diff --git "a/joy_caption_alpha_one_jupyter.ipynb" "b/joy_caption_alpha_one_jupyter.ipynb" --- "a/joy_caption_alpha_one_jupyter.ipynb" +++ "b/joy_caption_alpha_one_jupyter.ipynb" @@ -2,13 +2,46 @@ "cells": [ { "cell_type": "code", + "source": [ + "import os\n", + "home_directory = '/content/'\n", + "using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n", + "if using_Kaggle : home_directory = '/kaggle/working/'\n", + "%cd {home_directory}\n", + "\n", + "def my_mkdirs(folder):\n", + " if os.path.exists(folder)==False:\n", + " os.makedirs(folder)\n", + "\n", + "my_mkdirs(f'{home_directory}tmp')" + ], + "metadata": { + "id": "kLmXfHXy80BY", + "outputId": "171efb10-d9ae-479a-be16-7ddf1888bcb2", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": { "id": "VjYy0F2gZIPR", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "9f3cc331-9920-4c50-a2f9-8e2710eeee8d" + "outputId": "c5c49f72-107b-40cb-d180-a0442a5de70c" }, "outputs": [ { @@ -23,7 +56,7 @@ "Need to get 1,513 kB of archives.\n", "After this operation, 5,441 kB of additional disk space will be used.\n", "Selecting previously unselected package libc-ares2:amd64.\n", - "(Reading database ... 123634 files and directories currently installed.)\n", + "(Reading database ... 124565 files and directories currently installed.)\n", "Preparing to unpack .../libc-ares2_1.18.1-1ubuntu0.22.04.3_amd64.deb ...\n", "Unpacking libc-ares2:amd64 (1.18.1-1ubuntu0.22.04.3) ...\n", "Selecting previously unselected package libaria2-0:amd64.\n", @@ -37,37 +70,37 @@ "Setting up aria2 (1.36.0-1) ...\n", "Processing triggers for man-db (2.10.2-1) ...\n", "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n", - "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", "\n", "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libumf.so.0 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n", "\n", "/sbin/ldconfig.real: /usr/local/lib/libtcm.so.1 is not a symbolic link\n", "\n", "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n", "\n", "/sbin/ldconfig.real: /usr/local/lib/libhwloc.so.15 is not a symbolic link\n", "\n", "/sbin/ldconfig.real: /usr/local/lib/libtcm_debug.so.1 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libumf.so.0 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n", - "\n", "\n", "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "ffb219|\u001b[1;32mOK\u001b[0m | 70KiB/s|/content/joy/text_model/adapter_config.json\n", + "63edad|\u001b[1;32mOK\u001b[0m | 49KiB/s|/content/joy/text_model/adapter_config.json\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -75,7 +108,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "718b2f|\u001b[1;32mOK\u001b[0m | 86MiB/s|/content/joy/text_model/adapter_model.safetensors\n", + "c5c8aa|\u001b[1;32mOK\u001b[0m | 110MiB/s|/content/joy/text_model/adapter_model.safetensors\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -83,7 +116,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "bfbd1a|\u001b[1;32mOK\u001b[0m | 185MiB/s|/content/joy/clip_model.pt\n", + "60e2f9|\u001b[1;32mOK\u001b[0m | 159MiB/s|/content/joy/clip_model.pt\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -91,7 +124,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "6caf9b|\u001b[1;32mOK\u001b[0m | 115KiB/s|/content/joy/config.yaml\n", + "a50c87|\u001b[1;32mOK\u001b[0m | 101KiB/s|/content/joy/config.yaml\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -99,40 +132,53 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "4ea39d|\u001b[1;32mOK\u001b[0m | 40MiB/s|/content/joy/image_adapter.pt\n", + "44ace6|\u001b[1;32mOK\u001b[0m | 129MiB/s|/content/joy/image_adapter.pt\n", "\n", "Status Legend:\n", "(OK):download completed.\n", - "Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.14.0)\n", + "Requirement already satisfied: peft in /usr/local/lib/python3.11/dist-packages (0.14.0)\n", "Collecting bitsandbytes\n", " Downloading bitsandbytes-0.45.0-py3-none-manylinux_2_24_x86_64.whl.metadata (2.9 kB)\n", - "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft) (1.26.4)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from peft) (24.2)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft) (5.9.5)\n", - "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from peft) (6.0.2)\n", - "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft) (2.5.1+cu121)\n", - "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from peft) (4.47.1)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft) (4.67.1)\n", - "Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from peft) (1.2.1)\n", - "Requirement 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\u001b[32m69.1/69.1 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: bitsandbytes\n", "Successfully installed bitsandbytes-0.45.0\n" ] @@ -151,514 +197,407 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "id": "EBNKXBwIkJLk", "colab": { "base_uri": "https://localhost:8080/", - "height": 717, + "height": 569, "referenced_widgets": [ - "fe075f28150044278546077d0e3be569", - "1ec3a005495c4370bcc9a7bf46b21f48", - "9e20a5e5ce2e4108ae27946dd1b332d4", - "41370bfaf6c84d57b4f7149c3cc5d909", - "a6e947ca661049e68664bb24f9622ffe", - "227cce7a260c4b8c989070701f156763", - "d87ffbfa27f44da48897df5a6130eb57", - "39119ce0e0d0498fb4cb977607a0ebdc", - "3da7fe56c6694e019b8be2eee3699c11", - "218dd676b86b4e8496a3c5adfc2f6787", - "518b237108de426684556090388105cb", - "3f7b649a98a54d998981aa2f943ce179", - "3017f01947d64a9eb77353a8f6f36771", - "cac9a18629fb47ec94d0868828ce3c52", - 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It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + ":70: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " checkpoint = torch.load(\"/content/joy/clip_model.pt\", map_location='cpu')\n" ] }, { + "output_type": "display_data", "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e0be789c5d4e4b2ab1ed9f21f260f8e0", - "version_major": 2, - "version_minor": 0 - }, "text/plain": [ "tokenizer_config.json: 0%| | 0.00/50.6k [00:00:83: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " image_adapter.load_state_dict(torch.load(\"/content/joy/image_adapter.pt\", map_location=\"cpu\"))\n" - ] + "metadata": {} } ], "source": [ @@ -796,22 +735,23 @@ " if generate_ids[0][-1] == tokenizer.eos_token_id or generate_ids[0][-1] == tokenizer.convert_tokens_to_ids(\"<|eot_id|>\"):\n", " generate_ids = generate_ids[:, :-1]\n", " caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]\n", - " return caption.strip()" + " caption = f'{caption.strip()}'.replace('Prompt: Describe the image in 400 words','')\n", + " return caption" ] }, { "cell_type": "code", "source": [ "suffix = 'png'\n", - "\n", - "for number in range(228):\n", - " try:\n", - " input_image = Image.open(f\"/content/{number+1}.{suffix}\").convert('RGB')\n", - " caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n", - " print(f\"...\\n\\n...caption for {number+1}.{suffix}\\n\\n...\")\n", - " print(caption)\n", - " except:\n", - " continue\n", + "%cd /content/tmp\n", + "for number in range(81):\n", + " input_image = Image.open(f\"/content/{number+1}.{suffix}\").convert('RGB')\n", + " caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n", + " f = open(f\"{number}.txt\", \"w\")\n", + " f.write(f'{caption}')\n", + " f.close()\n", + " print(f\"...\\n\\n...caption for {number+1}.{suffix}\\n\\n...\")\n", + " print(caption)\n", "#----#\n" ], "metadata": { @@ -3004,7 +2944,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "id": "ldMOiaY7kJLp", "colab": { @@ -3801,7 +3741,7 @@ "id": "YVzy1DZMUT1k", "outputId": "5a7e80ee-7783-4c3e-8ae6-516987d7e725" }, - "execution_count": 21, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -3950,7 +3890,7 @@ "id": "iBNWPGbIWzTX", "outputId": "0da75a82-e0ef-46f2-b0c2-8f52dfa6b7fa" }, - "execution_count": 26, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4005,7 +3945,7 @@ "id": "aJ06l10TW0TR", "outputId": "76782d5b-a9a0-4dcf-f20d-eb84256a7216" }, - "execution_count": 10, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4119,7 +4059,7 @@ "id": "EYWVlpPeW0oU", "outputId": "27198d9e-5588-40af-bcd1-465002032548" }, - "execution_count": 11, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4243,7 +4183,7 @@ "id": "1HnMK8WfW07L", "outputId": "a2651707-6c86-4214-afb2-236c4d4e9937" }, - "execution_count": 12, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4361,7 +4301,7 @@ "id": "4l_2a7aSW5BS", "outputId": "54c41d4c-55af-4523-abde-2c458ad73b92" }, - "execution_count": 13, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4481,7 +4421,7 @@ "id": "MB9qKZ3jW5T4", "outputId": "f996af17-e204-407b-d350-6c8346f799af" }, - "execution_count": 14, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4597,7 +4537,7 @@ "id": "zOfslc64W5l4", "outputId": "8bfbfd63-88b9-417a-ba06-d41ac7788ea8" }, - "execution_count": 15, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4717,7 +4657,7 @@ "id": "_shI8ri-W9PT", "outputId": "02191e58-b9bd-4d71-cca2-806b2e34129c" }, - "execution_count": 16, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -5144,7 +5084,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "fe075f28150044278546077d0e3be569": { + "a14e8a4867fb4f4facf8c9476f77387a": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -5159,14 +5099,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_1ec3a005495c4370bcc9a7bf46b21f48", - "IPY_MODEL_9e20a5e5ce2e4108ae27946dd1b332d4", - "IPY_MODEL_41370bfaf6c84d57b4f7149c3cc5d909" + "IPY_MODEL_d5dbf2c92ea5457c82d55ed6a2834f7e", + "IPY_MODEL_d8b457240dc5410e867d26b706805e49", + "IPY_MODEL_f8234bb9f29843f3a5037e6ee64f842c" ], - "layout": "IPY_MODEL_a6e947ca661049e68664bb24f9622ffe" + "layout": "IPY_MODEL_f2d73797838e4a82ac97cf17d3c2c7c5" } }, - "1ec3a005495c4370bcc9a7bf46b21f48": { + "d5dbf2c92ea5457c82d55ed6a2834f7e": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -5181,13 +5121,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_227cce7a260c4b8c989070701f156763", + "layout": "IPY_MODEL_37dbf61b582b404f88c5afedc0c63987", "placeholder": "​", - "style": "IPY_MODEL_d87ffbfa27f44da48897df5a6130eb57", + "style": "IPY_MODEL_906dd072eda14a46a519a1ae3926ebb7", "value": "preprocessor_config.json: 100%" } }, - "9e20a5e5ce2e4108ae27946dd1b332d4": { + "d8b457240dc5410e867d26b706805e49": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -5203,15 +5143,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_39119ce0e0d0498fb4cb977607a0ebdc", + "layout": "IPY_MODEL_310ac4aaa6ab403d9448dc56d8df6097", "max": 368, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_3da7fe56c6694e019b8be2eee3699c11", + "style": "IPY_MODEL_e09a92e17f074d4a88715b2128578a7c", "value": 368 } }, - "41370bfaf6c84d57b4f7149c3cc5d909": { + "f8234bb9f29843f3a5037e6ee64f842c": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -5226,13 +5166,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_218dd676b86b4e8496a3c5adfc2f6787", + "layout": "IPY_MODEL_590a646434d949faa5b5cc10bf51dff0", "placeholder": "​", - "style": "IPY_MODEL_518b237108de426684556090388105cb", - "value": " 368/368 [00:00<00:00, 25.6kB/s]" + "style": "IPY_MODEL_5917ffbfc51b44d29e3df055acdfe338", + "value": " 368/368 [00:00<00:00, 10.8kB/s]" } }, - "a6e947ca661049e68664bb24f9622ffe": { + "f2d73797838e4a82ac97cf17d3c2c7c5": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5284,7 +5224,7 @@ "width": null } }, - "227cce7a260c4b8c989070701f156763": { + "37dbf61b582b404f88c5afedc0c63987": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5336,7 +5276,7 @@ "width": null } }, - "d87ffbfa27f44da48897df5a6130eb57": { + "906dd072eda14a46a519a1ae3926ebb7": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -5351,7 +5291,7 @@ "description_width": "" } }, - "39119ce0e0d0498fb4cb977607a0ebdc": { + "310ac4aaa6ab403d9448dc56d8df6097": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5403,7 +5343,7 @@ "width": null } }, - "3da7fe56c6694e019b8be2eee3699c11": { + "e09a92e17f074d4a88715b2128578a7c": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", @@ -5419,7 +5359,7 @@ "description_width": "" } }, - "218dd676b86b4e8496a3c5adfc2f6787": { + "590a646434d949faa5b5cc10bf51dff0": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5471,7 +5411,7 @@ "width": null } }, - "518b237108de426684556090388105cb": { + "5917ffbfc51b44d29e3df055acdfe338": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -5486,7 +5426,7 @@ "description_width": "" } }, - "3f7b649a98a54d998981aa2f943ce179": { + "bffec348322846eaac6baa99866ea42f": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -5501,14 +5441,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_3017f01947d64a9eb77353a8f6f36771", - "IPY_MODEL_cac9a18629fb47ec94d0868828ce3c52", - "IPY_MODEL_f85238273fc942bf859d7252244ffe68" + "IPY_MODEL_c5e95e071b4541dfa0542324fb5e4fcc", + "IPY_MODEL_0892f71ef770497f9d96e9ebbd750ee8", + "IPY_MODEL_557f626ac56b49e0bcec8ce4d58dc1e9" ], - "layout": "IPY_MODEL_cf8f6acf20f645f59dbfe29578301b3f" + "layout": "IPY_MODEL_e420d824f8cd44ddb97a30f540af9f42" } }, - "3017f01947d64a9eb77353a8f6f36771": { + "c5e95e071b4541dfa0542324fb5e4fcc": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -5523,13 +5463,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_91c098afa5ab4ca0902cb75350a59693", + "layout": "IPY_MODEL_9824644b80e349c6a5f6176c017c76fa", "placeholder": "​", - "style": "IPY_MODEL_166e1f75cd3e4d45a246fd3aadac8d3c", + "style": "IPY_MODEL_bff16f5f53f343ebb87df92b2a3bd8ee", "value": "tokenizer_config.json: 100%" } }, - "cac9a18629fb47ec94d0868828ce3c52": { + "0892f71ef770497f9d96e9ebbd750ee8": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -5545,15 +5485,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_9e7b5907115d443584d7e0257657e7a8", + "layout": "IPY_MODEL_30ab1a70659048e68213bca8cbb2ab31", "max": 711, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_d593e3ba025a45db89417893d581ceaa", + "style": "IPY_MODEL_869cf1db7476484095e6bcbb9bddbfa7", "value": 711 } }, - "f85238273fc942bf859d7252244ffe68": { + "557f626ac56b49e0bcec8ce4d58dc1e9": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -5568,13 +5508,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_caf5429d469345db99249eccd4b8df6e", + "layout": "IPY_MODEL_5cdb1e1e62c94c0f8613a306a36e19e3", "placeholder": "​", - "style": "IPY_MODEL_8313364c9a024282bdd74de6989b1f84", - "value": " 711/711 [00:00<00:00, 34.9kB/s]" + "style": "IPY_MODEL_787deb8451d34459a6957ae003733b32", + "value": " 711/711 [00:00<00:00, 17.3kB/s]" } }, - "cf8f6acf20f645f59dbfe29578301b3f": { + "e420d824f8cd44ddb97a30f540af9f42": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5626,7 +5566,7 @@ "width": null } }, - "91c098afa5ab4ca0902cb75350a59693": { + "9824644b80e349c6a5f6176c017c76fa": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5678,7 +5618,7 @@ "width": null } }, - "166e1f75cd3e4d45a246fd3aadac8d3c": { + "bff16f5f53f343ebb87df92b2a3bd8ee": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -5693,7 +5633,7 @@ "description_width": "" } }, - "9e7b5907115d443584d7e0257657e7a8": { + "30ab1a70659048e68213bca8cbb2ab31": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5745,7 +5685,7 @@ "width": null } }, - "d593e3ba025a45db89417893d581ceaa": { + "869cf1db7476484095e6bcbb9bddbfa7": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", @@ -5761,7 +5701,7 @@ "description_width": "" } }, - "caf5429d469345db99249eccd4b8df6e": { + "5cdb1e1e62c94c0f8613a306a36e19e3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5813,7 +5753,7 @@ "width": null } }, - 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"layout": "IPY_MODEL_1f0c9ccc056749fd8d86ced6c6f58948", + "layout": "IPY_MODEL_3e6147b5eb4b49fa891ad0d2b2b18ead", "placeholder": "​", - "style": "IPY_MODEL_4d8be87dacda487bb0012ae4fcd860d3", + "style": "IPY_MODEL_0345c940dafe4b23be040b1ae28bb4f5", "value": "spiece.model: 100%" } }, - "5af57d61cadb4adbb2c2154e929593bd": { + "061aa843cfd3472eb9173b1afba4f2db": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -5887,15 +5827,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_3f43eefac7b5404199c9de0c27daeb3c", + "layout": "IPY_MODEL_a73a50f721e249559d25efedbe601667", "max": 798330, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_7fef2d863cfb40ca84e356bae2c4de48", + "style": "IPY_MODEL_f1f3d504c2da456a978a4ded9d35008f", "value": 798330 } }, - "d9069e0037fd4385b91dabc6b2a2eca9": { + "b08bd9f20cf04ed9b800ec138dbe04e1": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -5910,13 +5850,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - 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