Attention Is All You Need.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"Attention Is All You Need.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"},"widgets":{"application/vnd.jupyter.widget-state+json":{"e8aeb1336fda49548cf376f7f6af922a":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_a5b62788666d45f0bbef64be24f06252","IPY_MODEL_19fe60eaf96b4332952a78338ff2c22a"],"layout":"IPY_MODEL_e37a41d7c6a848f3ba53af5e91667aa7"}},"e37a41d7c6a848f3ba53af5e91667aa7":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"a5b62788666d45f0bbef64be24f06252":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_ee9eaab73c2b4543a70cafdde054d91e","max":454,"min":0,"orientation":"horizontal","style":"IPY_MODEL_01f04d04d39f45fb81c85b89d37f12ec","value":454}},"19fe60eaf96b4332952a78338ff2c22a":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_8cdf1c43e67643cab18bbf0ca3eb1e82","placeholder":"​","style":"IPY_MODEL_cae6b0a3cfcd49fbb21e1f24aaf9c117","value":" 454/454 [00:53&lt;00:00,  8.46it/s, loss=4.95]"}},"01f04d04d39f45fb81c85b89d37f12ec":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"ee9eaab73c2b4543a70cafdde054d91e":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"cae6b0a3cfcd49fbb21e1f24aaf9c117":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"8cdf1c43e67643cab18bbf0ca3eb1e82":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"69d5b2f7378c4d4eb6c29429ee0e10dc":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_11115d9f0b3d45f7bcedeaa6cbda4b69","IPY_MODEL_ee61bcf289cf42b1a6184da1ca9b1d62"],"layout":"IPY_MODEL_09df04e92dd142828e63cde2e4b33483"}},"09df04e92dd142828e63cde2e4b33483":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"11115d9f0b3d45f7bcedeaa6cbda4b69":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_959d1c2efecb400c83c24daf21535cd1","max":16,"min":0,"orientation":"horizontal","style":"IPY_MODEL_7f71a47637da4f4682f242df6ae337d7","value":16}},"ee61bcf289cf42b1a6184da1ca9b1d62":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_bc3af51a16e24f9eb17017920cd54570","placeholder":"​","style":"IPY_MODEL_d536c15257bd441388b06de04f50fd1a","value":" 16/16 [06:50&lt;00:00, 25.64s/it, loss=3.42]"}},"7f71a47637da4f4682f242df6ae337d7":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"959d1c2efecb400c83c24daf21535cd1":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"d536c15257bd441388b06de04f50fd1a":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"bc3af51a16e24f9eb17017920cd54570":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"4d9cbf8a87bf471e83ff47d39b9e64ea":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_4da1e4ae3daf4a6ba56593e75097b95d","IPY_MODEL_03bf4bb6fb994ce4ab865f6dc13d96cf"],"layout":"IPY_MODEL_25f291d1ceb14de0b49e4a7b6b9f604e"}},"25f291d1ceb14de0b49e4a7b6b9f604e":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"4da1e4ae3daf4a6ba56593e75097b95d":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_c7d8801678ba45a89dd414b755648121","max":454,"min":0,"orientation":"horizontal","style":"IPY_MODEL_cd24faed475743ed9c801649d541aec1","value":454}},"03bf4bb6fb994ce4ab865f6dc13d96cf":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_e06bca597e864f4eb5d185bc1b6acd61","placeholder":"​","style":"IPY_MODEL_a9347e2f80734cefa5e10d6316547c41","value":" 454/454 [00:53&lt;00:00,  8.50it/s, loss=3.06]"}},"cd24faed475743ed9c801649d541aec1":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"c7d8801678ba45a89dd414b755648121":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"a9347e2f80734cefa5e10d6316547c41":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"e06bca597e864f4eb5d185bc1b6acd61":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"4a8ef81a27a341c0b887f089fc75a958":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_516af50b6e89499cb5ec15f2687f3b0d","IPY_MODEL_ed5ccb8223f944409aa54d454ecfcfa0"],"layout":"IPY_MODEL_7dbccd8f35a248c0861e961d2eafc201"}},"7dbccd8f35a248c0861e961d2eafc201":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"516af50b6e89499cb5ec15f2687f3b0d":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_0c9e04465e4e4b54abf7aea56d90f826","max":16,"min":0,"orientation":"horizontal","style":"IPY_MODEL_3b3d35d7031f4a399f7281d7fe4bbb2f","value":16}},"ed5ccb8223f944409aa54d454ecfcfa0":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_bca0b980810d4ead94283d6e81736555","placeholder":"​","style":"IPY_MODEL_28267cb9bf1c4f529a366def0e4b3182","value":" 16/16 [00:56&lt;00:00,  3.55s/it, loss=2.57]"}},"3b3d35d7031f4a399f7281d7fe4bbb2f":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"0c9e04465e4e4b54abf7aea56d90f826":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"28267cb9bf1c4f529a366def0e4b3182":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"bca0b980810d4ead94283d6e81736555":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"99fbbd2d2c664db1bee2194adcd0be5d":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_1fb0efe9e65f48579ebf51793ea2ae0a","IPY_MODEL_3b25aeaf60b042d48a99830272453a64"],"layout":"IPY_MODEL_fb67518989ac425992bcd07f9e2f349d"}},"fb67518989ac425992bcd07f9e2f349d":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"1fb0efe9e65f48579ebf51793ea2ae0a":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_b3c2c4f1e7844fb2a0ebb3188fa4ac38","max":454,"min":0,"orientation":"horizontal","style":"IPY_MODEL_f2ede860d9254525a6490fa4cdaf9bcb","value":454}},"3b25aeaf60b042d48a99830272453a64":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_0e35363ead8840508aabb126d61b84ec","placeholder":"​","style":"IPY_MODEL_43bac4a77828440aa0070926b8665c57","value":" 454/454 [00:53&lt;00:00,  8.49it/s, loss=2.43]"}},"f2ede860d9254525a6490fa4cdaf9bcb":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"b3c2c4f1e7844fb2a0ebb3188fa4ac38":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"43bac4a77828440aa0070926b8665c57":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"0e35363ead8840508aabb126d61b84ec":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"2a5f3d41bf5541cba5ac75aa0f684db9":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_de6ea26bd89e4bb29e9ebc497be11be7","IPY_MODEL_a9401445de46497bb3fb8f4f27a9865c"],"layout":"IPY_MODEL_fb62961b77664df7a32b3ec53a7a011c"}},"fb62961b77664df7a32b3ec53a7a011c":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"de6ea26bd89e4bb29e9ebc497be11be7":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_6f2ae0fab76440f885572afd806e54b7","max":16,"min":0,"orientation":"horizontal","style":"IPY_MODEL_1fd401531f7d4221a3bc4cfaf5308dc9","value":16}},"a9401445de46497bb3fb8f4f27a9865c":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_bace66df38e54cc6b72a69330bccb256","placeholder":"​","style":"IPY_MODEL_631bd193a5e44c9494b2cda41aabf239","value":" 16/16 [04:57&lt;00:00, 18.58s/it, loss=2.2]"}},"1fd401531f7d4221a3bc4cfaf5308dc9":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"6f2ae0fab76440f885572afd806e54b7":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"631bd193a5e44c9494b2cda41aabf239":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"bace66df38e54cc6b72a69330bccb256":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"e20c18acf4a1465b96f9193dc6ece13e":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_3a9f8ea2c24c4ccf835d7c75a84044b6","IPY_MODEL_b251a084981d4c69b884ebee357e2c65"],"layout":"IPY_MODEL_2c4d51b3d86c48d7aa9fbdd0f0ddf866"}},"2c4d51b3d86c48d7aa9fbdd0f0ddf866":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"3a9f8ea2c24c4ccf835d7c75a84044b6":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_fea155d07fe74a84ab454de5aeb6085d","max":454,"min":0,"orientation":"horizontal","style":"IPY_MODEL_b7078e9db8b8443eaa46b997cc2f0736","value":454}},"b251a084981d4c69b884ebee357e2c65":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_eb07aec52bbf48709d596b62634db4a4","placeholder":"​","style":"IPY_MODEL_dc0d5623dc534cf48a9401b40bb39f34","value":" 454/454 [00:53&lt;00:00,  8.53it/s, loss=2.09]"}},"b7078e9db8b8443eaa46b997cc2f0736":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"fea155d07fe74a84ab454de5aeb6085d":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"dc0d5623dc534cf48a9401b40bb39f34":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"eb07aec52bbf48709d596b62634db4a4":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"cbef428b0ddf4a5a9d7064a8d5d902f8":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_2e9e722708a44e1e990325539685ded5","IPY_MODEL_0a0b80bd60d643cd98af1ba2c49078ab"],"layout":"IPY_MODEL_0194315b6461458688d78342f0504cbd"}},"0194315b6461458688d78342f0504cbd":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"2e9e722708a44e1e990325539685ded5":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_06c419efd7444dae8696acf6496129b9","max":16,"min":0,"orientation":"horizontal","style":"IPY_MODEL_18235b6f867d4e54a979d5cfb57d600a","value":16}},"0a0b80bd60d643cd98af1ba2c49078ab":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_8c7a73dfd5364e459a03db3eb48db05c","placeholder":"​","style":"IPY_MODEL_d4116f0ef0a2421d86769f810f008d14","value":" 16/16 [00:57&lt;00:00,  3.57s/it, loss=2.03]"}},"18235b6f867d4e54a979d5cfb57d600a":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"06c419efd7444dae8696acf6496129b9":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"d4116f0ef0a2421d86769f810f008d14":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"8c7a73dfd5364e459a03db3eb48db05c":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"0a819e74e7b745bdbd35658db37a92f5":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_8d7c304548fb4ae0a0a2aa14681610f3","IPY_MODEL_993341b769fe4ac682142db7bf39a904"],"layout":"IPY_MODEL_f14bf853129a40639ed2aac2919805a9"}},"f14bf853129a40639ed2aac2919805a9":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"8d7c304548fb4ae0a0a2aa14681610f3":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_7b543f755a364bd4811dfee80cf269f7","max":454,"min":0,"orientation":"horizontal","style":"IPY_MODEL_f3eb61310db14fe3b931dd80abdf208e","value":454}},"993341b769fe4ac682142db7bf39a904":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_892d59d27f684c3cb75bd62187cda102","placeholder":"​","style":"IPY_MODEL_2c83a2a93ee8410590588f7d8d53f0a2","value":" 454/454 [00:53&lt;00:00,  8.50it/s, loss=1.91]"}},"f3eb61310db14fe3b931dd80abdf208e":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"7b543f755a364bd4811dfee80cf269f7":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"2c83a2a93ee8410590588f7d8d53f0a2":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"892d59d27f684c3cb75bd62187cda102":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"e32af2cd5eb14ab2806e94f23844c48b":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_479491ba3f904d0ab6a41c0da7d49b01","IPY_MODEL_f2f5198abc1c4dafba4826a12477b94d"],"layout":"IPY_MODEL_d0494fa575f043f596a478472abbaead"}},"d0494fa575f043f596a478472abbaead":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"479491ba3f904d0ab6a41c0da7d49b01":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_fd382220e30a42d1ab4b2c02ef8ffab4","max":16,"min":0,"orientation":"horizontal","style":"IPY_MODEL_94b91f5e2d0f48959cb8ccdb56d4ec01","value":16}},"f2f5198abc1c4dafba4826a12477b94d":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_203ff12468bf4b8a93aa0f015e0351ce","placeholder":"​","style":"IPY_MODEL_1b053268dbd14ae4a8d09419d23eed67","value":" 16/16 [03:04&lt;00:00, 11.52s/it, loss=1.97]"}},"94b91f5e2d0f48959cb8ccdb56d4ec01":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"fd382220e30a42d1ab4b2c02ef8ffab4":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"1b053268dbd14ae4a8d09419d23eed67":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"203ff12468bf4b8a93aa0f015e0351ce":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"898231c431434b70adea4a773975a3b2":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_8a0348c359d74a77aedcbd4e638e422e","IPY_MODEL_24690fe1c6d34af3a738a23cda53f476"],"layout":"IPY_MODEL_2f0b710c09444946bf313f53a4c948ab"}},"2f0b710c09444946bf313f53a4c948ab":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"8a0348c359d74a77aedcbd4e638e422e":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_6fea03d1f5284c17bdaae2594f4c39b3","max":454,"min":0,"orientation":"horizontal","style":"IPY_MODEL_4391a4114c354cc1a8562c284f7119b4","value":454}},"24690fe1c6d34af3a738a23cda53f476":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_1fdc61c7e31945dfa83e2ed9aeac4ef8","placeholder":"​","style":"IPY_MODEL_cf10442feb4d4184a44f667111ac34ee","value":" 454/454 [00:53&lt;00:00,  8.49it/s, loss=1.85]"}},"4391a4114c354cc1a8562c284f7119b4":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"6fea03d1f5284c17bdaae2594f4c39b3":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"cf10442feb4d4184a44f667111ac34ee":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"1fdc61c7e31945dfa83e2ed9aeac4ef8":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"80d277eeb07d43ea9e1e5f43db0dc096":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_5f643ef8139b401a914b719e462c6b33","IPY_MODEL_d0afe0617e4c4bff92e246c312860d74"],"layout":"IPY_MODEL_c0ac8b7de2c944378a5b74e9bcb5d954"}},"c0ac8b7de2c944378a5b74e9bcb5d954":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"5f643ef8139b401a914b719e462c6b33":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_a821acbf979c49cdbc0233de4ec67acf","max":16,"min":0,"orientation":"horizontal","style":"IPY_MODEL_0d7b94c952be4bc59f4f061622321c92","value":16}},"d0afe0617e4c4bff92e246c312860d74":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_aa316c34d53b492bad4d36c0233a187d","placeholder":"​","style":"IPY_MODEL_3eb5c9e90b78423abbbbc96580f0b270","value":" 16/16 [00:57&lt;00:00,  3.59s/it, loss=1.97]"}},"0d7b94c952be4bc59f4f061622321c92":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"a821acbf979c49cdbc0233de4ec67acf":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"3eb5c9e90b78423abbbbc96580f0b270":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"aa316c34d53b492bad4d36c0233a187d":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"ed0c33858e0e4122afabd055f2edcfa2":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_c03279242d524b30b76ab3ca9263ce35","IPY_MODEL_0ae7a22f6ccb4210aaad82aa02b4d111"],"layout":"IPY_MODEL_3da737b389bb45be9c363cae56376c12"}},"3da737b389bb45be9c363cae56376c12":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"c03279242d524b30b76ab3ca9263ce35":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_cc33b1a8a65e4b5b821106377fdc278b","max":454,"min":0,"orientation":"horizontal","style":"IPY_MODEL_0c0b6849f7484b35873469a4db32232f","value":454}},"0ae7a22f6ccb4210aaad82aa02b4d111":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_4e601e18bcd5496bbef3cb06e0ebbb28","placeholder":"​","style":"IPY_MODEL_e783382140ff42d1b83b4807f69fe59f","value":" 454/454 [00:53&lt;00:00,  8.48it/s, loss=1.87]"}},"0c0b6849f7484b35873469a4db32232f":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"cc33b1a8a65e4b5b821106377fdc278b":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"e783382140ff42d1b83b4807f69fe59f":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"4e601e18bcd5496bbef3cb06e0ebbb28":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"b953ec33ec0641289852c60f72575f5d":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_b77999e60d3948d9be5fc882681802fe","IPY_MODEL_3c4112beeab14a21a731b0a6d1512b13"],"layout":"IPY_MODEL_cc7b220abefd426b866e844f8cecd29e"}},"cc7b220abefd426b866e844f8cecd29e":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"b77999e60d3948d9be5fc882681802fe":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_dca746d902354cc5946eb4c1cb22cf7b","max":16,"min":0,"orientation":"horizontal","style":"IPY_MODEL_8b0b3963eafb4753b807228ec2c66377","value":16}},"3c4112beeab14a21a731b0a6d1512b13":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_af6232cb83c24a739b315f76f2122862","placeholder":"​","style":"IPY_MODEL_d61e710e4ac549369ac56718190bb410","value":" 16/16 [01:11&lt;00:00,  4.48s/it, loss=2.06]"}},"8b0b3963eafb4753b807228ec2c66377":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"dca746d902354cc5946eb4c1cb22cf7b":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"d61e710e4ac549369ac56718190bb410":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"af6232cb83c24a739b315f76f2122862":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"bf6820f63e484c959ace1c107abc5211":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_fc0c8156f748403b8b749d0a8dc9c4d8","IPY_MODEL_a440b7baa94143ada7f279810ed27387"],"layout":"IPY_MODEL_a6ff1a8899404eed88d3805e633e5f48"}},"a6ff1a8899404eed88d3805e633e5f48":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"fc0c8156f748403b8b749d0a8dc9c4d8":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_b5d21132450443efaefbdf4532aefe2c","max":454,"min":0,"orientation":"horizontal","style":"IPY_MODEL_0bbc6b0ef69d40abbb317c393b720b03","value":454}},"a440b7baa94143ada7f279810ed27387":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_bf05c9db31a34586bcc2e6be75496405","placeholder":"​","style":"IPY_MODEL_ba0af9c682a341748e03443cf07bc10f","value":" 454/454 [00:53&lt;00:00,  8.48it/s, loss=2]"}},"0bbc6b0ef69d40abbb317c393b720b03":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"b5d21132450443efaefbdf4532aefe2c":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"ba0af9c682a341748e03443cf07bc10f":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"bf05c9db31a34586bcc2e6be75496405":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"5499f57947ef478b95041b0325db0826":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_ad603b57272a4b499c36cdc2894412b0","IPY_MODEL_85bb2e410ce74096b001b0787f9ef68c"],"layout":"IPY_MODEL_7a01ffd3c2dd4d85bb774b968f72ffbf"}},"7a01ffd3c2dd4d85bb774b968f72ffbf":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"ad603b57272a4b499c36cdc2894412b0":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_fd1ddfa6a5eb49849f4a263e8d8bb3ab","max":16,"min":0,"orientation":"horizontal","style":"IPY_MODEL_48ca01b6f5f84ecd96895d04ff2b6b86","value":16}},"85bb2e410ce74096b001b0787f9ef68c":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_ec54a394676449279c6c89b92c1fbd5f","placeholder":"​","style":"IPY_MODEL_77b3577863814591ad02c7897c8c77ae","value":" 16/16 [00:08&lt;00:00,  1.88it/s, loss=2.2]"}},"48ca01b6f5f84ecd96895d04ff2b6b86":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"fd1ddfa6a5eb49849f4a263e8d8bb3ab":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"77b3577863814591ad02c7897c8c77ae":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"ec54a394676449279c6c89b92c1fbd5f":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"e3db3d5adfc84fff95bc553dbdc39165":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","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_5028b81b0d9d45a48f117de230634c03","IPY_MODEL_993307bf11c54448803e4e4adf4054b6"],"layout":"IPY_MODEL_b890e392b87c4bb5afa9a2e2db45a0c1"}},"b890e392b87c4bb5afa9a2e2db45a0c1":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"5028b81b0d9d45a48f117de230634c03":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","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":"100%","description_tooltip":null,"layout":"IPY_MODEL_cd88947bc69841eebf49208ba8a7ef9c","max":16,"min":0,"orientation":"horizontal","style":"IPY_MODEL_c88220cfecf24d39b6c7e8e5ece4eb51","value":16}},"993307bf11c54448803e4e4adf4054b6":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","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_48133b56453d4207978cae101320c061","placeholder":"​","style":"IPY_MODEL_ac394ee9eb9a4ce99f9c9b8aaed3fd0c","value":" 16/16 [00:08&lt;00:00,  1.86it/s, loss=1.95]"}},"c88220cfecf24d39b6c7e8e5ece4eb51":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","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":"initial"}},"cd88947bc69841eebf49208ba8a7ef9c":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}},"ac394ee9eb9a4ce99f9c9b8aaed3fd0c":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","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":""}},"48133b56453d4207978cae101320c061":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","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}}}}},"cells":[{"cell_type":"markdown","metadata":{"id":"zxtnjdKur3qe"},"source":["# Attention Is All You Need\n","\n","\n","_This notebook demonstrates the implementation of Transformers architecture proposed by [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762) for neural machine translation (NMT)._\n"]},{"cell_type":"markdown","metadata":{"id":"6K_bcX5BZyk5"},"source":["> _Sequence-to-sequence models are deep learning models that have achieved a lot of success in tasks like machine translation, text summarization, and image captioning. Google Translate started [using](https://blog.google/products/translate/found-translation-more-accurate-fluent-sentences-google-translate/) such a model in production in late 2016. These models are explained in the two pioneering papers ([Sutskever et al., 2014](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf), [Cho et al., 2014](http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf)). (Excerpt from a [blog post](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/) by Jay Alammar)_\n","\n","Sequence-to-sequence (aka seq2seq) model is an end-to-end deep learning model which transforms one sequence to another. It consists of two recurrent neural networks:\n","- encoder, which encodes an input sequence of variable size into a context vector of fixed size\n","- decoder, which unfolds the context vector into a new sequence (output).\n"]},{"cell_type":"markdown","metadata":{"id":"STglYoYWZ3EG"},"source":["![seq2seq model](data:image/png;base64,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 \"Seq2Seq Model\")\n"]},{"cell_type":"markdown","metadata":{"id":"A4EjRUU9bNBd"},"source":["Seq2Seq NMT model can be thought of autoregressive language model where the decoder finds the target sequence that is the most probable conditioned on the input sequence. Mathematically, it can be formulated as follows:\n","\n","$$\n","P(y|x) = P(y_1|x) P(y_2|y_1,x) \\dots P(y_T|y_1, \\dots, y_{T-1}, x)\n","$$\n","\n","where $x$ is the input sequence and $y$ is the output sequence.\n"]},{"cell_type":"markdown","metadata":{"id":"uhAz-gO7bTLb"},"source":["Attention mechanism have overcomed the drawback of vanilla seq2seq models by providing us a way to see different parts of encoder outputs during the decoding stage, which allows us to preserve the context. The attention mechanim between encoder and decoder resulted in performance improvements for machine translation task.\n","\n","Transformer are a class of deep learning models which does not use any recurrence. Instead they use self-attention mechanism for representations to find global dependencies between input and target. Transformers have become the de facto standard in NLP and have achieved state-of-the-art performance for various tasks. The Transformer is behind all the popular language models such as BERT, GPT, etc.\n"]},{"cell_type":"markdown","metadata":{"id":"ODkEzd29_OHL"},"source":["We will build a Transformer model for English to German translation using PyTorch.\n"]},{"cell_type":"code","metadata":{"id":"XoaCg-ckvOJ2"},"source":["%%capture\n","# download the spacy language models for english and german\n","!python -m spacy download en --quiet\n","!python -m spacy download de --quiet"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3B4upHLMvSSi"},"source":["%%capture\n","!pip install torchtext --upgrade"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3WXGcHeZvU3W"},"source":["## Imports\n"]},{"cell_type":"code","metadata":{"id":"W22AvWY0yF0b"},"source":["import math\n","import random\n","import numpy as np\n","import seaborn as sns\n","from pylab import rcParams\n","import matplotlib.pyplot as plt\n","from matplotlib import rc\n","\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torch.nn.functional as F\n","\n","import spacy\n","import torchtext\n","from torchtext.legacy.datasets import Multi30k\n","from torchtext.legacy.data import Field, BucketIterator\n","from torchtext.data.metrics import bleu_score\n","\n","import warnings\n","from tqdm.notebook import tqdm\n","warnings.filterwarnings('ignore')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7K1GUB75yMg0"},"source":["# plot style configuration\n","%matplotlib inline\n","%config InlineBackend.figure_format = 'retina'\n","\n","sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n","\n","rcParams['figure.figsize'] = 12, 8"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"61KZ6jb0yOC9"},"source":["# for reproducibility\n","# refer https://pytorch.org/docs/stable/notes/randomness.html\n","SEED = 42\n","\n","random.seed(SEED)\n","np.random.seed(SEED)\n","torch.manual_seed(SEED)\n","torch.cuda.manual_seed(SEED)\n","torch.backends.cudnn.deterministic = True"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"MEFtjtwayP5d"},"source":["device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1HntcYvjyQlH"},"source":["## Data Preprocessing and Loading\n"]},{"cell_type":"code","metadata":{"id":"J7pNEQTsyVH6"},"source":["spacy_en = spacy.load('en')\n","spacy_de = spacy.load('de')\n","\n","def tokenize_en(text):\n","    \"\"\" Translates english text sequence into a list of tokens.\n","    \"\"\"\n","    return [token.text for token in spacy_en.tokenizer(text)]\n","\n","def tokenize_de(text):\n","    \"\"\" Translates german text sequence into a list of tokens.\n","    \"\"\"\n","    return [token.text for token in spacy_de.tokenizer(text)]\n","\n","PAD_TOKEN = '<pad>'\n","SOS_TOKEN = '<s>'\n","EOS_TOKEN = '</s>'\n","UNK_TOKEN = '<unk>'\n","\n","# english text field\n","en_text = Field(\n","    tokenize=tokenize_en, lower=True, include_lengths=True, batch_first=True,\n","    pad_token=PAD_TOKEN, init_token=SOS_TOKEN, eos_token=EOS_TOKEN, unk_token=UNK_TOKEN,\n",")\n","\n","# german text field\n","de_text = Field(\n","    tokenize=tokenize_de, lower=True, include_lengths=True, batch_first=True,\n","    pad_token=PAD_TOKEN, init_token=SOS_TOKEN, eos_token=EOS_TOKEN, unk_token=UNK_TOKEN,\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VvzIKI_ZyV5-","executionInfo":{"elapsed":1910506,"status":"ok","timestamp":1622724211755,"user":{"displayName":"Jay Gala","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghr20IAxple1q2jggw00r147YTV5sjlqKrRANzYVA=s64","userId":"11525158385229787114"},"user_tz":-330},"outputId":"9731e07a-9200-4019-ace4-5c235422188c"},"source":["data_fields = (('src', en_text), ('trg', de_text))\n","\n","train_data, valid_data, test_data = Multi30k.splits(\n","    exts=('.en', '.de'), fields=data_fields\n",")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["downloading training.tar.gz\n"],"name":"stdout"},{"output_type":"stream","text":["training.tar.gz: 100%|██████████| 1.21M/1.21M [00:00<00:00, 1.30MB/s]\n"],"name":"stderr"},{"output_type":"stream","text":["downloading validation.tar.gz\n"],"name":"stdout"},{"output_type":"stream","text":["validation.tar.gz: 100%|██████████| 46.3k/46.3k [00:00<00:00, 176kB/s]\n"],"name":"stderr"},{"output_type":"stream","text":["downloading mmt_task1_test2016.tar.gz\n"],"name":"stdout"},{"output_type":"stream","text":["mmt_task1_test2016.tar.gz: 100%|██████████| 66.2k/66.2k [00:00<00:00, 166kB/s]\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"dekZ61FYUQeS"},"source":["# build the english and german vocabulary\n","en_text.build_vocab(train_data.src, max_size=10000, min_freq=2)\n","de_text.build_vocab(train_data.trg, max_size=10000, min_freq=2)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z8A95oSRyb5t","executionInfo":{"elapsed":31,"status":"ok","timestamp":1622789355159,"user":{"displayName":"Blitz Striker","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5ROpeT4_n-3EmkBAU-gzRfia2y4oSdzK8x8Zo=s64","userId":"15921332253478855826"},"user_tz":-330},"outputId":"a53f0a46-2485-4ae6-a8c8-d2ad6265b6e5"},"source":["PAD_IDX = de_text.vocab.stoi[PAD_TOKEN]\n","SOS_IDX = de_text.vocab.stoi[SOS_TOKEN]\n","EOS_IDX = de_text.vocab.stoi[EOS_TOKEN]\n","UNK_IDX = de_text.vocab.stoi[UNK_TOKEN]\n","\n","print(f'English vocabulary: {len(en_text.vocab)} words')\n","print(f'German vocabulary: {len(de_text.vocab)} words')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["English vocabulary: 5893 words\n","German vocabulary: 7855 words\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i5555iYBydy2","executionInfo":{"elapsed":5,"status":"ok","timestamp":1622789357245,"user":{"displayName":"Blitz Striker","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5ROpeT4_n-3EmkBAU-gzRfia2y4oSdzK8x8Zo=s64","userId":"15921332253478855826"},"user_tz":-330},"outputId":"cb933d6f-1f73-41ad-fc5b-654b36beffba"},"source":["print(f'# of training examples: {len(train_data.examples)}')\n","print(f'# of validation examples: {len(valid_data.examples)}')\n","print(f'# of testing examples: {len(test_data.examples)}')\n","\n","print(train_data[0].__dict__.keys())\n","print(train_data[0].__dict__.values())"],"execution_count":null,"outputs":[{"output_type":"stream","text":["# of training examples: 29000\n","# of validation examples: 1014\n","# of testing examples: 1000\n","dict_keys(['src', 'trg'])\n","dict_values([['two', 'young', ',', 'white', 'males', 'are', 'outside', 'near', 'many', 'bushes', '.'], ['zwei', 'junge', 'weiße', 'männer', 'sind', 'im', 'freien', 'in', 'der', 'nähe', 'vieler', 'büsche', '.']])\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Jovm9rTgyhCh"},"source":["BATCH_SIZE = 64\n","\n","train_iterator, valid_iterator, test_iterator = BucketIterator.splits(\n","    (train_data, valid_data, test_data),\n","    batch_size=BATCH_SIZE,\n","    sort_within_batch=True,\n","    sort_key=lambda x: len(x.src),\n","    device=device\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a9Tvmd8Ayi_w","executionInfo":{"elapsed":12,"status":"ok","timestamp":1622789367330,"user":{"displayName":"Blitz Striker","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5ROpeT4_n-3EmkBAU-gzRfia2y4oSdzK8x8Zo=s64","userId":"15921332253478855826"},"user_tz":-330},"outputId":"73932ea6-996f-4de0-9f08-ece06e7094c8"},"source":["# sanity check to see if data loader is working\n","x = next(iter(train_iterator))\n","\n","print({'source': x.src[0].shape, 'source_lengths': x.src[1].shape})\n","print({'target': x.trg[0].shape, 'target_lengths': x.trg[1].shape})"],"execution_count":null,"outputs":[{"output_type":"stream","text":["{'source': torch.Size([64, 16]), 'source_lengths': torch.Size([64])}\n","{'target': torch.Size([64, 22]), 'target_lengths': torch.Size([64])}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"RYehNmoUyn7R"},"source":["## Model Architecture\n"]},{"cell_type":"markdown","metadata":{"id":"a27gHIdI_OHR"},"source":["![transformer_model](data:image/png;base64,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 \"Transformer Model\")\n","\n","(source: https://arxiv.org/pdf/1706.03762.pdf)\n"]},{"cell_type":"markdown","metadata":{"id":"etLARHRK3hnu"},"source":["### Scaled Dot-Product Attention\n"]},{"cell_type":"markdown","metadata":{"id":"bOFhzLer_OHX"},"source":["![scaled_dot_product_attention](data:image/png;base64,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 \"Scaled Dot-Product Attention Block\")\n","\n","(source: https://arxiv.org/pdf/1706.03762.pdf)\n"]},{"cell_type":"markdown","metadata":{"id":"SJFYWX8Dduh6"},"source":["> _An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key. (Excerpt from [Attention is All You Need paper](https://arxiv.org/pdf/1706.03762.pdf))_\n","\n","The Transformer uses scaled dot-production attention as a self-attention block to compute the representations by taking the dot product of the query matrix $Q$ and the key matrix $K$ scaled by square root of head dimension ($\\sqrt{d_k}$) followed by applying softmax and multiplying the values matrix $V$.\n","\n","$$\n","\\text{Attention}(Q, K, V) = \\text{softmax}(\\frac{Q K^T}{\\sqrt{d_k}}) V\n","$$\n","\n","The scaled dot-product attention is similar to standard dot-product (multiplicative) attention except that we use a scaling factor $\\sqrt{d_k}$ in order to avoid extremely small gradients.\n","\n","Scaled Dot-Product Attention returns the following:\n","- `output`: weighted sum of the values (self-attention representations) w.r.t alignment scores\n","- `attn_probs`: alignment scores indicating how well values around position $i$ match with values around position $j$\n"]},{"cell_type":"code","metadata":{"id":"NMtdltGo3hkx"},"source":["class ScaledDotProductAttention(nn.Module):\n","    \"\"\" Computes scaled dot product attention\n","    \"\"\"\n","\n","    def __init__(self, scale, dropout_rate):\n","        super(ScaledDotProductAttention, self).__init__()\n","        self.scale = scale\n","        self.dropout_rate = dropout_rate\n","        \n","        self.dropout = nn.Dropout(dropout_rate)\n","\n","\n","    def forward(self, query, key, value, mask=None):\n","        \"\"\" query: (batch_size, n_heads, query_len, head_dim)\n","            key: (batch_size, n_heads, key_len, head_dim)\n","            value: (batch_size, n_heads, value_len, head_dim)\n","            mask: (batch_size, 1, 1, source_seq_len) for source mask\n","                  (batch_size, 1, target_seq_len, target_seq_len) for target mask\n","        \"\"\"\n","        # calculate alignment scores\n","        scores = torch.matmul(query, key.transpose(-2, -1))  # (batch_size, n_heads, query_len, value_len)\n","        scores = scores / self.scale  # (batch_size, num_heads, query_len, value_len)\n","\n","        # mask out invalid positions\n","        if mask is not None:\n","            scores = scores.masked_fill(mask == 0, float('-inf'))  # (batch_size, n_heads, query_len, value_len)\n","\n","        # calculate the attention weights (prob) from alignment scores\n","        attn_probs = F.softmax(scores, dim=-1)  # (batch_size, n_heads, query_len, value_len)\n","        \n","        # calculate context vector\n","        output = torch.matmul(self.dropout(attn_probs), value)  # (batch_size, n_heads, query_len, head_dim)\n","\n","        # output: (batch_size, n_heads, query_len, head_dim)\n","        # attn_probs: (batch_size, n_heads, query_len, value_len)\n","        return output, attn_probs"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C3dw-W7-1fCT"},"source":["### Multi-Head Attention Module\n"]},{"cell_type":"markdown","metadata":{"id":"z4sdE76U_OHZ"},"source":["![multi_head_attention](data:image/png;base64,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 \"Multi-Head Attention Block\")\n","\n","(source: https://arxiv.org/pdf/1706.03762.pdf)\n"]},{"cell_type":"markdown","metadata":{"id":"s3ADbV3D_OHa"},"source":["Instead of using single self-attention layer, the authors introduced multi-head attention mechanism which simply performs multiple scaled dot-product attention computations in parallel. The authors split queries, keys and values with $d_{model}$ dimension into $h$ heads and then re-combine scaled dot-product computation over each head into a single vector with $d_model$ dimension.\n","\n","> _Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. (Excerpt from [Attention is All You Need paper](https://arxiv.org/pdf/1706.03762.pdf))_\n","\n","$$\n","\\text{MultiHead}(Q, K, V) = \\text{Concat}(\\text{head}_1, \\dots, \\text{head}_h) W^O \\\\\n","\\text{where}\\ \\text{head}_i = \\text{Attention}(QW_i^Q, KW_i^K, VW_i^V) \\\\\n","W_i^Q \\in \\mathbb{R}^{\\mathrm{d_{model}\\times d_k}}, W_i^K \\in \\mathbb{R}^{\\mathrm{d_{model}\\times d_k}}, W_i^V \\in \\mathbb{R}^{\\mathrm{d_{model}\\times d_v}}, W_i^O \\in \\mathbb{R}^{\\mathrm{hd_v\\times d_{model}}}\n","$$\n","\n","First, the inputs: query matrix $Q$, key matrix $K$ and value matrix $V$ are linearly projected using $W^Q$, $W^K$ and $W^V$. The projected query $QW^Q$, key $KW^K$ and value $VW^V$ are split into $h$ heads. Scaled dot-product attention is computed for each head $i$. The independent attention head computed are then concatenated and linearly projected using $W^O$.\n","\n","Multi-Head Attention returns the following:\n","- `x`: context vector concatenated over all the attention heads\n","- `attn`: alignment scores (attention weights) over all the attention heads\n"]},{"cell_type":"code","metadata":{"id":"AxYEXnogyq3T"},"source":["class MultiHeadAttention(nn.Module):\n","    \"\"\" Implements Multi-Head Self-Attention proposed by Vaswani et al., 2017.\n","        refer https://arxiv.org/abs/1706.03762\n","    \"\"\"\n","\n","    def __init__(self, d_model, n_heads, dropout_rate=0.1):\n","        super(MultiHeadAttention, self).__init__()\n","        assert d_model % n_heads == 0, \"`d_model` should be a multiple of `n_heads`\"\n","\n","        self.d_model = d_model\n","        self.n_heads = n_heads\n","        self.d_k = self.d_v = d_model // n_heads  # head_dim\n","        self.dropout_rate = dropout_rate\n","\n","        self.W_q = nn.Linear(d_model, d_model, bias=False)\n","        self.W_k = nn.Linear(d_model, d_model, bias=False)\n","        self.W_v = nn.Linear(d_model, d_model, bias=False)\n","        self.W_o = nn.Linear(d_model, d_model)\n","\n","        self.attention = ScaledDotProductAttention(np.sqrt(self.d_k), dropout_rate)\n","    \n","\n","    def split_heads(self, x):\n","        \"\"\" x: (batch_size, seq_len, d_model)\n","        \"\"\"\n","        batch_size = x.size(0)\n","        x = x.view(batch_size, -1, self.n_heads, self.d_k).transpose(1, 2)  # (batch_size, n_heads, seq_len, d_k)\n","\n","        # x: (batch_size, n_heads, seq_len, head_dim)\n","        return x\n","\n","\n","    def group_heads(self, x):\n","        \"\"\" x: (batch_size, n_heads, seq_len, head_dim)\n","        \"\"\"\n","        batch_size = x.size(0)\n","        x = x.transpose(1, 2).contiguous().view(batch_size, -1, self.n_heads * self.d_k)\n","\n","        # x: (batch_size, seq_len, d_model)\n","        return x\n","\n","\n","    def forward(self, query, key, value, mask=None):\n","        \"\"\" query: (batch_size, query_len, d_model)\n","            key: (batch_size, key_len, d_model)\n","            value: (batch_size, value_len, d_model)\n","            mask: (batch_size, 1, source_seq_len) for source mask\n","                  (batch_size, target_seq_len, target_seq_len) for target mask\n","        \"\"\"\n","        # apply linear projections to query, key and value\n","        Q = self.split_heads(self.W_q(query))  # (batch_size, n_heads, query_len, head_dim)\n","        K = self.split_heads(self.W_k(key))  # (batch_size, n_heads, key_len, head_dim)\n","        V = self.split_heads(self.W_v(value))  # (batch_size, n_heads, value_len, head_dim)\n","\n","        if mask is not None:\n","            # apply same mask for all the heads\n","            mask = mask.unsqueeze(1)\n","\n","            # mask: (batch_size, 1, 1, source_seq_len) for source mask\n","            #       (batch_size, 1, target_seq_len, target_seq_len) for target mask\n","        \n","        # calculate attention weights and context vector for each of the heads\n","        x, attn = self.attention(Q, K, V, mask)\n","\n","        # x: (batch_size, n_heads, query_len, head_dim)\n","        # attn: (batch_size, n_heads, query_len, value_len)\n","\n","        # concatenate context vector of all the heads\n","        x = self.group_heads(x)  # (batch_size, query_len, d_model)\n","\n","        # apply linear projection to concatenated context vector\n","        x = self.W_o(x)  # (batch_size, query_len, d_model)\n","\n","        # x: (batch_size, query_len, d_model)\n","        # attn: (batch_size, n_heads, query_len, value_len)\n","        return x, attn"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"tzPAB4hXQRm_"},"source":["### Position-wise Feed-Forward Network Module\n"]},{"cell_type":"markdown","metadata":{"id":"SzggIu52_OHb"},"source":["This is the another block in the encoder as well as decoder layer which transforms from $d_{model}$ to $d_{ff}$ and then again to $d_{model}$. The same linear transformation is applied across different positions. This can also be viewed as two convolutions with filter size 1.\n","\n","$$\n","FFN(x) = max(0, xW_1 + b_1)W_2 + b_2\n","$$\n","\n","Position-wise Feed-Forward Network returns the following:\n","- `x`: non-linearity transformed vector of dimension same as input\n"]},{"cell_type":"code","metadata":{"id":"ys6yPryURAYy"},"source":["class PositionwiseFeedForward(nn.Module):\n","    \"\"\" Implements a two layer feed-forward network.\n","    \"\"\"\n","\n","    def __init__(self, d_model, d_ff, dropout_rate=0.1):\n","        super(PositionwiseFeedForward, self).__init__()\n","        self.d_model = d_model\n","        self.d_ff = d_ff\n","        self.dropout_rate = dropout_rate\n","\n","        self.w_1 = nn.Linear(d_model, d_ff)\n","        self.w_2 = nn.Linear(d_ff, d_model)\n","        self.dropout = nn.Dropout(dropout_rate)\n","    \n","\n","    def forward(self, x):\n","        \"\"\" x: (batch_size, seq_len, d_model)\n","        \"\"\"\n","        x = self.dropout(F.relu(self.w_1(x)))  # (batch_size, seq_len, d_ff)\n","        x = self.w_2(x)  # (batch_size, seq_len, d_model)\n","\n","        # x: (batch_size, seq_len, d_model)\n","        return x"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"mCTP_6UtTiMO"},"source":["### Positional Encoding Module\n"]},{"cell_type":"markdown","metadata":{"id":"KKHM16gt_OHb"},"source":["Since the transformer contains no recurrence and no convolutions, we need some way to inject information about relative or absolute position to assign order to tokens in the sequence. Sinusoidal positional encodings are used with different frequencies to unique represent the order and the position of the token in sequence.\n","\n","$$\n","\\begin{aligned}\n","\\text{PE}_{(pos, 2i)} &= \\text{sin}(\\frac{pos}{10000^{2i/d_{model}}}) \\\\\n","\\text{PE}_{(pos, 2i + 1)} &= \\text{cos}(\\frac{pos}{10000^{2i/d_{model}}})\n","\\end{aligned}\n","$$\n","\n","where $pos$ is the position and $i$ is the dimension.\n","\n","> _We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $PE_{pos+k}$ can be represented as a linear function of $PE_{pos}$. (Excerpt from [Attention is All You Need paper](https://arxiv.org/pdf/1706.03762.pdf))_\n","\n","Positional Encoding Module returns the following:\n","- `x`: word embedding of the token in the sequence with inherent position information\n"]},{"cell_type":"code","metadata":{"id":"L2zKHw9VXl98"},"source":["class PositionalEncoding(nn.Module):\n","    \"\"\" Implements the sinusoidal positional encoding.\n","    \"\"\"\n","\n","    def __init__(self, d_model, dropout_rate=0.1, max_len=5000):\n","        super(PositionalEncoding, self).__init__()\n","        self.d_model = d_model\n","        self.dropout_rate = dropout_rate\n","        self.max_len = max_len\n","\n","        self.dropout = nn.Dropout(dropout_rate)\n","        \n","        # compute positional encodings\n","        pe = torch.zeros(max_len, d_model)  # (max_len, d_model)\n","        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)  # (max_len, 1)\n","        div_term = torch.exp(\n","            torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)\n","        )  # (d_model,)\n","        pe[:, 0::2] = torch.sin(position * div_term)\n","        pe[:, 1::2] = torch.cos(position * div_term)\n","        pe = pe.unsqueeze(0).transpose(0, 1)  # (max_len, 1, d_model)\n","        self.register_buffer('pe', pe)\n","\n","    def forward(self, x):\n","        \"\"\" x: (batch_size, seq_len, d_model)\n","        \"\"\"\n","        x = x + self.pe[:x.size(0), :]  # (batch_size, seq_len, d_model)\n","        x = self.dropout(x)  # (batch_size, seq_len, d_model)\n","\n","        # x: (batch_size, seq_len, d_model)\n","        return x"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"QdEcezZ2a3Al"},"source":["### Encoder Module\n"]},{"cell_type":"markdown","metadata":{"id":"Nv0AskuH_OHc"},"source":["The encoder consists of a stack of $N = 6$ identical layers, each containing two sub-layers, a multi-head self-attention layer, and a position-wise fully connected feed-forward network. Each sub-layer has a residual connection and layer normalization.\n","\n","Encoder returns the following:\n","- `x`: encoded sequence representation using self-attention\n"]},{"cell_type":"code","metadata":{"id":"l3kF1ruSeio4"},"source":["class EncoderLayer(nn.Module):\n","    \"\"\" Encoder is made up of a self-attention layer and a feed-forward layer.\n","    \"\"\"\n","\n","    def __init__(self, d_model, n_heads, d_ff, dropout_rate=0.1):\n","        super(EncoderLayer, self).__init__()\n","        self.d_model = d_model\n","        self.d_ff = d_ff\n","        self.n_heads = n_heads\n","        self.dropout_rate = dropout_rate\n","\n","        self.attn_layer = MultiHeadAttention(d_model, n_heads, dropout_rate)\n","        self.attn_layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n","\n","        self.ff_layer = PositionwiseFeedForward(d_model, d_ff, dropout_rate)\n","        self.ff_layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n","\n","        self.dropout = nn.Dropout(dropout_rate)\n","\n","\n","    def forward(self, x, mask):\n","        \"\"\" x: (batch_size, source_seq_len, d_model)\n","            mask: (batch_size, 1, source_seq_len)\n","        \"\"\"\n","        # apply self-attention\n","        x1, _ = self.attn_layer(x, x, x, mask)  # (batch_size, source_seq_len, d_model)\n","\n","        # apply residual connection followed by layer normalization\n","        x = self.attn_layer_norm(x + self.dropout(x1))  # (batch_size, source_seq_len, d_model)\n","\n","        # apply position-wise feed-forward\n","        x1 = self.ff_layer(x)  # (batch_size, source_seq_len, d_model)\n","\n","        # apply residual connection followed by layer normalization\n","        x = self.ff_layer_norm(x + self.dropout(x1))  # (batch_size, source_seq_len, d_model)\n","\n","        # x: (batch_size, source_seq_len, d_model)\n","        return x"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"2kU4chASmKWt"},"source":["class Encoder(nn.Module):\n","    \"\"\" Encoder block is a stack of N identical encoder layers.\n","    \"\"\"\n","\n","    def __init__(self, vocab_size, d_model, n_layers, n_heads, d_ff, pad_idx, dropout_rate=0.1, max_len=5000):\n","        super(Encoder, self).__init__()\n","        self.vocab_size = vocab_size\n","        self.d_model = d_model\n","        self.n_layers = n_layers\n","        self.d_ff = d_ff\n","        self.pad_idx = pad_idx\n","        self.dropout_rate = dropout_rate\n","        self.max_len = max_len\n","\n","        self.tok_embedding = nn.Embedding(vocab_size, d_model, padding_idx=pad_idx)\n","        self.pos_embedding = PositionalEncoding(d_model, dropout_rate, max_len)\n","        self.layers = nn.ModuleList([\n","            EncoderLayer(d_model, n_heads, d_ff, dropout_rate)\n","            for _ in range(n_layers)\n","        ])\n","        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n","\n","    \n","    def forward(self, x, mask):\n","        \"\"\" x: (batch_size, source_seq_len)\n","            mask: (batch_size, 1, source_seq_len)\n","        \"\"\"\n","        # apply positional encoding to token sequences\n","        x = self.tok_embedding(x)  # (batch_size, source_seq_len, d_model)\n","        x = self.pos_embedding(x)  # (batch_size, source_seq_len, d_model)\n","        \n","        for layer in self.layers:\n","            x = layer(x, mask)  # (batch_size, source_seq_len, d_model)\n","        \n","        x = self.layer_norm(x)  # (batch_size, source_seq_len, d_model)\n","\n","        # x: (batch_size, source_seq_len, d_model)\n","        return x"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-P61cZS6I2bx"},"source":["### Decoder Module\n"]},{"cell_type":"markdown","metadata":{"id":"TljdE_5Q_OHd"},"source":["The decoder is quite similar to the encoder, except that the decoder contains two multi-head self-attention layers instead of a single layer in a stack of $N = 6$ identical layers. The first multi-head self-attention layer attends to decoder outputs generated so far and is masked in order to prevent positions from attending to future positions, whereas the second multi-head self-attention layer attends over the encoder stack output.\n","\n","Decoder returns the following:\n","- `x`: output sequence representation using self-attention\n","- `attn`: alignment scores (attention weights) between input sequence and output sequence\n"]},{"cell_type":"code","metadata":{"id":"IyxqY3wmJAl0"},"source":["class DecoderLayer(nn.Module):\n","    \"\"\" Decoder is made up of a self-attention layer, a encoder-decoder attention \n","        layer and a feed-forward layer.\n","    \"\"\"\n","\n","    def __init__(self, d_model, n_heads, d_ff, dropout_rate=0.1):\n","        super(DecoderLayer, self).__init__()\n","        self.d_model = d_model\n","        self.d_ff = d_ff\n","        self.n_heads = n_heads\n","        self.dropout_rate = dropout_rate\n","\n","        self.attn_layer = MultiHeadAttention(d_model, n_heads, dropout_rate)\n","        self.attn_layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n","\n","        self.enc_attn_layer = MultiHeadAttention(d_model, n_heads, dropout_rate)\n","        self.enc_attn_layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n","\n","        self.ff_layer = PositionwiseFeedForward(d_model, d_ff, dropout_rate)\n","        self.ff_layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n","\n","        self.dropout = nn.Dropout(dropout_rate)\n","\n","    \n","    def forward(self, x, memory, src_mask, tgt_mask):\n","        \"\"\" x: (batch_size, target_seq_len, d_model)\n","            memory: (batch_size, source_seq_len, d_model)\n","            src_mask: (batch_size, 1, source_seq_len)\n","            tgt_mask: (batch_size, target_seq_len, target_seq_len)\n","        \"\"\"\n","        # apply self-attention\n","        x1, _ = self.attn_layer(x, x, x, tgt_mask)  # (batch_size, target_seq_len, d_model)\n","\n","        # apply residual connection followed by layer normalization\n","        x = self.attn_layer_norm(x + self.dropout(x1))  # (batch_size, target_seq_len, d_model)\n","\n","        # apply encoder-decoder attention\n","        # memory is the output from encoder block (encoder states)\n","        x1, attn = self.enc_attn_layer(x, memory, memory, src_mask)\n","\n","        # x1: (batch_size, target_seq_len, d_model)\n","        # attn: (batch_size, n_heads, target_seq_len, source_seq_len)\n","\n","        # apply residual connection followed by layer normalization\n","        x = self.attn_layer_norm(x + self.dropout(x1))  # (batch_size, target_seq_len, d_model)\n","        \n","        # apply position-wise feed-forward\n","        x1 = self.ff_layer(x)  # (batch_size, target_seq_len, d_model)\n","\n","        # apply residual connection followed by layer normalization\n","        x = self.ff_layer_norm(x + self.dropout(x1))  # (batch_size, target_seq_len, d_model)\n","\n","        # x: (batch_size, target_seq_len, d_model)\n","        # attn: (batch_size, n_heads, target_seq_len, source_seq_len)\n","        return x, attn"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Bmaw-X6UQlyt"},"source":["class Decoder(nn.Module):\n","    \"\"\" Decoder block is a stack of N identical decoder layers.\n","    \"\"\"\n","\n","    def __init__(self, vocab_size, d_model, n_layers, n_heads, d_ff, pad_idx, dropout_rate=0.1, max_len=5000):\n","        super(Decoder, self).__init__()\n","        self.vocab_size = vocab_size\n","        self.d_model = d_model\n","        self.n_layers = n_layers\n","        self.d_ff = d_ff\n","        self.pad_idx = pad_idx\n","        self.dropout_rate = dropout_rate\n","        self.max_len = max_len\n","\n","        self.tok_embedding = nn.Embedding(vocab_size, d_model, padding_idx=pad_idx)\n","        self.pos_embedding = PositionalEncoding(d_model, dropout_rate, max_len)\n","        self.layers = nn.ModuleList([\n","            DecoderLayer(d_model, n_heads, d_ff, dropout_rate)\n","            for _ in range(n_layers)\n","        ])\n","        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n","\n","    \n","    def forward(self, x, memory, src_mask, tgt_mask):\n","        \"\"\" x: (batch_size, target_seq_len, d_model)\n","            memory: (batch_size, source_seq_len, d_model)\n","            src_mask: (batch_size, 1, source_seq_len)\n","            tgt_mask: (batch_size, target_seq_len, target_seq_len)\n","        \"\"\"\n","        # apply positional encoding to token sequences\n","        x = self.tok_embedding(x)  # (batch_size, target_seq_len, d_model)\n","        x = self.pos_embedding(x)  # (batch_size, target_seq_len, d_model)\n","\n","        for layer in self.layers:\n","            x, attn = layer(x, memory, src_mask, tgt_mask)  # (batch_size, target_seq_len, d_model)\n","        \n","        x = self.layer_norm(x)  # (batch_size, target_seq_len, d_model)\n","\n","        # x: (batch_size, target_seq_len, d_model)\n","        # attn: (batch_size, n_heads, target_seq_len, source_seq_len)\n","        return x, attn"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ZoOjXM0JPUHn"},"source":["### Transformer\n"]},{"cell_type":"markdown","metadata":{"id":"Az1hRdFJ_OHe"},"source":["The input and output sequences are embedded into a $d_{model}$ dimensional space, which is the usual step before feeding the sequence into the neural network. Positional encoding is also applied to the embedded sequence, which gives a sense of order in the sequence. Encoder encodes the input sequence and generates encoded representation using self-attention mechanism. The decoder uses this representation along with the sequence generated so far to compute the output sequence.\n","\n","The Transformer returns the following:\n","- `output`: output sequence generated\n","- `attn`: alignment scores (attention weights) between input sequence and output sequence\n"]},{"cell_type":"code","metadata":{"id":"lzJTbhWxQL7f"},"source":["class Transformer(nn.Module):\n","    \"\"\" Transformer wrapper for encoder and decoder.\n","    \"\"\"\n","\n","    def __init__(self, encoder, decoder, generator, pad_idx):\n","        super(Transformer, self).__init__()\n","        self.pad_idx = pad_idx\n","\n","        self.encoder = encoder\n","        self.decoder = decoder\n","        self.generator = generator\n","\n","    \n","    def get_pad_mask(self, x, pad_idx):\n","        \"\"\" x: (batch_size, seq_len)\n","        \"\"\"\n","        x = (x != pad_idx).unsqueeze(-2)  # (batch_size, 1, seq_len)\n","\n","        # x: (batch_size, 1, seq_len) \n","        return x\n","\n","    \n","    def get_subsequent_mask(self, x):\n","        \"\"\" x: (batch_size, seq_len)\n","        \"\"\"\n","        seq_len = x.size(1)\n","        subsequent_mask = np.triu(np.ones((1, seq_len, seq_len)), k=1).astype(np.int8)  # (batch_size, seq_len, seq_len)\n","        subsequent_mask = (torch.from_numpy(subsequent_mask) == 0).to(x.device)  # (batch_size, seq_len, seq_len)\n","\n","\n","        # subsequent_mask: (batch_size, seq_len, seq_len)\n","        return subsequent_mask\n","\n","\n","    def forward(self, src, tgt):\n","        \"\"\" src: (batch_size, source_seq_len)\n","            tgt: (batch_size, target_seq_len)\n","        \"\"\"\n","        # create masks for source and target\n","        src_mask = self.get_pad_mask(src, self.pad_idx)\n","        tgt_mask = self.get_pad_mask(tgt, self.pad_idx) & self.get_subsequent_mask(tgt)\n","\n","        # src_mask: (batch_size, 1, seq_len)\n","        # tgt_mask: (batch_size, seq_len, seq_len)\n","\n","        # encode the source sequence\n","        enc_output = self.encoder(src, src_mask)  # (batch_size, source_seq_len, d_model)\n","\n","        # decode based on source sequence and target sequence generated so far\n","        dec_output, attn = self.decoder(tgt, enc_output, src_mask, tgt_mask)\n","\n","        # dec_output: (batch_size, target_seq_len, d_model)\n","        # attn: (batch_size, n_heads, target_seq_len, source_seq_len)\n","\n","        # apply linear projection to obtain the output distribution\n","        output = self.generator(dec_output)  # (batch_size, target_seq_len, vocab_size)\n","\n","        # output: (batch_size, target_seq_len, vocab_size)\n","        # attn: (batch_size, n_heads, target_seq_len, source_seq_len)\n","        return output, attn"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"PlT3p9hxgryW"},"source":["class Generator(nn.Module):\n","    \"\"\" Linear projection layer for generating output distribution.\n","    \"\"\"\n","\n","    def __init__(self, d_model, vocab_size):\n","        super(Generator, self).__init__()\n","        self.proj = nn.Linear(d_model, vocab_size)\n","    \n","\n","    def forward(self, x):\n","        \"\"\" x: (batch_size, target_seq_len, d_model)\n","        \"\"\"\n","        # apply linear projection followed by softmax to obtain output distribution\n","        x = self.proj(x)  # (batch_size, target_seq_len, vocab_size)\n","        output = F.log_softmax(x, dim=-1)  # (batch_size, target_seq_len)\n","\n","        # output: (batch_size, target_seq_len)\n","        return output"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"YgzoRyqeli54"},"source":["## Helper Utilities\n"]},{"cell_type":"code","metadata":{"id":"8JabGcUVlkqc"},"source":["def model_summary(model):\n","    print(model)\n","    print(f'# of trainable params: {sum(p.numel() for p in model.parameters() if p.requires_grad):,}')\n","    print(f'# of non-trainable params: {sum(p.numel() for p in model.parameters() if not p.requires_grad):,}')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ETC1Dzmtvo1p"},"source":["def convert_ids_to_text(ids, vocab, eos_idx, unk_idx):\n","    \"\"\" Converts token ids to text.\n","    \"\"\"\n","    if ids.dim() == 1:\n","        output_tokens = []\n","        for token_id in ids:\n","            if token_id == eos_idx:\n","                break\n","            else:\n","                output_tokens.append(vocab.itos[token_id])\n","        return output_tokens\n","    \n","    elif ids.dim() == 2:\n","        return [convert_ids_to_text(ids[i, :], vocab, eos_idx, unk_idx) for i in range(ids.size(0))]\n","    \n","    raise RuntimeError(f'ids has {ids.size()} dimensions, expected 2 dimensions')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pmwvcO8zpNeT"},"source":["class NoamOptim(object):\n","    \"\"\" Optimizer wrapper for learning rate scheduling.\n","    \"\"\"\n","\n","    def __init__(self, optimizer, d_model, factor, n_warmup_steps):\n","        self.optimizer = optimizer\n","        self.d_model = d_model\n","        self.factor = factor\n","        self.n_warmup_steps = n_warmup_steps\n","        self.n_steps = 0\n","    \n","\n","    def zero_grad(self):\n","        self.optimizer.zero_grad()\n","\n","\n","    def step(self):\n","        self.n_steps += 1\n","        lr = self.get_lr()\n","        for p in self.optimizer.param_groups:\n","            p['lr'] = lr\n","        self.optimizer.step()\n","\n","    \n","    def get_lr(self):\n","        return self.factor * (\n","            self.d_model ** (-0.5)\n","            * min(self.n_steps ** (-0.5), self.n_steps * self.n_warmup_steps ** (-1.5))\n","        )"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"IzJvnYbslp4z"},"source":["## Training\n"]},{"cell_type":"code","metadata":{"id":"NaX8TjkHlx81"},"source":["def train_fn(model, iterator, optimizer, critertion, clip=1.0):\n","    model.train()\n","    total_loss = 0\n","    steps = 0\n","\n","    tk0 = tqdm(iterator, total=len(iterator), position=0, leave=True)\n","\n","    for idx, batch in enumerate(tk0):\n","        source, source_lengths = batch.src\n","        target, target_lengths = batch.trg\n","\n","        # source: (batch_size, source_seq_len), source_lengths: (batch_size)\n","        # target: (batch_size, target_seq_len), target_lengths: (batch_size)\n","        \n","        # forward pass\n","        optimizer.zero_grad()\n","        output, _ = model(source, target[:, :-1])  # (batch_size, target_seq_len - 1, vocab_size)\n","        \n","        # calculate the loss\n","        loss = criterion(\n","            output.view(-1, output.size(-1)),  # (batch_size * (target_seq_len - 1), vocab_size)\n","            target[:, 1:].contiguous().view(-1)  # (batch_size * (target_seq_len - 1))\n","        )\n","        total_loss += loss.item()\n","        steps += 1\n","\n","        output = output.argmax(dim=-1)  # (batch_size, target_seq_len - 1)\n","\n","        # backward pass\n","        loss.backward()\n","\n","        # clip gradients to avoid exploding gradients issue\n","        nn.utils.clip_grad_norm_(model.parameters(), clip)\n","\n","        # update model parameters\n","        optimizer.step()\n","        \n","        tk0.set_postfix(loss=total_loss/steps)\n","\n","    tk0.close()\n","\n","    perplexity = np.exp(total_loss / len(iterator))\n","    \n","    return output, perplexity"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qn-EPKbsvQxH"},"source":["def eval_fn(model, iterator, criterion):\n","    model.eval()\n","    total_loss = 0.0\n","    steps = 0\n","    hypotheses = []\n","    references = []\n","\n","    tk0 = tqdm(iterator, total=len(iterator), position=0, leave=True)\n","\n","    with torch.no_grad():\n","        for idx, batch in enumerate(tk0):\n","            source, source_lengths = batch.src\n","            target, target_lengths = batch.trg\n","\n","            # source: (batch_size, source_seq_len), source_lengths: (batch_size)\n","            # target: (batch_size, target_seq_len), target_lengths: (batch_size)\n","            \n","            # forward pass\n","            optimizer.zero_grad()\n","            output, _ = model(source, target[:, :-1])  # (batch_size, target_seq_len - 1, vocab_size)\n","            \n","            # calculate the loss\n","            loss = criterion(\n","                output.view(-1, output.size(-1)),  # (batch_size * (target_seq_len - 1), vocab_size)\n","                target[:, 1:].contiguous().view(-1)  # (batch_size * (target_seq_len - 1))\n","            )\n","            total_loss += loss.item()\n","            steps += 1\n","\n","            output = output.argmax(dim=-1)  # (batch_size, target_seq_len - 1)\n","            target = target[:, 1:]  # (batch_size, target_seq_len - 1)\n","\n","            # converting the ids to tokens (used later for calculating BLEU score)\n","            pred_tokens = convert_ids_to_text(output, de_text.vocab, EOS_IDX, UNK_IDX)\n","            target_tokens = convert_ids_to_text(target, de_text.vocab, EOS_IDX, UNK_IDX)\n","\n","            hypotheses += pred_tokens\n","            references += [[token] for token in target_tokens]\n","\n","            tk0.set_postfix(loss=total_loss/steps)\n","\n","    tk0.close()\n","\n","    perplexity = np.exp(total_loss / len(iterator))\n","    bleu4 = bleu_score(hypotheses, references)\n","    \n","    return output, perplexity, bleu4"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dR-ZgkGQzSR3"},"source":["# hyperparameters\n","INPUT_SIZE = len(en_text.vocab)  # source vocab size\n","OUTPUT_SIZE = len(de_text.vocab)  # target vocab size\n","HIDDEN_SIZE = 512\n","N_LAYERS = 6\n","N_HEADS = 8\n","FF_SIZE = 2048\n","DROPOUT_RATE = 0.1\n","N_EPOCHS = 10\n","CLIP = 1.0"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-PlQoMNT0_zC","executionInfo":{"elapsed":1166,"status":"ok","timestamp":1622790248511,"user":{"displayName":"Blitz Striker","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5ROpeT4_n-3EmkBAU-gzRfia2y4oSdzK8x8Zo=s64","userId":"15921332253478855826"},"user_tz":-330},"outputId":"56d02b8f-3091-4930-99b2-5aa3ac9ecb08"},"source":["encoder = Encoder(INPUT_SIZE, HIDDEN_SIZE, N_LAYERS, N_HEADS, FF_SIZE, PAD_IDX, DROPOUT_RATE)\n","decoder = Decoder(OUTPUT_SIZE, HIDDEN_SIZE, N_LAYERS, N_HEADS, FF_SIZE, PAD_IDX, DROPOUT_RATE)\n","generator = Generator(HIDDEN_SIZE, OUTPUT_SIZE)\n","\n","model = Transformer(encoder, decoder, generator, PAD_IDX).to(device)\n","model_summary(model)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Transformer(\n","  (encoder): Encoder(\n","    (tok_embedding): Embedding(5893, 512, padding_idx=1)\n","    (pos_embedding): PositionalEncoding(\n","      (dropout): Dropout(p=0.1, inplace=False)\n","    )\n","    (layers): ModuleList(\n","      (0): EncoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","      (1): EncoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","      (2): EncoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","      (3): EncoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","      (4): EncoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","      (5): EncoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","    )\n","    (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","  )\n","  (decoder): Decoder(\n","    (tok_embedding): Embedding(7855, 512, padding_idx=1)\n","    (pos_embedding): PositionalEncoding(\n","      (dropout): Dropout(p=0.1, inplace=False)\n","    )\n","    (layers): ModuleList(\n","      (0): DecoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (enc_attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (enc_attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","      (1): DecoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (enc_attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (enc_attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","      (2): DecoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (enc_attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (enc_attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","      (3): DecoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (enc_attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (enc_attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","      (4): DecoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (enc_attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (enc_attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","      (5): DecoderLayer(\n","        (attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (enc_attn_layer): MultiHeadAttention(\n","          (W_q): Linear(in_features=512, out_features=512, bias=False)\n","          (W_k): Linear(in_features=512, out_features=512, bias=False)\n","          (W_v): Linear(in_features=512, out_features=512, bias=False)\n","          (W_o): Linear(in_features=512, out_features=512, bias=True)\n","          (attention): ScaledDotProductAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (enc_attn_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (ff_layer): PositionwiseFeedForward(\n","          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n","          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ff_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","      )\n","    )\n","    (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n","  )\n","  (generator): Generator(\n","    (proj): Linear(in_features=512, out_features=7855, bias=True)\n","  )\n",")\n","# of trainable params: 55,181,487\n","# of non-trainable params: 0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TWOqbtFNzcU_"},"source":["optimizer = NoamOptim(\n","    optim.Adam(model.parameters(), lr=1e-4, betas=(0.9, 0.98), eps=1e-9),\n","    model.encoder.d_model, 2, 4000\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZbG68m6fzeDY"},"source":["criterion = nn.CrossEntropyLoss(ignore_index=PAD_IDX)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["e8aeb1336fda49548cf376f7f6af922a","e37a41d7c6a848f3ba53af5e91667aa7","a5b62788666d45f0bbef64be24f06252","19fe60eaf96b4332952a78338ff2c22a","01f04d04d39f45fb81c85b89d37f12ec","ee9eaab73c2b4543a70cafdde054d91e","cae6b0a3cfcd49fbb21e1f24aaf9c117","8cdf1c43e67643cab18bbf0ca3eb1e82","69d5b2f7378c4d4eb6c29429ee0e10dc","09df04e92dd142828e63cde2e4b33483","11115d9f0b3d45f7bcedeaa6cbda4b69","ee61bcf289cf42b1a6184da1ca9b1d62","7f71a47637da4f4682f242df6ae337d7","959d1c2efecb400c83c24daf21535cd1","d536c15257bd441388b06de04f50fd1a","bc3af51a16e24f9eb17017920cd54570","4d9cbf8a87bf471e83ff47d39b9e64ea","25f291d1ceb14de0b49e4a7b6b9f604e","4da1e4ae3daf4a6ba56593e75097b95d","03bf4bb6fb994ce4ab865f6dc13d96cf","cd24faed475743ed9c801649d541aec1","c7d8801678ba45a89dd414b755648121","a9347e2f80734cefa5e10d6316547c41","e06bca597e864f4eb5d185bc1b6acd61","4a8ef81a27a341c0b887f089fc75a958","7dbccd8f35a248c0861e961d2eafc201","516af50b6e89499cb5ec15f2687f3b0d","ed5ccb8223f944409aa54d454ecfcfa0","3b3d35d7031f4a399f7281d7fe4bbb2f","0c9e04465e4e4b54abf7aea56d90f826","28267cb9bf1c4f529a366def0e4b3182","bca0b980810d4ead94283d6e81736555","99fbbd2d2c664db1bee2194adcd0be5d","fb67518989ac425992bcd07f9e2f349d","1fb0efe9e65f48579ebf51793ea2ae0a","3b25aeaf60b042d48a99830272453a64","f2ede860d9254525a6490fa4cdaf9bcb","b3c2c4f1e7844fb2a0ebb3188fa4ac38","43bac4a77828440aa0070926b8665c57","0e35363ead8840508aabb126d61b84ec","2a5f3d41bf5541cba5ac75aa0f684db9","fb62961b77664df7a32b3ec53a7a011c","de6ea26bd89e4bb29e9ebc497be11be7","a9401445de46497bb3fb8f4f27a9865c","1fd401531f7d4221a3bc4cfaf5308dc9","6f2ae0fab76440f885572afd806e54b7","631bd193a5e44c9494b2cda41aabf239","bace66df38e54cc6b72a69330bccb256","e20c18acf4a1465b96f9193dc6ece13e","2c4d51b3d86c48d7aa9fbdd0f0ddf866","3a9f8ea2c24c4ccf835d7c75a84044b6","b251a084981d4c69b884ebee357e2c65","b7078e9db8b8443eaa46b997cc2f0736","fea155d07fe74a84ab454de5aeb6085d","dc0d5623dc534cf48a9401b40bb39f34","eb07aec52bbf48709d596b62634db4a4","cbef428b0ddf4a5a9d7064a8d5d902f8","0194315b6461458688d78342f0504cbd","2e9e722708a44e1e990325539685ded5","0a0b80bd60d643cd98af1ba2c49078ab","18235b6f867d4e54a979d5cfb57d600a","06c419efd7444dae8696acf6496129b9","d4116f0ef0a2421d86769f810f008d14","8c7a73dfd5364e459a03db3eb48db05c","0a819e74e7b745bdbd35658db37a92f5","f14bf853129a40639ed2aac2919805a9","8d7c304548fb4ae0a0a2aa14681610f3","993341b769fe4ac682142db7bf39a904","f3eb61310db14fe3b931dd80abdf208e","7b543f755a364bd4811dfee80cf269f7","2c83a2a93ee8410590588f7d8d53f0a2","892d59d27f684c3cb75bd62187cda102","e32af2cd5eb14ab2806e94f23844c48b","d0494fa575f043f596a478472abbaead","479491ba3f904d0ab6a41c0da7d49b01","f2f5198abc1c4dafba4826a12477b94d","94b91f5e2d0f48959cb8ccdb56d4ec01","fd382220e30a42d1ab4b2c02ef8ffab4","1b053268dbd14ae4a8d09419d23eed67","203ff12468bf4b8a93aa0f015e0351ce","898231c431434b70adea4a773975a3b2","2f0b710c09444946bf313f53a4c948ab","8a0348c359d74a77aedcbd4e638e422e","24690fe1c6d34af3a738a23cda53f476","4391a4114c354cc1a8562c284f7119b4","6fea03d1f5284c17bdaae2594f4c39b3","cf10442feb4d4184a44f667111ac34ee","1fdc61c7e31945dfa83e2ed9aeac4ef8","80d277eeb07d43ea9e1e5f43db0dc096","c0ac8b7de2c944378a5b74e9bcb5d954","5f643ef8139b401a914b719e462c6b33","d0afe0617e4c4bff92e246c312860d74","0d7b94c952be4bc59f4f061622321c92","a821acbf979c49cdbc0233de4ec67acf","3eb5c9e90b78423abbbbc96580f0b270","aa316c34d53b492bad4d36c0233a187d","ed0c33858e0e4122afabd055f2edcfa2","3da737b389bb45be9c363cae56376c12","c03279242d524b30b76ab3ca9263ce35","0ae7a22f6ccb4210aaad82aa02b4d111","0c0b6849f7484b35873469a4db32232f","cc33b1a8a65e4b5b821106377fdc278b","e783382140ff42d1b83b4807f69fe59f","4e601e18bcd5496bbef3cb06e0ebbb28","b953ec33ec0641289852c60f72575f5d","cc7b220abefd426b866e844f8cecd29e","b77999e60d3948d9be5fc882681802fe","3c4112beeab14a21a731b0a6d1512b13","8b0b3963eafb4753b807228ec2c66377","dca746d902354cc5946eb4c1cb22cf7b","d61e710e4ac549369ac56718190bb410","af6232cb83c24a739b315f76f2122862","bf6820f63e484c959ace1c107abc5211","a6ff1a8899404eed88d3805e633e5f48","fc0c8156f748403b8b749d0a8dc9c4d8","a440b7baa94143ada7f279810ed27387","0bbc6b0ef69d40abbb317c393b720b03","b5d21132450443efaefbdf4532aefe2c","ba0af9c682a341748e03443cf07bc10f","bf05c9db31a34586bcc2e6be75496405","5499f57947ef478b95041b0325db0826","7a01ffd3c2dd4d85bb774b968f72ffbf","ad603b57272a4b499c36cdc2894412b0","85bb2e410ce74096b001b0787f9ef68c","48ca01b6f5f84ecd96895d04ff2b6b86","fd1ddfa6a5eb49849f4a263e8d8bb3ab","77b3577863814591ad02c7897c8c77ae","ec54a394676449279c6c89b92c1fbd5f"]},"id":"w4C75P1izhEu","executionInfo":{"elapsed":450619,"status":"ok","timestamp":1622790699754,"user":{"displayName":"Blitz Striker","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5ROpeT4_n-3EmkBAU-gzRfia2y4oSdzK8x8Zo=s64","userId":"15921332253478855826"},"user_tz":-330},"outputId":"de4066c5-c661-438b-b65b-8b8a733a5ae6"},"source":["best_bleu4 = float('-inf')\n","es_patience = 3\n","patience = 0\n","model_path = 'model.pth'\n","\n","\n","for epoch in range(0, N_EPOCHS + 1):\n","    # one epoch training\n","    _, train_perplexity = train_fn(model, train_iterator, optimizer, criterion, CLIP)\n","    \n","    # one epoch validation\n","    _, valid_perplexity, valid_bleu4 = eval_fn(model, valid_iterator, criterion)\n","    \n","    print(f'Epoch: {epoch}, Train perplexity: {train_perplexity:.4f}, Valid perplexity: {valid_perplexity:.4f}, Valid BLEU4: {valid_bleu4:.4f}')\n","    \n","    # early stopping\n","    is_best = valid_bleu4 > best_bleu4\n","    if is_best:\n","        print(f'BLEU score improved ({best_bleu4:.4f} -> {valid_bleu4:.4f}). Saving Model!')\n","        best_bleu4 = valid_bleu4\n","        patience = 0\n","        torch.save(model.state_dict(), model_path)\n","    else:\n","        patience += 1\n","        print(f'Early stopping counter: {patience} out of {es_patience}')\n","        if patience == es_patience:\n","            print(f'Early stopping! Best BLEU4: {best_bleu4:.4f}')\n","            break"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e8aeb1336fda49548cf376f7f6af922a","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=454.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"69d5b2f7378c4d4eb6c29429ee0e10dc","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","Epoch: 0, Train perplexity: 140.6567, Valid perplexity: 30.4818, Valid BLEU4: 0.1017\n","BLEU score improved (-inf -> 0.1017). Saving Model!\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4d9cbf8a87bf471e83ff47d39b9e64ea","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=454.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4a8ef81a27a341c0b887f089fc75a958","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","Epoch: 1, Train perplexity: 21.2735, Valid perplexity: 13.0304, Valid BLEU4: 0.1761\n","BLEU score improved (0.1017 -> 0.1761). Saving Model!\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"99fbbd2d2c664db1bee2194adcd0be5d","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=454.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2a5f3d41bf5541cba5ac75aa0f684db9","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","Epoch: 2, Train perplexity: 11.3182, Valid perplexity: 9.0256, Valid BLEU4: 0.2114\n","BLEU score improved (0.1761 -> 0.2114). Saving Model!\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e20c18acf4a1465b96f9193dc6ece13e","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=454.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"cbef428b0ddf4a5a9d7064a8d5d902f8","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","Epoch: 3, Train perplexity: 8.0590, Valid perplexity: 7.5950, Valid BLEU4: 0.2291\n","BLEU score improved (0.2114 -> 0.2291). Saving Model!\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0a819e74e7b745bdbd35658db37a92f5","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=454.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e32af2cd5eb14ab2806e94f23844c48b","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","Epoch: 4, Train perplexity: 6.7525, Valid perplexity: 7.1998, Valid BLEU4: 0.2392\n","BLEU score improved (0.2291 -> 0.2392). Saving Model!\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"898231c431434b70adea4a773975a3b2","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=454.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"80d277eeb07d43ea9e1e5f43db0dc096","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","Epoch: 5, Train perplexity: 6.3664, Valid perplexity: 7.1364, Valid BLEU4: 0.2384\n","Early stopping counter: 1 out of 3\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ed0c33858e0e4122afabd055f2edcfa2","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=454.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b953ec33ec0641289852c60f72575f5d","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","Epoch: 6, Train perplexity: 6.4898, Valid perplexity: 7.8183, Valid BLEU4: 0.2280\n","Early stopping counter: 2 out of 3\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"bf6820f63e484c959ace1c107abc5211","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=454.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5499f57947ef478b95041b0325db0826","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","Epoch: 7, Train perplexity: 7.3691, Valid perplexity: 9.0013, Valid BLEU4: 0.2004\n","Early stopping counter: 3 out of 3\n","Early stopping! Best BLEU4: 0.2392\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":101,"referenced_widgets":["e3db3d5adfc84fff95bc553dbdc39165","b890e392b87c4bb5afa9a2e2db45a0c1","5028b81b0d9d45a48f117de230634c03","993307bf11c54448803e4e4adf4054b6","c88220cfecf24d39b6c7e8e5ece4eb51","cd88947bc69841eebf49208ba8a7ef9c","ac394ee9eb9a4ce99f9c9b8aaed3fd0c","48133b56453d4207978cae101320c061"]},"id":"l97kFO63zil1","executionInfo":{"elapsed":2970,"status":"ok","timestamp":1622790706614,"user":{"displayName":"Blitz Striker","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5ROpeT4_n-3EmkBAU-gzRfia2y4oSdzK8x8Zo=s64","userId":"15921332253478855826"},"user_tz":-330},"outputId":"97610120-582b-471f-aef0-f87e5dc95df0"},"source":["# evaluate the model on test data\n","model.load_state_dict(torch.load(model_path, map_location=device))\n","\n","print('Evaluating the model on test data ...')\n","_, test_perplexity, test_bleu4 = eval_fn(model, test_iterator, criterion)\n","print(f'Test perplexity: {test_perplexity:.4f}, Test BlEU4: {test_bleu4:.4f}')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Evaluating the model on test data ...\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e3db3d5adfc84fff95bc553dbdc39165","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","Test perplexity: 6.9992, Test BlEU4: 0.2432\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ph5Wl2IN1YkY"},"source":["## Inference\n"]},{"cell_type":"code","metadata":{"id":"KmLi7oa41g6J"},"source":["def greedy_decode(model, sentence, max_len=100):\n","    model.eval()\n","\n","    if isinstance(sentence, str):\n","        tokens = [token.text.lower() for token in spacy_de(sentence)]\n","    else:\n","        tokens = [token.lower() for token in sentence]\n","    \n","    token_ids = [SOS_IDX] + [en_text.vocab.stoi.get(token, UNK_TOKEN) for token in tokens] + [EOS_IDX]\n","    \n","    source = torch.tensor(token_ids, dtype=torch.long).unsqueeze(0).to(device)  # (1, source_seq_len)\n","    source_mask = model.get_pad_mask(source, PAD_IDX)  # (1, 1, source_seq_len)\n","\n","    # encode the source sequence\n","    with torch.no_grad():\n","        enc_output = model.encoder(source, source_mask)  # (1, source_seq_len, d_model)\n","    \n","    target_ids = [SOS_IDX]\n","\n","    for i in range(max_len):\n","        target = torch.tensor(target_ids, dtype=torch.long).unsqueeze(0).to(device)  # (1, target_seq_len)\n","        target_mask = model.get_pad_mask(target, PAD_IDX) & model.get_subsequent_mask(target)\n","\n","        # decode the sequence\n","        with torch.no_grad():\n","            dec_output, attn = model.decoder(target, enc_output, source_mask, target_mask)\n","\n","            # dec_output: (1, target_seq_len, d_model)\n","            # attn: (1, n_heads, target_seq_len, source_seq_len)\n","\n","            output = model.generator(dec_output)  # (1, target_seq_len, vocab_size)\n","\n","        target_id = output.argmax(dim=-1)[:, -1].item()\n","        target_ids.append(target_id)\n","\n","        # stop decoding if we encounter EOS_TOKEN or reach the max length\n","        if target_id == EOS_IDX or len(target_ids) >= max_len:\n","            break\n","        \n","    target_tokens = [de_text.vocab.itos[id] for id in target_ids]\n","    attn = attn.squeeze(0).cpu().detach().numpy()  # (n_heads, target_seq_len, source_seq_len)\n","\n","    # target_tokens: list of size (target_seq_len - 1)\n","    # attn: (n_heads, target_seq_len, source_seq_len)\n","    \n","    return target_tokens[1:], attn"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"V_L5dp604b4d"},"source":["def plot_attention_scores(source, target, attention):\n","    n_heads = attention.shape[0]\n","\n","    if isinstance(source, str):\n","        source = [token.lower for token in source.split(\" \")] + [EOS_TOKEN]\n","    else:\n","        source = [token.lower() for token in source] + [EOS_TOKEN]\n","\n","    fig = plt.figure(figsize=(24, 12))\n","\n","    for h, head in enumerate(attention):\n","        ax = fig.add_subplot(2, 4, h + 1)\n","        x = source\n","        y = target if h % 4 == 0 else []\n","\n","        sns.heatmap(\n","            head, xticklabels=x, yticklabels=y, square=True,\n","            vmin=0.0, vmax=1.0, cbar=False, cmap=\"Blues\", ax=ax,\n","        )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":933},"id":"Ri_DP5jmBaLL","executionInfo":{"elapsed":5383,"status":"ok","timestamp":1622790717535,"user":{"displayName":"Blitz Striker","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5ROpeT4_n-3EmkBAU-gzRfia2y4oSdzK8x8Zo=s64","userId":"15921332253478855826"},"user_tz":-330},"outputId":"ffbdeebc-2722-485d-82ed-370910f5aa28"},"source":["example_idx = 7\n","\n","source = vars(train_data.examples[example_idx])['src']\n","target = vars(train_data.examples[example_idx])['trg']\n","\n","predicted, attention_scores = greedy_decode(model, source)\n","\n","print(f'source: {source}\\n')\n","print(f'target: {target}\\n')\n","print(f'predicted: {predicted}\\n')\n","\n","plot_attention_scores(source, predicted, attention_scores)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["source: ['a', 'trendy', 'girl', 'talking', 'on', 'her', 'cellphone', 'while', 'gliding', 'slowly', 'down', 'the', 'street', '.']\n","\n","target: ['ein', 'schickes', 'mädchen', 'spricht', 'mit', 'dem', 'handy', 'während', 'sie', 'langsam', 'die', 'straße', 'entlangschwebt', '.']\n","\n","predicted: ['ein', 'mädchen', 'telefoniert', 'mit', 'ihrem', 'mobiltelefon', ',', 'während', 'ihr', 'handy', 'auf', 'der', 'straße', 'eine', '<unk>', '.', '</s>']\n","\n","\n","\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1728x864 with 8 Axes>"]},"metadata":{"tags":[],"image/png":{"height":740,"width":1434}}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":784},"id":"YVgvi2rMBtB3","executionInfo":{"elapsed":3291,"status":"ok","timestamp":1622790720814,"user":{"displayName":"Blitz Striker","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5ROpeT4_n-3EmkBAU-gzRfia2y4oSdzK8x8Zo=s64","userId":"15921332253478855826"},"user_tz":-330},"outputId":"07cb6619-f2f8-4a2b-b654-96a1e9aa3079"},"source":["example_idx = 24\n","\n","source = vars(valid_data.examples[example_idx])['src']\n","target = vars(valid_data.examples[example_idx])['trg']\n","\n","predicted, attention_scores = greedy_decode(model, source)\n","\n","print(f'source: {source}\\n')\n","print(f'target: {target}\\n')\n","print(f'predicted: {predicted}\\n')\n","\n","plot_attention_scores(source, predicted, attention_scores)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["source: ['construction', 'workers', 'standing', 'on', 'top', 'of', 'a', 'piece', 'of', 'machinery', '.']\n","\n","target: ['bauarbeiter', 'stehen', 'auf', 'einer', 'maschine']\n","\n","predicted: ['bauarbeiter', 'stehen', 'auf', 'einer', 'maschine', 'eines', '<unk>', '.', '</s>']\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1728x864 with 8 Axes>"]},"metadata":{"tags":[],"image/png":{"height":663,"width":1440}}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":781},"id":"pbaiUL_RBvC1","executionInfo":{"elapsed":2796,"status":"ok","timestamp":1622790723528,"user":{"displayName":"Blitz Striker","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5ROpeT4_n-3EmkBAU-gzRfia2y4oSdzK8x8Zo=s64","userId":"15921332253478855826"},"user_tz":-330},"outputId":"d8360564-8ed9-4267-dcb6-db049c41b46d"},"source":["example_idx = 50\n","\n","source = vars(test_data.examples[example_idx])['src']\n","target = vars(test_data.examples[example_idx])['trg']\n","\n","predicted, attention_scores = greedy_decode(model, source)\n","\n","print(f'source: {source}\\n')\n","print(f'target: {target}\\n')\n","print(f'predicted: {predicted}\\n')\n","\n","plot_attention_scores(source, predicted, attention_scores)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["source: ['people', 'walking', 'down', 'sidewalk', 'next', 'to', 'a', 'line', 'of', 'stores', '.']\n","\n","target: ['leute', 'gehen', 'auf', 'einem', 'gehsteig', 'neben', 'einer', 'reihe', 'von', 'geschäften', '.']\n","\n","predicted: ['leute', 'gehen', 'einen', 'gehweg', 'neben', 'einer', 'schlange', 'entlang', '.', '</s>']\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1728x864 with 8 Axes>"]},"metadata":{"tags":[],"image/png":{"height":660,"width":1422}}}]},{"cell_type":"markdown","metadata":{"id":"eOlH6r6ERvGb"},"source":["## References\n","\n","1. [Attention Is All You Need](https://arxiv.org/abs/1706.03762)\n","2. [Łukasz Kaiser’s talk on Attentional Neural Network](https://www.youtube.com/watch?v=rBCqOTEfxvg)\n","3. [The Illustrated Transformer by Jay Alammar](https://jalammar.github.io/illustrated-transformer/)\n","4. [The Annotated Transformer by Harvard NLP Group](https://nlp.seas.harvard.edu/2018/04/03/attention.html)\n","5. [Attention is all you need: A Pytorch Implementation by @jadore801120](https://github.com/jadore801120/attention-is-all-you-need-pytorch)\n","6. [PyTorch Seq2Seq by Ben Trevett](https://github.com/bentrevett/pytorch-seq2seq)\n"]}]}