI分为4部曲,前三部同时将食物的生物分子分离,保留一切有营养的纤维、维生素、母乳所需的热量等等,阻断一切让产妇制胖的热量(淀粉、糖、油脂),最后一部曲,将释放500倍弹力纤维与胶原三肽,收紧松弛的腹部。
传统的腹肌训练虽然对对腹直肌是有效果的,而对于腹横肌效果甚微,所以在腹横肌没有恢复的前提下,再多卷腹也是徒然,甚至带来的伤害比好处多。下面我给大家分享几个合适的动作,除了能很好地训练到腹横肌,还会对盆底肌有一定帮助,因为很多产后妈妈也会因为盆底肌无力出现失禁的尴尬现象。如果希望快速看到效果,建议至少每周4天,每天一到两次。
1、膝盖夹球腹式呼吸

2、弹力带抗阻伸腿

产后瘦肚子,如何恢复肚子弹性:
尽管身形仍玲珑有致,外表完全看不出曾经怀孕的痕迹,但其实松垮的肚皮正大大打击妈咪们的自信心!文献显示松垮的肚皮和妊娠纹均是皮肤层断裂,只要提高皮肤弹性,让表皮细胞和真皮细胞再生是可以修复妊娠纹的。
HICIBI孕产期营养群组I释放的弹性纤维:很多女性发现,生完宝宝之后,感觉自己老了很多,这是因为弹性单笔的流失因为生孩子皮肤张力以光速在消失,正常情况下,弹性蛋白在肌肤中就像橡皮筋的角色,让肌肤有伸展和褶合的能力,其功用就如床垫中的弹簧,负责维持与支撑肌肤的弹性。当女性怀孕超过3个月时,子宫不断扩张,促使皮肤弹性不断扩张,我们皮肤是否有弹性,是弹性蛋白和胶原三肽决定的。
胶原三肽生成皮肤纵向纤维,而弹性蛋白形成皮肤的横向弹力,正常弹性蛋白可以帮助胶原纤维的横向弹性增加5倍到30倍。而我们10个月的孕期可以把弹性纤维扩大到500倍,而缺少HICIBI孕产期营养群组I释放的10种分子促成弹性纤维的生长,就会出现生产或减重后的��肤松弛,还有妊娠纹情况。
产后女性一边想要减肥,恢复到怀孕前的苗条身材,一边又怕营养不够,影响喂奶和宝宝的身体健康。其实,产后减肥和正常哺乳是可以并存的。下面就介绍一些在哺乳期既能减肥又不影响母乳的食物,起到作用,还能维持动脉血管的健康和弹性。
欧洲产后瘦身师建议配合哺乳期减肥HICIBI:HICIBI分为4部曲,前三部同时将食物的生物分子分离,保留一切有营养的纤维、维生素、母乳所需的热量等等,阻断一切让产妇制胖的热量(淀粉、糖、油脂),最后一部曲,将释放500倍弹力纤维与胶原三肽,收紧松弛的腹部。

产后瘦肚子还会反弹吗?
关于胖你一定要记住:体重不能说明一切,水分的减少,少吃东西也可以减轻体重。减重真正使减脂,产后的女性更是如此,只有内脂减少,我们才算是真的瘦了。哪怕你的称重是100斤,看上去却只有90斤,那么你就成功了,除了增长肌肉以后我们就是消除脂肪,内脂和每天摄入的糖、淀粉、油脂等等。
关于脂肪你必须记住关键的两点,1, 你必须选对所摄入的脂肪种类,2你必须了解你吃下了多少脂肪,在脂肪摄入量这个问题上,对于哺乳期的妈妈,来说,获取脂肪中的营养,和阻断,脂肪的肥胖风险,到双管齐下,这才是王道,,相比较碳水化合物,脂肪的能量更为密集,一点点脂肪就能大显身手,所以哺乳期如果拥有太多,那就过犹不及了。
通过临床验证,每餐摄入HICIBI孕产期平衡营养群组,哺乳期女性每日顺畅通便,其次在她们的粪便中,发现包括了吸收食物中能量和营养后的食物残渣,还包括了消化液,消化酶中的**,大量的饱和脂肪酸,反式脂肪和胆固醇的代谢。这说明营养被吸收、热量被排泄。
HICIBI孕产期平衡营养群组系多家全球百强企业原料制造,出身名门,能在分子层级一一分解脂肪细胞,将其分解为水和油脂,随肠道排出体外!不仅能减肥,还能养颜嫩肤,使用该HICIBI孕产期平衡营养群组减肥成功后,能明显感觉到变年轻了,皮肤变好了。只需每天坚持三杯,10斤,20斤,30斤,40斤,瘦身节奏,完全由自己把控!
1.不要服用减肥药和减肥茶
这些减肥药物会通过乳汁排出,宝宝也就间接食用了药物成分。婴儿肝脏解毒能力差,大量的减肥药物会引起婴儿肝脏功能降低,从而导致肝功能异常。
2.不要剧烈运动
产后剧烈的运动很可能会干扰子宫的恢复,还会导致子宫下垂和肌肉韧带松弛。剧烈运动过后还会产生乳酸,喂食含有乳酸的乳汁不利于宝宝的健康成长。
3.不要疯狂节食
疯狂节食法对于产后女性来说是一种不可采取的方法,因为新妈妈要承担着哺育宝宝的重担,疯狂节食会让身体营养不足,从而让宝宝也深受其害,不能得到充足的营养补充,不利于宝宝的健康成长。
在哺乳期是不适合减肥和瘦身的,由于生产我们女性身体里的一些激素水平会上升,上升的激素,是我们产后发胖的根本,它是需要通过一些科技的方式让他轻松的面对,主要的方法就是HICIBI孕产期降脂平衡营养群组阻断多余的热量堆积成脂肪和适度锻炼,而如果这个期间控制饮食,就会导致乳汁分泌减少,这是不可避免的影响到孩子的。所以在哺乳期期间,饮食上不能刻意的控制,通过HICIBI孕产期平衡营养群组来阻断多余的热量堆积成脂肪,就能够起到控制体重和降低体重的作用。

CLR…WHO明确HICIBI针对肥胖问题修护国际减脂九项标准
CLR WHO(全称Cell lipid reduction细胞减脂)
1、燃烧内脂,溶解消化系统油脂率提升 18-20
2、减少皮脂,加快分解脂肪堆积13-15
3、降低血糖,修护血糖的动态平衡 12-15
4、降低血脂,抑制脂肪酶活性,修护血脂正常含量 10-12
5、预防反弹,收紧松弛脂肪细胞数量大小21-23
6、皮肤收紧,彻底改善肌肤失去弹性的根源 17-19
7、易瘦体质,调节消化酶打造易瘦体质 12-15
8、收缩胃肠组织容量,恢复消化系统过度扩张 9-11
9、食物热量阻断,避免脂肪堆积的源头 16-22
世卫组织表示,报告涵盖的所有国家和地区中,超重和肥胖人口“呈上升趋势”。世卫定义,身高体重指数超过30为肥胖。国际科学减脂研究院现有建议进行了补充,就如何改变肥胖问题和肥胖体质转换修护等问题提出了具体步骤。')\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "result = df_queries.apply(handle, axis=1)" + ], + "metadata": { + "id": "Dukd3NpYkWvh" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result_df = pd.DataFrame(result.tolist(), columns=['condition', 'anchor', 'pos', 'neg'])\n", + "result_df" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 597 + }, + "id": "4G-HkzOzknsE", + "outputId": "ca5cda8e-5a3f-4e67-9fd5-aa238e092e57" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " condition anchor \\\n", + "0 蜂巢取快递验证码摁错怎么办

【重新获取取件码】
1、首先来到丰巢快递柜前,点击屏幕上的【... \n", + "1 生产过后怎么还有一层肚子 女性生物孩子后很多人肚子会留下厚厚的一层赘肉,对于爱美的女人是不能接受的,那么要怎么做才能在... \n", + "2 大学怎么网上选宿舍 选的时候,一个班固定某几个宿舍,如果你认识的人和你不是一个班的,就不能选到一起。银行卡应该没... \n", + "3 怎么判断鱼卵是否活着 早上鱼产卵了,但大部分都被鱼吃了,只抢救出很小一部分,刚才,我拿放大镜观察了一... \n", + "4 胆碱重要吗 胆碱是一种人体必不可少的微量元素,各种体内重要生理功能和身体健康里都扮演着重要的角色。为了保... \n", + "... ... ... \n", + "5903 手机屏幕投屏到电视上怎么操作 方法一:
1.首先,将智能电视和苹果手机置于同一局域网内。
2.开启手机,手指从... \n", + "5904 圆台体积公式面积 如果圆台上、下底面半径分别为r、R,圆台高为h,圆台体积为V,那么
V=1/3×h×... \n", + "5905 婆婆性格特别强势,还很现实 从某些程度上来说,如果让儿媳一味地退让,一味地受委屈,那么这肯定也是让儿媳无法忍受的,从而导... \n", + "5906 竞价外包多少钱 
这个要看您账户大小,也就是消费情况,您账户一天消费几百,费用也就一两千,一天跑几万...   \n",
+              "5907        y=cosx图像  1.y=cosx画函数图
答:y=cosx,如图所示
2.y=-cosx的图像是...   \n",
+              "\n",
+              "                                                    pos  \\\n",
+              "0     
丰巢快递柜的取件码丢了、失效了、取件码错误,可以使用其他的方式进行取件,或...   \n",
+              "1     刚刚生完孩子的时候,孕妇妈妈们会遇上种种的烦心事。其中不乏坐月子摊上坏婆婆、产后开奶不及时、...   \n",
+              "2     \"2017级新生
网上选房
操作指南
网上选房时段
宿舍分配原则 有了这几种方法,手机投屏对你来说可能就简单多了,每种方法都要求设备有一定的借口或者...   \n",
+              "5904  \"圆台的体积公式: 
定义域为

圆台的高
...  \n",
+              "5905  问:因为疫情,我婆婆过来帮忙带孩子。但是因为过来后因为各种生活琐事,我们之间的矛盾越来越深。...  \n",
+              "5906                                 zuowendaQ⒐375837⒉⒉  \n",
+              "5907  题目

答案解析
查看更多优质解析
举报
函数y=c...  \n",
+              "\n",
+              "[5908 rows x 4 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
conditionanchorposneg
0蜂巢取快递验证码摁错怎么办<br><img><br>【重新获取取件码】<br>1、首先来到丰巢快递柜前,点击屏幕上的【...<img><br>丰巢快递柜的取件码丢了、失效了、取件码错误,可以使用其他的方式进行取件,或...找寄件人改号码
1生产过后怎么还有一层肚子女性生物孩子后很多人肚子会留下厚厚的一层赘肉,对于爱美的女人是不能接受的,那么要怎么做才能在...刚刚生完孩子的时候,孕妇妈妈们会遇上种种的烦心事。其中不乏坐月子摊上坏婆婆、产后开奶不及时、...让产后妈妈最烦恼的是什么?不用多想肯定是产后如何恢复肚子,都说生育是女人的分水岭一点也不错,...
2大学怎么网上选宿舍选的时候,一个班固定某几个宿舍,如果你认识的人和你不是一个班的,就不能选到一起。银行卡应该没...\"2017级新生<br>网上选房<br>操作指南<br>网上选房时段<br>宿舍分配原则<b...出国留学高考网为大家提供武汉多所高校开通了网上自主选寝室系统,更多高考资讯请关注我们网站的更...
3怎么判断鱼卵是否活着<table>早上鱼产卵了,但大部分都被鱼吃了,只抢救出很小一部分,刚才,我拿放大镜观察了一...早上产的卵,晚上就看见黑点,是不可能的,起码也得第二天,气温在25度左右,当天晚上可看见受精...哇这也有问着问题的。我答:“鱼卵可以在土里存活30~40年。
4胆碱重要吗胆碱是一种人体必不可少的微量元素,各种体内重要生理功能和身体健康里都扮演着重要的角色。为了保...胆碱的确对大脑有一定作用。胆碱广泛存在于各种食物中,它在食物中主要以卵磷脂的形式存在于各类食...磷脂水解的产物包括二脂酰甘油,其本身即是一种信使分子,又是 脂质代谢的中介物。正常情况下,蛋...
...............
5903手机屏幕投屏到电视上怎么操作方法一:<br>1.首先,将智能电视和苹果手机置于同一局域网内。<br>2.开启手机,手指从...<br> 有了这几种方法,手机投屏对你来说可能就简单多了,每种方法都要求设备有一定的借口或者...手机投屏分为三种情况:1、手机自带无线投屏功能,从手机设置中找到并连接;2、APP投屏,手机...
5904圆台体积公式面积如果圆台上、下底面半径分别为r、R,圆台高为h,圆台体积为V,那么 <br>V=1/3×h×...\"圆台的体积公式: <br><img src=\"\"https://pic.wenwen.so...设圆台的母线的直线方程为<br><img>定义域为<br><img><br>圆台的高<br>...
5905婆婆性格特别强势,还很现实从某些程度上来说,如果让儿媳一味地退让,一味地受委屈,那么这肯定也是让儿媳无法忍受的,从而导...我都快烦死我婆婆了!天天非得显摆自己知识有多好,天天给人讲那些历史,还说孩子出生后她来带,一...问:因为疫情,我婆婆过来帮忙带孩子。但是因为过来后因为各种生活琐事,我们之间的矛盾越来越深。...
5906竞价外包多少钱<pre>这个要看您账户大小,也就是消费情况,您账户一天消费几百,费用也就一两千,一天跑几万...在为关键词和其所在的推广单元同时设定出价的情况下,以关键词的出价为准。请注意,为关键词单独出...zuowendaQ⒐375837⒉⒉
5907y=cosx图像1.y=cosx画函数图<br>答:y=cosx,如图所示<br>2.y=-cosx的图像是...\"y=cosx为余弦函数,根据函数作图方法:列表取值,描点连线,利用五点法,我们可以画出余弦...题目<br><img><br>答案解析<br>查看更多优质解析<br>举报<br>函数y=c...
\n", + "

5908 rows × 4 columns

\n", + "\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "
\n", + " \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "result_df", + "summary": "{\n \"name\": \"result_df\",\n \"rows\": 5908,\n \"fields\": [\n {\n \"column\": \"condition\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5908,\n \"samples\": [\n \"\\u8089\\u6587\\u7684\\u63cf\\u5199\\u6709\\u54ea\\u4e9b\",\n \"\\u5f39\\u4e38\\u8bba\\u7834 \\u83b2\\u84ec\\u5934\\u4ee5\\u5916\\u7684\\u4e1c\\u897f\",\n \"\\u7ed2\\u5154\\u661f\\u7403\\u4ec0\\u4e48\\u610f\\u601d\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"anchor\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5903,\n \"samples\": [\n \"\\u53ef\\u4ee5\\u683d\\u95e8\\u65c1,\\u91d1\\u94f6\\u82b1\\u662f\\u4e00\\u79cd\\u7f8e\\u4e3d\\u7684\\u89c2\\u8d4f\\u82b1\\u5349,\\u5c0f\\u82b1\\u6735\\u6735,\\u9ec4\\u767d\\u4e24\\u8272\\u4ea4\\u9519,\\u76db\\u590f\\u5728\\u91d1\\u94f6\\u82b1\\u68da\\u67b6\\u4e0b\\u4e58\\u51c9,\\u8d4f\\u82b1\\u95fb\\u9999,\\u8da3\\u5473\\u65e0\\u7a77\\u3002\\u91d1\\u94f6\\u82b1\\u540d\\u79f0\\u597d,\\u6709\\u91d1\\u6709\\u94f6\\u6709\\u82b1,\\u662f\\u5bcc\\u88d5\\u5e78\\u798f\\u7684\\u8c61\\u5f81,\\u5c01\\u4e18\\u6749\\u6cc9\\u91d1\\u94f6\\u82b1,\",\n \"\\u4f24\\u53e3\\u7f1d\\u9488\\u4e86\\u4ee5\\u540e\\u53ef\\u4ee5\\u5403\\u9e2d\\u8089,\\u4f46\\u662f\\u8981\\u7ed3\\u5408\\u4f24\\u53e3\\u7684\\u5177\\u4f53\\u60c5\\u51b5\\u3002\\u5982\\u679c\\u53ea\\u662f\\u76ae\\u5916\\u4f24,\\u5728\\u4f24\\u53e3\\u6e05\\u521b\\u7f1d\\u5408\\u4ee5\\u540e,\\u5403\\u9e2d\\u8089\\u662f\\u6ca1\\u6709\\u5f71\\u54cd\\u7684,\\u4e0d\\u4f1a\\u5f71\\u54cd\\u5230\\u4f24\\u53e3\\u6108\\u5408\\u3002\\u4f46\\u662f\\u5982\\u679c\\u662f\\u5728\\u8179\\u90e8\\u505a\\u624b\\u672f,\\u6bd4\\u5982\\u80c3\\u80a0\\u9053\\u624b\\u672f\\u6216\\u8005\\u505a\\u9611\\u5c3e\\u624b\\u672f\\u7b49\\u3002\\u5728\\u624b\\u672f\\u6cbb\\u7597\\u4ee5\\u540e,\\u4e0d\\u4ec5\\u662f\\u8981\\u5173\\u6ce8\\u4f24\\u53e3\\u6108\\u5408\\u7684\\u60c5\\u51b5,\\u8fd8\\u9700\\u8981\\u4e86\\u89e3\\u8179\\u8154\\u5185\\u90e8\\u7684\\u60c5\\u51b5,\\u6bd4\\u5982\\u80c3\\u80a0\\u9053\\u7684\\u6062\\u590d\\u60c5\\u51b5,\\u4ee5\\u53ca\\u5728\\u624b\\u672f\\u7684\\u90e8\\u4f4d\\u662f\\u5426\\u5b8c\\u5168\\u5730\\u6108\\u5408\\u3002\\u4e00\\u822c\\u8981\\u7b49\\u5230\\u624b\\u672f\\u7684\\u90e8\\u4f4d\\u5b8c\\u5168\\u6108\\u5408,\\u80c3\\u80a0\\u9053\\u529f\\u80fd\\u6062\\u590d\\u6b63\\u5e38,\\u901a\\u5e38\\u662f\\u6ca1\\u6709\\u8179\\u75db\\u3001\\u8179\\u80c0\\u3001\\u809b\\u95e8\\u6392\\u6c14\\u3001\\u6392\\u4fbf\\u6b63\\u5e38\\u7684\\u60c5\\u51b5\\u4e0b,\\u624d\\u80fd\\u591f\\u5403\\u4e00\\u4e9b\\u56fa\\u4f53\\u7684\\u98df\\u7269,\\u6bd4\\u5982\\u9e2d\\u8089\\u4e4b\\u7c7b\\u7684\\u98df\\u7269\\u7b49\\u3002\",\n \"\\u3010www.quanqiunao.cn--\\u7b49\\u7ea7\\u8003\\u8bd5\\u3011
\\u5976\\u5757\\u5c04\\u624b\\u5957\\u88c5\\u600e\\u4e48\\u83b7\\u53d6?\\u76ee\\u524d\\u5976\\u5757\\u5171\\u5f00\\u653e\\u4e09\\u7cfb\\u4e09\\u4e2a\\u7b49\\u7ea7\\u7684\\u5957\\u88c5 ,\\u90a3\\u5c04\\u624b\\u5404\\u7c7b\\u5957\\u88c5\\u5206\\u522b\\u8981\\u5565\\u6750\\u6599\\u5462?\\u76f8\\u4fe1\\u5c0f\\u4f19\\u4f34\\u90fd\\u8fd8\\u4e0d\\u662f\\u5f88\\u6e05\\u695a ,\\u63a5\\u4e0b\\u6765\\u897f\\u897f\\u5c31\\u7ed9\\u5927\\u5bb6\\u5e26\\u6765\\u4e86\\u5976\\u5757\\u5c04\\u624b\\u4e0d\\u540c\\u7b49\\u7ea7\\u5957\\u88c5\\u83b7\\u53d6\\u653b\\u7565\\u3002
T1\\u5957\\u88c5
\\u5c04\\u624b - \\u76ae\\u8d28\\u5957\\u88c5
\\u76ae\\u8d28\\u5934\\u9970:\\u76ae\\u9769*5
\\u76ae\\u8d28\\u62a4\\u80f8:\\u76ae\\u9769*8
\\u76ae\\u8d28\\u62a4\\u817f:\\u76ae\\u9769*7
\\u76ae\\u8d28\\u4fbf\\u978b:\\u76ae\\u9769*4

T2\\u5957\\u88c5
\\u5c04\\u624b - \\u72e9\\u730e\\u5957\\u88c5
\\u72e9\\u730e\\u5934\\u9970:\\u72c2\\u91ce\\u517d\\u76ae*5
\\u72e9\\u730e\\u62a4\\u80f8:\\u72c2\\u91ce\\u517d\\u76ae*8
\\u72e9\\u730e\\u62a4\\u817f:\\u72c2\\u91ce\\u517d\\u76ae*7
\\u72c2\\u91ce\\u4fbf\\u978b:\\u72c2\\u91ce\\u517d\\u76ae*4
\\u72e9\\u730e\\u7bad\\u888b:\\u72c2\\u91ce\\u517d\\u76ae*3
\\u72e9\\u730e\\u957f\\u5f13:\\u9b54\\u94c1\\u952d*3+\\u72c2\\u91ce\\u517d\\u76ae*3
\\u72c2\\u91ce\\u517d\\u76ae\\u5408\\u6210\\u6750\\u6599
\\u76ae\\u9769*4+\\u72c2\\u91ce\\u517d\\u8840*4+\\u94bb\\u77f3*1

T3\\u5957\\u88c5
\\u5c04\\u624b - \\u6c38\\u6052\\u68a6\\u9b47\\u5957\\u88c5
\\u5934\\u76d4:\\u6c38\\u6052\\u68a6\\u9b47\\u9762\\u7f69 = \\u534e\\u4e3d\\u76ae\\u9769 * 5
\\u8863\\u670d:\\u6c38\\u6052\\u68a6\\u9b47\\u80f8\\u7532 = \\u534e\\u4e3d\\u76ae\\u9769 * 8
\\u88e4\\u5b50:\\u6c38\\u6052\\u68a6\\u9b47\\u62a4\\u817f = \\u534e\\u4e3d\\u76ae\\u9769 * 7
\\u978b\\u5b50:\\u6c38\\u6052\\u68a6\\u9b47\\u4fbf\\u978b = \\u534e\\u4e3d\\u76ae\\u9769 * 4
\\u6b66\\u5668:\\u6c38\\u6052\\u68a6\\u9b47\\u730e\\u5f13 = \\u771f\\u7406\\u4e4b\\u5f13 + \\u534e\\u4e3d\\u76ae\\u9769 * 3 + \\u6e90\\u8d28\\u952d * 3
\\u526f\\u624b:\\u6c38\\u6052\\u68a6\\u9b47\\u7bad\\u888b = \\u534e\\u4e3d\\u76ae\\u9769 * 3
\\u534e\\u4e3d\\u76ae\\u9769\\u5408\\u6210\\u6750\\u6599?
\\u72c2\\u91ce\\u517d\\u76ae * 1 + \\u7cbe\\u81f4\\u76ae\\u9769 * 6 + \\u68a6\\u9b47\\u836f\\u6c34 * 2

\\u7279\\u522b\\u6ce8\\u610f
T3\\u5957\\u88c5\\u662f\\u9700\\u8981\\u89d2\\u8272\\u8fbe\\u5230\\u4e00\\u5b9a\\u58f0\\u671b\\u503c
\\u624d\\u53ef\\u627e\\u804c\\u4e1a\\u5bfc\\u5e08\\u89e3\\u9501\\u5957\\u88c5\\u90e8\\u4f4d\\u7684\\u88c5\\u5907\\u6743\\u9650\\u7684\\u54df ,\\u5149\\u6709\\u88c5\\u5907\\u8fd8\\u4e0d\\u591f\\u7684
\\u4e00\\u65e6\\u89e3\\u9501\\u67d0\\u5957\\u88c5\\u7684\\u90e8\\u4f4d ,\\u4fbf\\u53ef\\u4ee5\\u6c38\\u4e45\\u62e5\\u6709\\u8be5\\u90e8\\u4f4d\\u7684\\u88c5\\u5907\\u6743\\u5566
\\u600e\\u6837\\u624d\\u80fd\\u63d0\\u5347\\u58f0\\u671b\\u503c\\u5462?\\u9014\\u5f84\\u5f88\\u7b80\\u5355 ,\\u53ea\\u8981\\u901a\\u8fc7\\u6bcf\\u65e5\\u6210\\u5c31\\u4fbf\\u53ef\\u4ee5\\u83b7\\u5f97\\u5566!

\\u8fc5\\u96f7\\u6240\\u6709\\u7248\\u672c\\u90fd\\u65e0\\u6cd5\\u4e0b\\u8f7d\\u600e\\u4e48\\u529e?\\u8fc5\\u96f7\\u6240\\u6709\\u4e0b\\u8f7d\\u4efb\\u52a1\\u663e\\u793a\\u6d89\\u5acc\\u8fdd\\u89c4\\u600e\\u4e48\\u56de\\u4e8b?\\u4ece\\u524d\\u4e24\\u65e5\\u5f00\\u59cb,\\u8fc5\\u96f7\\u7528\\u6237\\u90fd\\u5728\\u8d34\\u5427\\u3001\\u8bba\\u575b\\u4e0a\\u5410\\u69fd\\u6240\\u6709\\u7684\\u4e0b\\u8f7d\\u4efb\\u52a1\\u90fd\\u65e0\\u6cd5\\u6b63\\u5e38\\u4e0b\\u8f7d,\\u663e\\u793a\\u4efb\\u52a1\\u5305\\u542b\\u8fdd\\u89c4\\u5185\\u5bb9,\\u65e0\\u6cd5\\u7ee7\\u7eed\\u4e0b\\u8f7d\\u7684\\u63d0\\u793a,\\u8fd9\\u662f\\u600e\\u4e48\\u56de\\u4e8b\\u5462?\\u4e00[db:cate]

\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pos\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5687,\n \"samples\": [\n \"\\u90d1\\u4f2f\\u514b\\u6bb5\\u4e8e\\u9122
\\u3010\\u539f\\u6587\\u3011
\\u521d\\u2460,\\u90d1\\u6b66\\u516c\\u5a36\\u4e8e\\u7533\\u2461 ,\\u65e5\\u6b66\\u59dc\\u2462\\u3002\\u751f\\u5e84\\u516c\\u53ca\\u5171\\u53d4\\u6bb5\\u2463\\u3002\\u5e84\\u516c\\u5be4\\u751f\\u2464,\\u60ca\\u59dc\\u6c0f,\\u6545\\u540d\\u66f0\\u201c\\u5be4\\u751f\\u201d,\\u9042\\u6076\\u4e4b\\u2465\\u3002\\u7231\\u5171\\u53d4\\u6bb5,\\u6b32\\u7acb\\u4e4b,\\u4e9f \\u8bf7\\u4e8e\\u6b66\\u516c\\u2466,\\u516c\\u5f17\\u8bb8\\u3002\\u53ca\\u5e84\\u516c\\u5373\\u4f4d,\\u4e3a\\u4e4b\\u8bf7\\u5236\\u2467\\u3002\\u516c\\u66f0:\\u201c\\u5236,\\u5ca9\\u9091 \\u4e5f\\u2468, \\u8662\\u53d4\\u6b7b\\u7109\\u2469,\\u4f26\\u9091\\u552f\\u547d\\u247e\\u3002\\u201d\\u8bf7\\u4eac\\u247f,\\u4f7f\\u5c45\\u4e4b,\\u8c13\\u4e4b\\u201c\\u4eac\\u57ce\\u5927\\u53d4\\u201d\\u3002
\\u796d\\u4ef2\\u66f0\\u2480:\\u201c\\u90fd,\\u57ce\\u8fc7\\u767e\\u96c9\\u2481,\\u56fd\\u4e4b\\u5bb3\\u4e5f\\u3002\\u5148\\u738b\\u4e4b\\u5236:\\u5927\\u90fd,\\u4e0d\\u8fc7\\u53c2\\u56fd\\u4e4b\\u4e00\\u2482;\\u4e2d,\\u4e94\\u4e4b\\u4e00;\\u5c0f,\\u4e5d\\u4e4b\\u4e00\\u3002\\u4eca\\u4eac\\u4e0d\\u5ea6,\\u975e\\u5236\\u4e5f,\\u541b \\u5c06\\u4e0d\\u582a\\u2483\\u3002\\u201d\\u516c\\u66f0:\\u201c\\u59dc\\u6c0f\\u6b32\\u4e4b,\\u7109\\u8f9f\\u5bb3\\u2484?\\u201d\\u5bf9\\u66f0:\\u201c\\u59dc\\u6c0f\\u4f55\\u538c\\u4e4b\\u6709\\u2485? \\u4e0d\\u5982\\u65e9\\u4e3a\\u4e4b\\u6240\\u2486,\\u65e0\\u4f7f\\u6ecb\\u8513\\u3002\\u8513,\\u96be\\u56fe\\u4e5f\\u2487\\u201d\\u3002\\u8513\\u8349\\u72b9\\u4e0d\\u53ef\\u9664,\\u51b5\\u541b\\u4e4b\\u5ba0\\u5f1f\\u4e4e?\\u201d\\u516c\\u66f0:\\u201c\\u591a\\u884c\\u4e0d\\u4e49,\\u5fc5\\u81ea\\u6bd9(21),\\u5b50\\u59d1\\u5f85\\u4e4b\\u3002\\u201d
\\u65e2\\u800c\\u5927\\u53d4\\u547d\\u897f\\u9119\\u3001\\u5317\\u9119\\u8d30\\u4e8e\\u5df1(22)\\u3002\\u516c\\u4e8e\\u5415\\u66f0(23):\\u201c\\u56fd\\u4e0d\\u582a\\u8d30,\\u541b \\u5c06\\u82e5\\u4e4b\\u4f55(24)\\u3002?\\u6b32\\u4e0e\\u5927\\u53d4,\\u81e3\\u8bf7\\u4e8b\\u4e4b;\\u82e5\\u5f17\\u4e0e,\\u5219\\u8bf7\\u9664\\u4e4b,\\u65e0\\u751f\\u6c11\\u5fc3\\u3002\\u201d \\u516c\\u66f0:\\u201c\\u65e0\\u5eb8(25),\\u5c06\\u81ea\\u53ca\\u3002\\u201d\\u5927\\u53d4\\u53c8\\u6536\\u8d30\\u4ee5\\u4e3a\\u5df1\\u9091,\\u81f3\\u4e8e\\u5eea\\u5ef6(26)\\u3002\\u5b50 \\u5c01\\u66f0:\\u201c\\u53ef\\u77e3\\u3002\\u539a\\u5c06\\u5f97\\u4f17\\u3002\\u201d\\u516c\\u66f0:\\u201c\\u4e0d\\u4e49\\u4e0d\\u6635(27)\\u201d,\\u539a\\u5c06\\u5d29\\u3002\\u201d
\\u5927\\u53d4\\u5b8c\\u805a(28),\\u7f2e\\u7532\\u5175,\\u5177\\u5352\\u4e58(29),\\u5c06\\u88ad\\u90d1\\u3002\\u592b\\u4eba\\u5c06\\u542f\\u4e4b(30)\\u3002\\u516c\\u95fb\\u5176\\u671f,\\u66f0:\\u201c\\u53ef\\u77e3!\\u201d\\u547d\\u5b50\\u5c01\\u5e05\\u8f66\\u4e8c\\u767e\\u4e58\\u4ee5\\u4f10\\u4eac(31)\\u3002\\u4eac\\u53db\\u5927\\u53d4\\u6bb5\\u3002\\u6bb5 \\u4eba\\u4e8e\\u9122\\u201c\\u3002\\u516c\\u4f10\\u8bf8\\u9122\\u3002\\u4e94\\u6708\\u8f9b\\u4e1132),\\u5927\\u53d4\\u51fa\\u5954\\u5171\\u3002
\\u9042\\u7f6e\\u59dc\\u6c0f\\u4e8e\\u57ce\\u988d(34),\\u800c\\u8a93\\u4e4b\\u65e5:\\u201c\\u4e0d\\u53ca\\u9ec4\\u6cc9,\\u65e0\\u76f8\\u89c1\\u4e5f(35)\\u3002\\u201d\\u65e2\\u800c\\u6094\\u4e4b\\u3002
\\u988d\\u8003\\u53d4\\u4e3a\\u988d\\u8c37\\u5c01\\u4eba(36),\\u95fb\\u4e4b,\\u6709\\u732e\\u4e8e\\u516c\\u3002\\u516c\\u8d50\\u4e4b\\u98df\\u3002\\u98df\\u820d\\u8089(37)\\u3002\\u516c\\u95ee\\u4e4b,\\u5bf9\\u66f0:\\u201c\\u5c0f\\u4eba\\u6709\\u6bcd,\\u7686\\u5c1d\\u541b\\u4e4b\\u7fb2(38)\\u3002\\u8bf7\\u4ee5\\u9057\\u4e4b(39)\\u3002\\u201d\\u516c\\u66f0:\\u201c\\u5c14\\u6709\\u6bcd\\u9057,\\u7e44\\u6211\\u72ec\\u65e0(40)!\\u201d\\u9896\\u8003\\u53d4\\u66f0:\\u201c\\u6562\\u95ee\\u4f55\\u8c13\\u4e5f?\\u201d\\u516c\\u8bed\\u4e4b\\u6545,\\u4e14\\u544a\\u4e4b\\u6094\\u3002\\u5bf9\\u66f0:\\u201c\\u541b\\u4f55\\u60a3\\u7109?\\u82e5\\u95d5\\u5730\\u53ca\\u6cc9(41),\\u9042\\u800c\\u76f8\\u89c1(42),\\u5176\\u8c01\\u66f0\\u4e0d\\u7136?\\u201d\\u516c\\u4ece\\u4e4b\\u3002\\u516c\\u5165\\u800c\\u8d4b(43):\\u201c\\u5927\\u96a7\\u4e4b\\u4e2d,\\u5176\\u4e50\\u4e5f\\u878d\\u878d(44)!\\u201d\\u59dc\\u51fa\\u800c\\u8d4b:\\u201c\\u5927\\u96a7\\u4e4b\\u5916,\\u5176\\u4e50\\u4e5f\\u6cc4\\u6cc4(45)!\\u201d\\u9042\\u4e3a\\u6bcd\\u5b50\\u5982\\u521d\\u3002
\\u541b\\u5b50\\u66f0(46):\\u201c\\u9896\\u8003\\u53d4,\\u7eaf\\u5b5d\\u4e5f\\u3002\\u7231\\u5176\\u6bcd,\\u65bd\\u53ca\\u5e84\\u516c(47)\\u3002\\u300a\\u8bd7\\u300b\\u66f0:\\u2018\\u5b5d\\u5b50\\u4e0d\\u532e,\\u6c38\\u9521\\u5c14\\u7c7b(48)\\u3002\\u2019\\u5176\\u662f\\u4e4b\\u8c13\\u4e4e?\\u201d
\\u3010\\u6ce8\\u91ca\\u3011
(1)\\u521d:\\u5f53\\u521d,\\u4ece\\u524d\\u3002\\u6545\\u4e8b\\u5f00\\u5934\\u65f6\\u7528\\u8bed\\u3002(2)\\u90d1\\u6b66\\u516c:\\u6625\\u79cb\\u65f6\\u8bf8\\u4faf\\u56fd\\u90d1\\u56fd(\\u5728\\u4eca\\u6cb3\\u5357\\u65b0\\u90d1)\\u56fd\\u541b,\\u59d3\\u59ec,\\u540d\\u6398\\u7a81,\\u6b66\\u4e3a\\u8c25\\u53f7\\u3002\\u7533:\\u8bf8\\u4faf\\u56fd\\u540d,\\u5728\\u4eca\\u6cb3\\u5357\\u5357\\u9633,\\u59dc\\u59d3\\u3002(3)\\u6b66\\u59dc:\\u6b66\\u8c25\\u90d1\\u6b66\\u516c\\u8c25\\u53f7,\\u59dc\\u8c25\\u5a18\\u5bb6\\u59d3\\u3002(4)\\u5e84\\u516c:\\u5373\\u90d1\\u5e84\\u516c\\u3002\\u5171(g\\u014dng)\\u53d4\\u6bb5:\\u5171\\u662f\\u56fd\\u540d,\\u53d4\\u4e3a\\u5144\\u5f1f\\u6392\\u884c\\u5c45\\u540e,\\u6bb5\\u662f\\u540d\\u3002(5)\\u7ab9(w\\u00f9)\\u751f:\\u9006\\u751f,\\u5012\\u751f,\\u5373\\u96be\\u4ea7\\u3002(6)\\u6076(w\\u00f9):\\u4e0d\\u559c\\u6b22\\u3002(7)\\u4e9f(q\\u00ec):\\u591a\\u6b21\\u5c61\\u6b21\\u3002(8)\\u5236:\\u90d1\\u56fd\\u9091\\u540d,\\u5728\\u4eca\\u6cb3\\u5357\\u8365\\u9633\\u53bf\\u864e\\u7262\\u5173\\u3002(9)\\u5ca9\\u9091:\\u9669\\u8981\\u5730\\u57ce\\u9091\\u3002(10)\\u8662(gu\\u00f3)\\u53d4:\\u4e1c\\u8662\\u56fd\\u56fd\\u541b\\u3002(11)\\u4f57:\\u540c\\u201c\\u4ed6\\u201d\\u3002\\u552f\\u547d:\\u201c\\u552f\\u547d\\u662f\\u4ece\\u201d\\u5730\\u7701\\u7565\\u3002(12)\\u4eac:\\u90d1\\u56fd\\u9091\\u540d,\\u5728\\u4eca\\u6cb3\\u5357\\u8365\\u9633\\u53bf\\u4e1c\\u5357\\u3002(13)\\u796d(zh\\u00e0i)\\u4ef2:\\u90d1\\u56fd\\u5927\\u592b,\\u5b57\\u8db3\\u3002(14)\\u96c9:\\u53e4\\u65f6\\u5efa\\u7b51\\u8ba1\\u91cf\\u5355\\u4f4d,\\u957f\\u4e09\\u4e08,\\u9ad8\\u4e00\\u4e08\\u3002(15)\\u53c2:\\u540c\\u201c\\u4e09\\u201d\\u3002\\u56fd:\\u56fd\\u90fd\\u3002(16)\\u582a:\\u7ecf\\u53d7\\u5f97\\u8d77\\u3002(17)\\u7109:\\u54ea\\u91cc\\u3002\\u8f9f:\\u540c\\u201c\\u907f\\u201d\\u3002(18)\\u4f55\\u538c\\u4e4b\\u6709:\\u6709\\u4f55\\u538c\\u3002\\u538c:\\u6ee1\\u8db3\\u3002(19)\\u6240:\\u5b89\\u7f6e,\\u5904\\u7406\\u3002(20)\\u56fe:\\u8bfe,\\u6cbb\\u3002(21)\\u6bd9:\\u4ec6\\u5012,\\u5012\\u4e0b\\u53bb\\u3002(22)\\u9119:\\u8fb9\\u5883\\u4e0a\\u5f97\\u9091\\u3002\\u8d30\\u4e8e\\u5df1:\\u540c\\u65f6\\u5c5e\\u4e8e\\u5e84\\u516c\\u548c\\u81ea\\u5df1\\u3002(23)\\u516c\\u5b50\\u5415:\\u90d1\\u56fd\\u5927\\u592b,\\u5b57\\u5b50\\u5c01\\u3002(24)\\u82e5\\u4e4b\\u4f55:\\u5bf9\\u4ed6\\u600e\\u4e48\\u529e\\u3002(25)\\u5eb8:\\u7528\\u3002(26)\\u5eea\\u5ef6:\\u90d1\\u56fd\\u9091\\u540d,\\u5728\\u4eca\\u6cb3\\u5357\\u5ef6\\u6d25\\u5317\\u3002(27)\\u6635:\\u4eb2\\u8fd1\\u3002(28)\\u5b8c:\\u4fee\\u7f2e\\u3002\\u805a:\\u79ef\\u805a\\u3002(29)\\u7f2e:\\u4fee\\u6574\\u3002\\u7532:\\u94e0\\u7532\\u3002\\u5175:\\u6b66\\u5668\\u3002\\u5177:\\u5907\\u9f50\\u3002\\u5352:\\u6b65\\u5175\\u3002\\u4e58(sh\\u00e8ng):\\u5175\\u8f66\\u3002(30)\\u592b\\u4eba:\\u6307\\u6b66\\u59dc\\u3002\\u542f\\u4e4b:\\u4e3a\\u4ed6\\u6253\\u5f00\\u57ce\\u95e8\\u3002(31)\\u5e05:\\u7387\\u9886\\u3002\\u4e58:\\u4e00\\u8f66\\u56db\\u9a6c\\u4e3a\\u4e00\\u4e58\\u3002\\u8f66\\u4e00\\u4e58\\u914d\\u7532\\u58eb\\u4e09\\u4eba,\\u6b65\\u5352\\u4e03\\u5341\\u4e8c\\u4eba\\u3002(32)\\u9122:\\u90d1\\u56fd\\u9091\\u540d,\\u5728\\u9675\\u5883\\u5185.(33)\\u4e94\\u6708\\u8f9b\\u4e11:\\u4e94\\u6708\\u4e8c\\u5341\\u4e09\\u65e5.\\u53e4\\u4eba\\u8bb0\\u65e5\\u7528\\u5929\\u5e72\\u548c\\u5730\\u652f\\u642d\\u914d.(34)\\u57ce\\u9896\\u897f\\u5317\\u3002(35)\\u9ec4\\u6cc9:\\u9ec4\\u571f\\u4e0b\\u7684\\u6cc9\\u6c34\\u3002\\u8fd9\\u91cc\\u6307\\u5893\\u7a74\\u3002(36)\\u9896\\u8003\\u53d4:\\u90d1\\u56fd\\u5927\\u592b\\u3002\\u9896\\u8c37:\\u90d1\\u56fd\\u9091\\u540d,\\u5728\\u4eca\\u6cb3\\u5357\\u767b\\u5c01\\u897f\\u5357\\u3002\\u5c01\\u4eba:\\u7ba1\\u7406\\u8fb9\\u754c\\u7684\\u5b98\\u3002(37)\\u820d\\u8089:\\u628a\\u8089\\u653e\\u5728\\u65c1\\u8fb9\\u4e0d\\u5403\\u3002(38)\\u7fb2:\\u8c03\\u548c\\u4e94\\u5473\\u505a\\u6210\\u7684\\u5e26\\u6c41\\u7684\\u8089\\u3002(39)\\u9057(w\\u00e9i):\\u8d60\\u9001\\u3002(40)\\u7e44(y\\u00ec):\\u8bed\\u6c14\\u52a9\\u8bcd\\u3002\\u6ca1\\u6709\\u5b9e\\u4e49\\u3002(41)\\u95d5:\\u540c\\u201c\\u6398\\u201d,\\u6316\\u3002(42)\\u96a7:\\u5730\\u9053\\u3002\\u8fd9\\u91cc\\u7684\\u610f\\u601d\\u662f\\u6316\\u96a7\\u9053\\u3002(43)\\u8d4b:\\u6307\\u4f5c\\u8bd7\\u3002(44)\\u878d\\u878d:\\u5feb\\u4e50\\u81ea\\u5f97\\u7684\\u6837\\u5b50\\u3002(45)\\u6cc4\\u6cc4(y\\u00ec):\\u5feb\\u4e50\\u8212\\u7545\\u7684\\u6837\\u5b50\\u3002(46)\\u541b\\u5b50:\\u4f5c\\u8005\\u5730\\u6258\\u3002\\u300a\\u5de6\\u4f20\\u300b\\u4f5c\\u8005\\u5e38\\u7528\\u8fd9\\u79cd\\u65b9\\u5f0f\\u53d1\\u8868\\u8bc4\\u8bba\\u3002(47)\\u65bd(y\\u00ec):\\u5ef6\\u53ca,\\u6269\\u5c55\\u3002(48)\\u8fd9\\u4e24\\u53e5\\u8bd7\\u51fa\\u81ea\\u300a\\u8bd7\\u00b7\\u5927\\u96c5\\u00b7\\u65e2\\u9189\\u300b\\u3002\\u532e:\\u7a77\\u5c3d\\u3002\\u9521:\\u540c\\u201c\\u8d50\\u201d,\\u7ed9\\u4e88\\u3002\",\n \"

\\u6700\\u65b0\\u56de\\u7b54


\\u7a97\\u6237\\u4f5c\\u7528\\u91cd\\u8981,\\u7a97\\u6237\\u5b89\\u88c5\\u4e5f\\u5c31\\u4e0d\\u80fd\\u9a6c\\u864e;\\u7a97\\u6237\\u9ad8\\u5ea6\\u89c4\\u8303\\u53ca\\u4e00\\u822c\\u7a97\\u6237\\u7684\\u5c3a\\u5bf8\\u90fd\\u662f\\u5fc5\\u987b\\u8981\\u6ce8\\u610f\\u5230\\u7684;\\u6309\\u4e00\\u822c\\u7a97\\u6237\\u7684\\u5c3a\\u5bf8\\u6765\\u8bbe\\u8ba1\\u7a97\\u6237\\u7684\\u5927\\u5c0f,\\u8981\\u5408\\u7406\\u9002\\u5b9c,\\u80fd\\u591f\\u7ed9\\u623f\\u95f4\\u5e26\\u6765\\u6bd4\\u8f83\\u597d\\u7684\\u6c1b\\u56f4,\\u4e00\\u8d77\\u6765\\u4e86\\u89e3\\u4e00\\u822c\\u7a97\\u6237\\u9ad8\\u5ea6\\u662f\\u591a\\u5c11\\u5427\\u3002
\\u4e00\\u3001\\u7a97\\u6237\\u9ad8\\u5ea6\\u4e00\\u822c\\u662f\\u591a\\u5c11
\\u4e00\\u822c\\u7684\\u5efa\\u7b51\\u4f4f\\u5b85\\u4e2d,\\u7a97\\u7684\\u9ad8\\u7ea6\\u4e3a1.49\\u7c73,\\u7a97\\u53f0\\u9ad8\\u5ea6\\u7ea6\\u4e3a0.85\\u7c73,\\u7a97\\u9876\\u8ddd\\u79bb\\u697c\\u9762\\u9ad8\\u5ea6\\u7ea6\\u4e3a2.4\\u7c73\\u3002\\u82e5\\u662f\\u7a97\\u53f0\\u9ad8\\u5ea6\\u5c11\\u4e8e0.795\\u7c73\\u65f6,\\u8981\\u8c28\\u614e\\u91c7\\u53d6\\u5468\\u5bc6\\u7684\\u9632\\u62a4\\u63aa\\u65bd\\u3002\\u7a97\\u6237\\u7684\\u5bbd\\u5ea6\\u901a\\u5e38\\u75310.6\\u7c73\\u5f00\\u59cb,\\u5bbd\\u7684\\u6807\\u51c6\\u662f\\u80fd\\u5448\\u73b0\\u201c\\u5e26\\u7a97\\u201d,\\u91c7\\u7528\\u901a\\u5bbd\\u7684\\u5e26\\u7a97\\u5de6\\u53f3\\u76f8\\u90bb\\u623f\\u95f4\\u7684\\u9694\\u79bb\\u58f0\\u97f3\\u7684\\u95ee\\u9898,\\u82e5\\u662f\\u91c7\\u7528\\u63a8\\u62c9\\u7a97\\u5219\\u8981\\u8003\\u8651\\u7a97\\u6247\\u7684\\u6ed1\\u52a8\\u8303\\u56f4\\u3002
\\u5ba2\\u5385\\u7684\\u7a97\\u6237\\u9ad8\\u5ea6\\u4e5f\\u662f\\u4e0d\\u56fa\\u5b9a\\u7684,\\u5982\\u679c\\u662f\\u843d\\u5730\\u7a97\\u5dee\\u522b\\u5c31\\u4f1a\\u504f\\u5927,\\u901a\\u5e38\\u4f4f\\u5b85\\u7a97\\u53f0\\u5927\\u7ea6\\u90fd\\u662f\\u88c5\\u4fee\\u597d90cm\\u9ad8,\\u7a97\\u662f144cm-154cm\\u4e4b\\u95f4,\\u800c\\u843d\\u5730\\u7a97\\u4f4d\\u7f6e\\u7684\\u7a97\\u53f0\\u5c31\\u8981\\u6bd4\\u3002
3\\u5929\\u524d

\\u4e00\\u822c\\u7a97\\u6237\\u9ad8\\u5ea6\\u6807\\u51c6\\u662f\\u591a\\u5c11


\\u7a97\\u6237\\u5c3a\\u5bf8\\u89c4\\u683c\\u6807\\u51c6\\u4e00\\u822c\\u662f:\\u4e00\\u822c\\u4f4f\\u5b85\\u5efa\\u7b51\\u4e2d,\\u7a97\\u7684\\u9ad8\\u5ea6\\u4e3a1.5\\u7c73,\\u7a97\\u53f0\\u9ad8\\u5ea6\\u7ea6\\u4e3a0.9\\u7c73,\\u7a97\\u9876\\u8ddd\\u79bb\\u697c\\u9762\\u9ad8\\u5ea6\\u7ea6\\u4e3a2.4\\u7c73\\u3002\\u82e5\\u662f\\u7a97\\u53f0\\u9ad8\\u5ea6\\u4f4e\\u4e8e0.8\\u7c73\\u65f6\\u8981\\u6ce8\\u610f\\u91c7\\u53d6\\u9632\\u62a4\\u63aa\\u65bd\\u3002\\u7a97\\u6237\\u7684\\u5bbd\\u5ea6\\u901a\\u5e38\\u75310.6\\u7c73\\u5f00\\u59cb,\\u5bbd\\u5230\\u80fd\\u6784\\u6210\\u201c\\u5e26\\u7a97\\u201d,\\u91c7\\u7528\\u901a\\u5bbd\\u7684\\u5e26\\u7a97\\u65f6\\u5de6\\u53f3\\u9694\\u58c1\\u623f\\u95f4\\u7684\\u9694\\u58f0\\u95ee\\u9898,\\u82e5\\u662f\\u91c7\\u7528\\u63a8\\u62c9\\u7a97\\u5219\\u8981\\u8003\\u8651\\u7a97\\u6247\\u7684\\u6ed1\\u52a8\\u8303\\u56f4\\u3002\\u4e0d\\u95f4\\u7684\\u7a97\\u6237\\u5c3a\\u5bf8\\u662f\\u4e0d\\u540c\\u7684,\\u4e0b\\u9762\\u662f\\u4e00\\u4e9b\\u4e0d\\u95f4\\u7684\\u4e00\\u822c\\u7a97\\u6237\\u5c3a\\u5bf8\\u3002\",\n \"\\u4e3a\\u4ec0\\u5e7a\\u8f9e\\u804c\\u8bfb\\u7814\\u540e\\u6094\\u4e86\\u5728\\u804c\\u4eba\\u5458\\u8003\\u7814\\u6700\\u5927\\u7684\\u4e00\\u4e2a\\u95ee\\u9898\\u5c31\\u662f\\u65f6\\u95f4,\\u7b2c\\u4e00\\u662f\\u4e0a \\u8bfe\\u7684\\u65f6\\u95f4\\u7b2c\\u4e8c\\u662f\\u8003\\u4e0a\\u4e4b\\u540e\\u4e0a\\u5b66\\u7684\\u65f6\\u95f4,\\u90a3\\u5e7a\\u5c0f\\u7f16\\u4e3a\\u4f60\\u5177\\u4f53\\u5206\\u6790: ?(1)\\u4e0a\\u8bfe\\u7684\\u65f6\\u95f4:\\u5f88\\u591a\\u5b66\\u6821\\u4e3a\\u4e86\\u65b9\\u4fbf\\u5728\\u804c\\u4eba\\u5458\\u8003\\u7814\\u90fd\\u5f00\\u8bbe\\u4e86\\u7f51\\u7edc\\u73ed,\\u5468\\u672b\\u73ed, \\u5168\\u56fd\\u73ed\\u7b49\\u5f88\\u591a\\u79cd\\u73ed\\u7ea7,\\u80fd\\u591f\\u6781\\u5927\\u5730\\u65b9\\u4fbf\\u5728\\u804c\\u4eba\\u5458\\u7684\\u5b66\\u4e60,\\u8fd9\\u4e2a\\u65b9\\u9762\\u5012\\u662f\\u4e0d\\u7528 \\u62c5\\u5fc3\\u3002 ?(2)\\u4e0a\\u5b66\\u7684\\u65f6\\u95f4:\\u8fd9\\u4e2d\\u95f4\\u7275\\u626f\\u5230\\u662f\\u4e00\\u6708\\u8054\\u8003\\u8fd8\\u662f\\u4e94\\u6708\\u540c\\u7b49\\u5b66\\u529b\\u7533\\u7855,\\u5982\\u679c\\u662f \\u8003\\u4e0a\\u4e86\\u4e00\\u6708\\u8054\\u8003,\\u90a3\\u5e7a\\u65f6\\u95f4\\u662f\\u6839\\u672c\\u4e0d\\u591f\\u7684,\\u5982\\u679c\\u662f\\u5e94\\u5c4a\\u6bd5\\u4e1a\\u751f,\\u90a3\\u5e7a\\u8003\\u4e0a\\u4e86 \\u5168\\u65e5\\u5236\\u5927\\u5b66,\\u53ea\\u80fd\\u8f9e\\u804c,\\u5982\\u679c\\u662f\\u5728\\u804c\\u7814\\u7a76\\u751f,\\u8003\\u5f97\\u662f\\u4e00\\u6708\\u8054\\u8003,\\u90a3\\u5e7a\\u591a\\u4e0e\\u65f6 \\u95f4\\u8981\\u6c42\\u5f88\\u7d27\\u4fc3,\\u8003\\u5f97\\u662f\\u4e94\\u6708\\u540c\\u7b49\\u5b66\\u529b\\u7533\\u7855,\\u90a3\\u5e7a\\u65f6\\u95f4\\u8fd8\\u662f\\u5f88\\u5145\\u8db3\\u7684\\u3002 ?(3)\\u516c\\u53f8\\u6216\\u8005\\u5355\\u4f4d\\u660e\\u6587\\u89c4\\u5b9a\\u6210\\u5458\\u4e0d\\u80fd\\u79c1\\u4e0b\\u8003\\u7814,\\u90a3\\u5e7a\\u4f60\\u53ea\\u80fd\\u901a\\u8fc7\\u4ed6\\u4eec\\u7684\\u6e20\\u9053 \\u8fdb\\u884c\\u7533\\u62a5,\\u8868\\u73b0\\u5f88\\u79ef\\u6781\\u5f88\\u597d\\u7684\\u8bdd,\\u5c31\\u80fd\\u8003\\u7814,\\u8fd9\\u79cd\\u60c5\\u51b5\\u4e00\\u822c\\u662f\\u5fc5\\u987b\\u4e0e\\u516c\\u53f8\\u6216 \\u8005\\u5355\\u4f4d\\u7b7e\\u7f72\\u5408\\u540c,\\u76f8\\u5f53\\u4e8e\\u4e00\\u5b9a\\u671f\\u9650\\u5185\\u662f\\u4e0d\\u80fd\\u6362\\u5de5\\u4f5c\\u7684\\u3002 ?(4)\\u5355\\u4f4d\\u89c4\\u5b9a\\u80fd\\u591f\\u8003\\u7814,\\u4f46\\u662f\\u9700\\u8981\\u548c\\u516c\\u53f8\\u7b7e\\u7f72\\u5408\\u540c,\\u516c\\u53f8\\u4e0d\\u4f1a\\u5168\\u8d39\\u8d44\\u52a9\\u4f46\\u662f \\u4f1a\\u6709\\u4e00\\u5b9a\\u8865\\u52a9\\u3002 ?(5)\\u5355\\u4f4d\\u89c4\\u5b9a\\u80fd\\u8003\\u7814,\\u4e0d\\u9700\\u8981\\u7b7e\\u7f72\\u5408\\u540c,\\u4f46\\u662f\\u5168\\u90e8\\u81ea\\u8d39,\\u5982\\u679c\\u5f3a\\u5236\\u8981\\u6c42\\u548c\\u516c \\u53f8\\u7b7e\\u7f72\\u5408\\u540c,\\u53ef\\u4ee5\\u8d70\\u6cd5\\u5f8b\\u8bc9\\u8bbc\\u3002 ?(6)\\u5355\\u4f4d\\u89c4\\u5b9a\\u4e0d\\u80fd\\u8003\\u7814,\\u4f60\\u53c8\\u60f3\\u8003,\\u53ea\\u80fd\\u8f9e\\u804c\\u3002 ? ? ?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"neg\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5838,\n \"samples\": [\n \"\\u9898\\u76ee
\\u7535\\u5bb9\\u5145\\u7535\\u529f\\u7387\\u516c\\u5f0f
\\u5df2\\u77e5\\u7535\\u5bb9\\u6700\\u9ad8\\u7535\\u538b\\u4e3a1kv,\\u5bb9\\u91cf\\u4e3a1000uf,\\u6c42\\u57281800s\\u4e4b\\u5185\\u5145\\u6ee1\\u9700\\u8981\\u7684\\u529f\\u7387

\\u7b54\\u6848\\u89e3\\u6790
\\u67e5\\u770b\\u66f4\\u591a\\u4f18\\u8d28\\u89e3\\u6790
\\u7535\\u5bb9\\u5145\\u6ee1\\u7535\\u540e\\u5177\\u6709\\u7684\\u80fd\\u91cf:
W = 1/2\\u00d7C\\u00d7U^2 = 1/2\\u00d71000\\u00d710^(-6)F \\u00d7(1000V)^2 = 500 J
\\u6240\\u4ee5,\\u5e73\\u5747\\u529f\\u7387 P = W\\u00f7t = 500J\\u00f71800s \\u223d 0.28W
\\u89e3\\u6790\\u770b\\u4e0d\\u61c2?\\u514d\\u8d39\\u67e5\\u770b\\u540c\\u7c7b\\u9898\\u89c6\\u9891\\u89e3\\u6790
\\u67e5\\u770b\\u89e3\\u7b54\",\n \"\\u60a8\\u597d,\\u6839\\u636e\\u60a8\\u7684\\u53d9\\u8ff0\\u60a8\\u5e94\\u5c5e\\u4e8e\\u52c3\\u8d77\\u529f\\u80fd\\u969c\\u788d\\u8868\\u73b0\\u591a\\u4e3a\\u56e0\\u9634\\u830e\\u4e0d\\u80fd\\u52c3\\u8d77\\u3001\\u52c3\\u8d77\\u4e0d\\u575a\\u4e0d\\u80fd\\u81ea\\u884c\\u63d2\\u5165\\u9634\\u9053\\u6216\\u5728\\u63d2\\u5165\\u8fc7\\u7a0b\\u4e2d\\u840e\\u8f6f,\\u4f7f\\u6027\\u4ea4\\u4e0d\\u80fd\\u8fdb\\u884c\\u3002\\u5305\\u76ae\\u8fc7\\u957f\\u3001\\u5438\\u70df\\u3001\\u624b\\u6deb\\u3001\\u524d\\u5217\\u817a\\u708e\\u7b49\\u90fd\\u4f1a\\u5f15\\u8d77\\u5efa\\u8bae\\u5230\\u4e13\\u4e1a\\u7537\\u79d1\\u533b\\u9662\\u5c31\\u8bca,\\u53ef\\u901a\\u8fc7\\u5148\\u8fdb\\u7684\\u8bca\\u65ad\\u6280\\u672f\\u6765\\u786e\\u8bca,\\u9488\\u5bf9\\u6027\\u6cbb\\u7597\\u4e5f\\u80fd\\u5c3d\\u65e9\\u5eb7\\u590d\\u3002\",\n \"\\u783c\\u7684\\u635f\\u8017\\u7387\\u662f\\u591a\\u5c11?\\u8c22\\u8c22 \\u7b54:\\u8bf4\\u5b9e\\u8bdd,\\u4f1a\\u8ba1\\u5236\\u5ea6\\u53ca\\u6838\\u7b97\\u4e2d,\\u5bf9\\u4ea7\\u54c1\\u7684\\u5408\\u7406\\u635f\\u8017\\u5e76\\u6ca1\\u6709\\u660e\\u786e\\u89c4\\u5b9a\\u635f\\u8017\\u7387\\u662f\\u591a\\u5c11\\u3002\\u56e0\\u6b64 \\u6df7\\u51dd\\u571f\\u3002\\u94bb \\u6e05\\u695a\\u6ca5\\u9752 \\u4eca\\u5e74\\u80fd\\u8fc7\\u4e24\\u4e2a\\u751f\\u65e5 \\u6797\\u5219\\u5f90\\u785d\\u70df \\u6881\\u7684\\u5206\\u7c7b \\u6c34\\u6ce5\\u5730\\u9762\\u6253\\u591a\\u539a
\\u6df7\\u51dd\\u571f\\u8fd0\\u8f93\\u635f\\u8017\\u7cfb\\u6570 \\u5b9a\\u989d\\u57fa\\u4ef7 \\u9020\\u4ef7\\u5458\\u8003\\u8bd5 \\u9752\\u5e74\\u4eba\\u5e74\\u6708\\u65e5 .\\u6750\\u6599\\u8fd0\\u8f93\\u635f\\u8017\\u662f\\u6307\\u6750\\u6599\\u5728\\u8fd0\\u8f93\\u548c\\u88c5\\u5378\\u642c\\u8fd0\\u8fc7\\u7a0b\\u4e2d\\u4e0d\\u53ef\\u907f\\u514d\\u7684\\u635f\\u8017,\\u4e00\\u822c\\u901a\\u8fc7\\u635f\\u8017\\u7387\\u6765\\u89c4\\u3002.\\u5df2\\u77e5\\u5728\\u94a2\\u7b4b\\u6df7\\u51dd\\u571f\\u57fa\\u5c42\\u4e0a\\u505amm\\u539a:\\u6c34\\u6ce5\\u7802\\u6d46
\\u6d45\\u8c08\\u9884\\u62cc\\u6df7\\u51dd\\u571f\\u4f01\\u4e1a\\u7269\\u6599\\u7684\\u635f\\u8017\\u53ca\\u9884\\u63a7 \\u8c46\\u4e01\\u7f51 \\u4ece\\u539f\\u8f85\\u6750\\u6599\\u8ff8\\u5382\\u5230\\u4ea7\\u54c1\\u51fa\\u5382,\\u5b9e\\u9645\\u4e0a\\u662f\\u4e00\\u4e2a\\u7269\\u6d41\\u548c\\u7269\\u6599 \\u5e73\\u8861\\u8fc7\\u7a0b,\\u5176\\u95f4\\u5fc5\\u5b58\\u5728\\u635f\\u8017,\\u5982\\u4f55\\u89c4\\u8303\\u6709\\u6548\\u5730\\u8ba1\\u7b97\\u3001\\u63a7\\u5236\\u5176\\u635f\\u8017, \\u5c06\\u635f\\u8017\\u7387\\u9884\\u63a7\\u5728\\u5408\\u7406\\u8303\\u56f4\\u5185,\\u63d0\\u9ad8\\u9884\\u62cc\\u6df7
\\u6df7\\u51dd\\u571f\\u7684\\u635f\\u8017\\u7387\\u662f\\u591a\\u5c11 \\u5bb6\\u6838\\u4f18\\u5c45 \\u6309\\u56fd\\u5bb6\\u6807\\u51c6,\\u6df7\\u51dd\\u571f\\u7684\\u635f\\u8017\\u7387\\u4e3a\\u6b63\\u3001\\u8d1f\\u767e\\u5206\\u4e4b\\u4e8c\\u3002\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "source": [ + "result_df = result_df.dropna()" + ], + "metadata": { + "id": "BUewuga8TTWH" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "! pip install datasets" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SQbTP5gTTHsu", + "outputId": "7566df70-af4f-4c4c-daa8-cd440247bc2c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (3.5.0)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from datasets) (3.18.0)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (2.0.2)\n", + "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (18.1.0)\n", + "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.3.8)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n", + "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.11/dist-packages (from datasets) (2.32.3)\n", + "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.11/dist-packages (from datasets) (4.67.1)\n", + "Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from datasets) (3.5.0)\n", + "Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.70.16)\n", + "Requirement already satisfied: fsspec<=2024.12.0,>=2023.1.0 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2024.12.0,>=2023.1.0->datasets) (2024.12.0)\n", + "Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from datasets) (3.11.14)\n", + "Requirement already satisfied: huggingface-hub>=0.24.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.29.3)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from datasets) (24.2)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from datasets) (6.0.2)\n", + "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (2.6.1)\n", + "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.3.2)\n", + "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (25.3.0)\n", + "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.5.0)\n", + "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (6.2.0)\n", + "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (0.3.0)\n", + "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.18.3)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.24.0->datasets) (4.12.2)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.4.1)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.10)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2.3.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2025.1.31)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.1)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from datasets import Dataset, DatasetDict\n", + "from sklearn.model_selection import train_test_split\n", + "from huggingface_hub import login, create_repo" + ], + "metadata": { + "id": "ANvThSSyxt61" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# 转换为Hugging Face Dataset\n", + "dataset = Dataset.from_pandas(result_df)\n", + "\n", + "# 划分数据集\n", + "# 首先划分训练集和临时集 (80% train, 20% temp)\n", + "train_df, temp_df = train_test_split(result_df, test_size=0.2, random_state=42)\n", + "\n", + "# 然后从临时集中划分验证集和测试集 (50% dev, 50% test)\n", + "dev_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n", + "\n", + "# 转换为Dataset对象\n", + "train_dataset = Dataset.from_pandas(train_df)\n", + "dev_dataset = Dataset.from_pandas(dev_df)\n", + "test_dataset = Dataset.from_pandas(test_df)\n", + "\n", + "# 创建DatasetDict\n", + "dataset_dict = DatasetDict({\n", + " \"train\": train_dataset,\n", + " \"validation\": dev_dataset,\n", + " \"test\": test_dataset\n", + "})\n" + ], + "metadata": { + "id": "bmlXZD19TRDY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import userdata\n", + "userdata.get('HF_TOKEN')\n", + "\n", + "login(token=userdata.get('HF_TOKEN'))\n", + "\n", + "# 创建数据集仓库\n", + "repo_id = \"bcai001/c-sts-t2ranking-query-as-condition\" # 例如: \"johnsmith/sentiment-analysis-data\"\n", + "create_repo(repo_id, repo_type=\"dataset\", exist_ok=True)\n", + "\n", + "# 上传数据集\n", + "dataset_dict.push_to_hub(repo_id, private=True)\n", + "\n", + "print(f\"Dataset uploaded to: https://huggingface.co/datasets/{repo_id}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 227, + "referenced_widgets": [ + "baa06260424c4f079f0250950900c535", + "ef6434ba075c46a185e1c99fae81713c", + "56e2f23e4a4c4afd9c0195c53c53e6ab", + "8f734141c6514beb869ffd4cb08e60f6", + "a2577001f8274e0281d0b95ded1543ba", + "178ee68b4d8341c5b42f645177a30c48", + "d79c30c1ea1d4f0f9fa0b4d0a82f4fc6", + "9a1a792d57304ad08c51baadaa54f224", + "b23df70fef024ce2b7b9b9ce1d1c12bb", + "178b2741232e4c62a60c9665bf720579", + "5e95f559ca63450b8f9678f7404d8de4", + "9e027dc25688494fb356494fe41d01ea", + "b171e2fb40584d53bb45ca0c62605162", + "ac30d6bab2b343a1a44b0c6511947434", + "a55bd449648745909b6753a124c1910e", + "99a6b254a63a4d78b9f5957b69aeee65", + "d72db3e3edb24b2680afc11ad450a279", + "a0c0cec387ee4b25a8e92ff9f7c64e86", + "90987220b3ad4b7c80b5573710e6ad85", + "3cf90ac9d04a4b9c9f04fe884f991404", + "4be699695c4f485eae90d601111011d2", + "ba30195bcaa24336a5af29d9280f2dfe", + "613dd88aaf684dc0b2f159d48ef4a72f", + "86b72d81f9884ab4892c0a66bb189a35", + "695db22343564105b604f4a7c4dc1d97", + "0601003a6ce14525bc52f451e45d4830", + "8064e5469a3b4794a67d57c0329e1a83", + "ae242adfdeda4138966151a6f4b2a881", + "b0393fba9ce24c598e5a34feadc56f5f", + "397f89172d2e4de4b80cace6b48c6d9b", + "c0eca1e6909c4f91a886a302f820b8b8", + "e0cbd109f265428abd62e2c06ed9b8e5", + "04271c6b6f78486aa65db1b2887cbb22", + "72afd67e51c748f0927101749cf2b5fd", + "d6668925180e4d61837c097d76fdf863", + "dc5aff585035477c8e9b067455cdeb27", + "89c47ed7c85a48f496a76fd5ae41868d", + "8b5980b36c1f44d88b55fdb06a1d5d91", + "e65abcb51cbf4ddc913ce29165f5f803", + "c46c9393cd2a4d7ebd1223711b1e13b7", + "9f1bd43cf0914272bd64e17007d92638", + "6dafc629ca0449538a2f4164e9a1dde8", + "e9151b44eb7e494699beb65a21b17ab2", + "7daf0e71ae6f40518d9a0052117e69d1", + "391eff8f2a894db08c88baf7c65e94dc", + "4b0b05af8d124d34998448302a568d38", + "ffb9c0c5ca154fc5a7221b6ba4b497ba", + "9176f9accfd94fd2a1e86f30121ad365", + "ec103b8f4ca449a5ab47999421e08cb4", + "eecc36d6419441c5ad970846753c3eb2", + "a16a6c20de274fffa59e6fa78ba66bb1", + "ba86a4e376c54c6c8dd06278082d8e31", + "8f0b5204839e48f99831ea82478e0e26", + "ed18d4e634dc4c54aa2e6de3939131ac", + "984b41e36f1647e099e351ad4855c96c", + "bbe37add810247ce96f1bce6948a8dc9", + "7a2d7ed3ac674c0fb45fb938d286ae2a", + "832f1293427342e18a3f4099222bbb61", + "9e59dee564b64b9eb9f7d0a67c43e548", + "ba1302210765460b9a383dc99e97ef53", + "d7991fe5b7e04a16a3667de2c05f7575", + "f041098dad4b4df8a5103271ff14f213", + "dd1a1bac484f4c839c10c2ece4f9f911", + "613bcd2618d74d2bb4cc6365a4f83236", + "19c3b2f5b5634ac8964d3ce23e2e8390", + "4084d8d01f824cdfb408bd5d2967faac" + ] + }, + "id": "MH4fEd_FTo5U", + "outputId": "7f143227-ef5e-44e6-81f1-76c4f4c271c9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Uploading the dataset shards: 0%| | 0/1 [00:00