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2778 |
"Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (1.11.0)\n", |
|
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2779 |
"Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.10.2)\n", |
|
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2780 |
"Requirement already satisfied: gdown in /usr/local/lib/python3.7/dist-packages (3.6.4)\n", |
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2781 |
"Requirement already satisfied: tqdm>=4.42 in /usr/local/lib/python3.7/dist-packages (from datasets) (4.62.0)\n", |
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2782 |
"Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets) (1.1.5)\n", |
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2783 |
"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from datasets) (4.6.4)\n", |
|
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2784 |
"Requirement already satisfied: fsspec>=2021.05.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2021.8.1)\n", |
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2785 |
"Requirement already satisfied: xxhash in /usr/local/lib/python3.7/dist-packages (from datasets) (2.0.2)\n", |
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2786 |
"Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.4)\n", |
|
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2787 |
"Requirement already satisfied: pyarrow!=4.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (3.0.0)\n", |
|
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2788 |
"Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from datasets) (21.0)\n", |
|
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2789 |
"Requirement already satisfied: huggingface-hub<0.1.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.0.16)\n", |
|
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2790 |
"Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2.23.0)\n", |
|
|
2791 |
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.19.5)\n", |
|
|
2792 |
"Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets) (0.70.12.2)\n", |
|
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2793 |
"Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\n", |
|
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2794 |
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<0.1.0->datasets) (3.7.4.3)\n", |
|
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2795 |
"Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->datasets) (2.4.7)\n", |
|
|
2796 |
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (3.0.4)\n", |
|
|
2797 |
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n", |
|
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2798 |
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2021.5.30)\n", |
|
|
2799 |
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2.10)\n", |
|
|
2800 |
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (5.4.1)\n", |
|
|
2801 |
"Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.45)\n", |
|
|
2802 |
"Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)\n", |
|
|
2803 |
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n", |
|
|
2804 |
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from gdown) (1.15.0)\n", |
|
|
2805 |
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.5.0)\n", |
|
|
2806 |
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2018.9)\n", |
|
|
2807 |
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n", |
|
|
2808 |
"Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n", |
|
|
2809 |
"Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n" |
|
|
2810 |
] |
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2811 |
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] |
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}, |
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2814 |
{ |
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"cell_type": "code", |
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2816 |
"metadata": { |
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2817 |
"id": "jMpji8WE8ERV" |
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2818 |
}, |
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2819 |
"source": [ |
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|
2820 |
"import transformers\n", |
|
|
2821 |
"from transformers import AutoTokenizer, AutoModelForQuestionAnswering, TrainingArguments, Trainer, default_data_collator\n", |
|
|
2822 |
"from datasets import load_dataset, load_metric" |
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|
2823 |
], |
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2824 |
"execution_count": null, |
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2825 |
"outputs": [] |
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2826 |
}, |
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2827 |
{ |
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2828 |
"cell_type": "code", |
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2829 |
"metadata": { |
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2830 |
"colab": { |
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2831 |
"base_uri": "https://localhost:8080/" |
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2832 |
}, |
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|
2833 |
"id": "TIbI5xnn_XvN", |
|
|
2834 |
"outputId": "48647dcf-144c-4689-b6c0-3a695f8091c6" |
|
|
2835 |
}, |
|
|
2836 |
"source": [ |
|
|
2837 |
"from google.colab import drive\n", |
|
|
2838 |
"drive.mount('/content/drive')" |
|
|
2839 |
], |
|
|
2840 |
"execution_count": null, |
|
|
2841 |
"outputs": [ |
|
|
2842 |
{ |
|
|
2843 |
"output_type": "stream", |
|
|
2844 |
"name": "stdout", |
|
|
2845 |
"text": [ |
|
|
2846 |
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" |
|
|
2847 |
] |
|
|
2848 |
} |
|
|
2849 |
] |
|
|
2850 |
}, |
|
|
2851 |
{ |
|
|
2852 |
"cell_type": "code", |
|
|
2853 |
"metadata": { |
|
|
2854 |
"colab": { |
|
|
2855 |
"base_uri": "https://localhost:8080/" |
|
|
2856 |
}, |
|
|
2857 |
"id": "5CnAFQ3DAzaJ", |
|
|
2858 |
"outputId": "4d8dfa4a-9fe3-45e0-c144-a777f408e8a5" |
|
|
2859 |
}, |
|
|
2860 |
"source": [ |
|
|
2861 |
"!gdown https://drive.google.com/uc?id=10663oMohQRDuNCQ0REMIeY8IAKoCiK3A" |
|
|
2862 |
], |
|
|
2863 |
"execution_count": null, |
|
|
2864 |
"outputs": [ |
|
|
2865 |
{ |
|
|
2866 |
"output_type": "stream", |
|
|
2867 |
"name": "stdout", |
|
|
2868 |
"text": [ |
|
|
2869 |
"Downloading...\n", |
|
|
2870 |
"From: https://drive.google.com/uc?id=10663oMohQRDuNCQ0REMIeY8IAKoCiK3A\n", |
|
|
2871 |
"To: /content/checkpoint-final-mlm-albert.zip\n", |
|
|
2872 |
"42.2MB [00:00, 134MB/s] \n" |
|
|
2873 |
] |
|
|
2874 |
} |
|
|
2875 |
] |
|
|
2876 |
}, |
|
|
2877 |
{ |
|
|
2878 |
"cell_type": "code", |
|
|
2879 |
"metadata": { |
|
|
2880 |
"colab": { |
|
|
2881 |
"base_uri": "https://localhost:8080/" |
|
|
2882 |
}, |
|
|
2883 |
"id": "lddjlLEZA2nu", |
|
|
2884 |
"outputId": "03e069f5-4d1d-46e7-939a-858764a20fd7" |
|
|
2885 |
}, |
|
|
2886 |
"source": [ |
|
|
2887 |
"!unzip checkpoint-final-mlm-albert.zip" |
|
|
2888 |
], |
|
|
2889 |
"execution_count": null, |
|
|
2890 |
"outputs": [ |
|
|
2891 |
{ |
|
|
2892 |
"output_type": "stream", |
|
|
2893 |
"name": "stdout", |
|
|
2894 |
"text": [ |
|
|
2895 |
"Archive: checkpoint-final-mlm-albert.zip\n", |
|
|
2896 |
"replace drive/MyDrive/albert-mlm/checkpoint-final-mlm-albert/config.json? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n" |
|
|
2897 |
] |
|
|
2898 |
} |
|
|
2899 |
] |
|
|
2900 |
}, |
|
|
2901 |
{ |
|
|
2902 |
"cell_type": "code", |
|
|
2903 |
"metadata": { |
|
|
2904 |
"colab": { |
|
|
2905 |
"base_uri": "https://localhost:8080/" |
|
|
2906 |
}, |
|
|
2907 |
"id": "6dBfG_vVAt9i", |
|
|
2908 |
"outputId": "ec5e1985-d8de-4de0-d1af-b35563b45765" |
|
|
2909 |
}, |
|
|
2910 |
"source": [ |
|
|
2911 |
"!ls drive/MyDrive/albert-mlm/checkpoint-final-mlm-albert" |
|
|
2912 |
], |
|
|
2913 |
"execution_count": null, |
|
|
2914 |
"outputs": [ |
|
|
2915 |
{ |
|
|
2916 |
"output_type": "stream", |
|
|
2917 |
"name": "stdout", |
|
|
2918 |
"text": [ |
|
|
2919 |
"all_results.json special_tokens_map.json training_args.bin\n", |
|
|
2920 |
"config.json\t tokenizer_config.json train_results.json\n", |
|
|
2921 |
"eval_results.json tokenizer.json\n", |
|
|
2922 |
"pytorch_model.bin trainer_state.json\n" |
|
|
2923 |
] |
|
|
2924 |
} |
|
|
2925 |
] |
|
|
2926 |
}, |
|
|
2927 |
{ |
|
|
2928 |
"cell_type": "code", |
|
|
2929 |
"metadata": { |
|
|
2930 |
"id": "sw5JuNBI8c8p" |
|
|
2931 |
}, |
|
|
2932 |
"source": [ |
|
|
2933 |
"max_length = 384 \n", |
|
|
2934 |
"doc_stride = 128 \n", |
|
|
2935 |
"model_checkpoint = \"drive/MyDrive/albert-mlm/checkpoint-final-mlm-albert\"" |
|
|
2936 |
], |
|
|
2937 |
"execution_count": null, |
|
|
2938 |
"outputs": [] |
|
|
2939 |
}, |
|
|
2940 |
{ |
|
|
2941 |
"cell_type": "code", |
|
|
2942 |
"metadata": { |
|
|
2943 |
"colab": { |
|
|
2944 |
"base_uri": "https://localhost:8080/", |
|
|
2945 |
"height": 290, |
|
|
2946 |
"referenced_widgets": [ |
|
|
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"174671e8e293450dadde235f2ee1e467", |
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|
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"0a1b4bdb26fb4aa38e456c35ca9c3fda", |
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|
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"4d26d276fc104474afa08d7fb01a6137", |
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"425b980f131c4524bd3d425e6898862a", |
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"80d7f12578e04833af509cf682375353", |
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"cdcbc5ad85d7426ea076419edadfde61", |
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"6bddf6c3baa347bcaa2a634c36f774f1", |
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"d2b2b3211ed5444c82527f117ea11f4f", |
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"784d735bf0504ce59e658fa3702ca7a9", |
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"78e3c4a562ca496e82637989e0f9865c", |
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"bbadcf3795a7444ba09f11ea3fd0b09a", |
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|
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"81721e2b8715454ba2275974eb046260", |
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|
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"e62a20d046944b6bbf71b8f013cacb51", |
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"fd46c5e6d6f54c4ea61b010d0d3d1e6d", |
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|
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"286f9106064a42789e32f959dd23a076", |
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|
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"0bbe258bac2a4bebb73f6856d3eb569f", |
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|
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"66080bd94e014bba9d6518b14a674bb0", |
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|
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"6839ed416b694a6f959629b03d82d058", |
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"d6627201960640efb3109eb811fcf78f", |
|
|
3008 |
"e1e4eb94b2944c1785c9df1955287264", |
|
|
3009 |
"edf598037d2d4d369de10323811501be", |
|
|
3010 |
"04fce2d7fabb4e12ab1247ca07c7224e", |
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|
3011 |
"3d37e021532c4acb930281cf3ed03151", |
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|
3012 |
"1e9b9f7f29324c9aaa652fb1cda8d9e1" |
|
|
3013 |
] |
|
|
3014 |
}, |
|
|
3015 |
"id": "vlnhdtQp9E6w", |
|
|
3016 |
"outputId": "0610fa28-830a-426a-f6d5-af3045372433" |
|
|
3017 |
}, |
|
|
3018 |
"source": [ |
|
|
3019 |
"datasets = load_dataset(\"squad_v2\")\n", |
|
|
3020 |
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n", |
|
|
3021 |
"pad_on_right = tokenizer.padding_side == \"right\"\n", |
|
|
3022 |
"model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)" |
|
|
3023 |
], |
|
|
3024 |
"execution_count": null, |
|
|
3025 |
"outputs": [ |
|
|
3026 |
{ |
|
|
3027 |
"output_type": "display_data", |
|
|
3028 |
"data": { |
|
|
3029 |
"application/vnd.jupyter.widget-view+json": { |
|
|
3030 |
"model_id": "96f412ee972242ccb6d651c3cc8b6e5a", |
|
|
3031 |
"version_minor": 0, |
|
|
3032 |
"version_major": 2 |
|
|
3033 |
}, |
|
|
3034 |
"text/plain": [ |
|
|
3035 |
"Downloading: 0%| | 0.00/1.87k [00:00<?, ?B/s]" |
|
|
3036 |
] |
|
|
3037 |
}, |
|
|
3038 |
"metadata": {} |
|
|
3039 |
}, |
|
|
3040 |
{ |
|
|
3041 |
"output_type": "display_data", |
|
|
3042 |
"data": { |
|
|
3043 |
"application/vnd.jupyter.widget-view+json": { |
|
|
3044 |
"model_id": "5d27eab2202a48319faf42b9de85ebad", |
|
|
3045 |
"version_minor": 0, |
|
|
3046 |
"version_major": 2 |
|
|
3047 |
}, |
|
|
3048 |
"text/plain": [ |
|
|
3049 |
"Downloading: 0%| | 0.00/1.02k [00:00<?, ?B/s]" |
|
|
3050 |
] |
|
|
3051 |
}, |
|
|
3052 |
"metadata": {} |
|
|
3053 |
}, |
|
|
3054 |
{ |
|
|
3055 |
"output_type": "stream", |
|
|
3056 |
"name": "stdout", |
|
|
3057 |
"text": [ |
|
|
3058 |
"Downloading and preparing dataset squad_v2/squad_v2 (download: 44.34 MiB, generated: 122.41 MiB, post-processed: Unknown size, total: 166.75 MiB) to /root/.cache/huggingface/datasets/squad_v2/squad_v2/2.0.0/09187c73c1b837c95d9a249cd97c2c3f1cebada06efe667b4427714b27639b1d...\n" |
|
|
3059 |
] |
|
|
3060 |
}, |
|
|
3061 |
{ |
|
|
3062 |
"output_type": "display_data", |
|
|
3063 |
"data": { |
|
|
3064 |
"application/vnd.jupyter.widget-view+json": { |
|
|
3065 |
"model_id": "307dd3c4bb8940878c96e9a559e4f8c5", |
|
|
3066 |
"version_minor": 0, |
|
|
3067 |
"version_major": 2 |
|
|
3068 |
}, |
|
|
3069 |
"text/plain": [ |
|
|
3070 |
"Downloading: 0%| | 0.00/9.55M [00:00<?, ?B/s]" |
|
|
3071 |
] |
|
|
3072 |
}, |
|
|
3073 |
"metadata": {} |
|
|
3074 |
}, |
|
|
3075 |
{ |
|
|
3076 |
"output_type": "display_data", |
|
|
3077 |
"data": { |
|
|
3078 |
"application/vnd.jupyter.widget-view+json": { |
|
|
3079 |
"model_id": "c39cdd8823ec43adb768efe255968373", |
|
|
3080 |
"version_minor": 0, |
|
|
3081 |
"version_major": 2 |
|
|
3082 |
}, |
|
|
3083 |
"text/plain": [ |
|
|
3084 |
"Downloading: 0%| | 0.00/801k [00:00<?, ?B/s]" |
|
|
3085 |
] |
|
|
3086 |
}, |
|
|
3087 |
"metadata": {} |
|
|
3088 |
}, |
|
|
3089 |
{ |
|
|
3090 |
"output_type": "display_data", |
|
|
3091 |
"data": { |
|
|
3092 |
"application/vnd.jupyter.widget-view+json": { |
|
|
3093 |
"model_id": "80d7f12578e04833af509cf682375353", |
|
|
3094 |
"version_minor": 0, |
|
|
3095 |
"version_major": 2 |
|
|
3096 |
}, |
|
|
3097 |
"text/plain": [ |
|
|
3098 |
"0 examples [00:00, ? examples/s]" |
|
|
3099 |
] |
|
|
3100 |
}, |
|
|
3101 |
"metadata": {} |
|
|
3102 |
}, |
|
|
3103 |
{ |
|
|
3104 |
"output_type": "display_data", |
|
|
3105 |
"data": { |
|
|
3106 |
"application/vnd.jupyter.widget-view+json": { |
|
|
3107 |
"model_id": "fd46c5e6d6f54c4ea61b010d0d3d1e6d", |
|
|
3108 |
"version_minor": 0, |
|
|
3109 |
"version_major": 2 |
|
|
3110 |
}, |
|
|
3111 |
"text/plain": [ |
|
|
3112 |
"0 examples [00:00, ? examples/s]" |
|
|
3113 |
] |
|
|
3114 |
}, |
|
|
3115 |
"metadata": {} |
|
|
3116 |
}, |
|
|
3117 |
{ |
|
|
3118 |
"output_type": "stream", |
|
|
3119 |
"name": "stdout", |
|
|
3120 |
"text": [ |
|
|
3121 |
"Dataset squad_v2 downloaded and prepared to /root/.cache/huggingface/datasets/squad_v2/squad_v2/2.0.0/09187c73c1b837c95d9a249cd97c2c3f1cebada06efe667b4427714b27639b1d. Subsequent calls will reuse this data.\n" |
|
|
3122 |
] |
|
|
3123 |
}, |
|
|
3124 |
{ |
|
|
3125 |
"output_type": "stream", |
|
|
3126 |
"name": "stderr", |
|
|
3127 |
"text": [ |
|
|
3128 |
"Some weights of the model checkpoint at drive/MyDrive/albert-mlm/checkpoint-final-mlm-albert were not used when initializing AlbertForQuestionAnswering: ['predictions.decoder.bias', 'predictions.decoder.weight', 'predictions.LayerNorm.weight', 'predictions.dense.weight', 'predictions.LayerNorm.bias', 'predictions.bias', 'predictions.dense.bias']\n", |
|
|
3129 |
"- This IS expected if you are initializing AlbertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", |
|
|
3130 |
"- This IS NOT expected if you are initializing AlbertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", |
|
|
3131 |
"Some weights of AlbertForQuestionAnswering were not initialized from the model checkpoint at drive/MyDrive/albert-mlm/checkpoint-final-mlm-albert and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias']\n", |
|
|
3132 |
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" |
|
|
3133 |
] |
|
|
3134 |
} |
|
|
3135 |
] |
|
|
3136 |
}, |
|
|
3137 |
{ |
|
|
3138 |
"cell_type": "code", |
|
|
3139 |
"metadata": { |
|
|
3140 |
"id": "u9PNvMvc9nCR" |
|
|
3141 |
}, |
|
|
3142 |
"source": [ |
|
|
3143 |
"def prepare_train_features(examples):\n", |
|
|
3144 |
" examples[\"question\"] = [q.lstrip() for q in examples[\"question\"]]\n", |
|
|
3145 |
"\n", |
|
|
3146 |
" tokenized_examples = tokenizer(\n", |
|
|
3147 |
" examples[\"question\" if pad_on_right else \"context\"],\n", |
|
|
3148 |
" examples[\"context\" if pad_on_right else \"question\"],\n", |
|
|
3149 |
" truncation=\"only_second\" if pad_on_right else \"only_first\",\n", |
|
|
3150 |
" max_length=max_length,\n", |
|
|
3151 |
" stride=doc_stride,\n", |
|
|
3152 |
" return_overflowing_tokens=True,\n", |
|
|
3153 |
" return_offsets_mapping=True,\n", |
|
|
3154 |
" padding=\"max_length\",\n", |
|
|
3155 |
" )\n", |
|
|
3156 |
"\n", |
|
|
3157 |
" sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n", |
|
|
3158 |
" offset_mapping = tokenized_examples.pop(\"offset_mapping\")\n", |
|
|
3159 |
"\n", |
|
|
3160 |
" tokenized_examples[\"start_positions\"] = []\n", |
|
|
3161 |
" tokenized_examples[\"end_positions\"] = []\n", |
|
|
3162 |
"\n", |
|
|
3163 |
" for i, offsets in enumerate(offset_mapping):\n", |
|
|
3164 |
" input_ids = tokenized_examples[\"input_ids\"][i]\n", |
|
|
3165 |
" cls_index = input_ids.index(tokenizer.cls_token_id)\n", |
|
|
3166 |
"\n", |
|
|
3167 |
" sequence_ids = tokenized_examples.sequence_ids(i)\n", |
|
|
3168 |
"\n", |
|
|
3169 |
" sample_index = sample_mapping[i]\n", |
|
|
3170 |
" answers = examples[\"answers\"][sample_index]\n", |
|
|
3171 |
"\n", |
|
|
3172 |
" if len(answers[\"answer_start\"]) == 0:\n", |
|
|
3173 |
" tokenized_examples[\"start_positions\"].append(cls_index)\n", |
|
|
3174 |
" tokenized_examples[\"end_positions\"].append(cls_index)\n", |
|
|
3175 |
" else:\n", |
|
|
3176 |
" start_char = answers[\"answer_start\"][0]\n", |
|
|
3177 |
" end_char = start_char + len(answers[\"text\"][0])\n", |
|
|
3178 |
"\n", |
|
|
3179 |
" token_start_index = 0\n", |
|
|
3180 |
" while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\n", |
|
|
3181 |
" token_start_index += 1\n", |
|
|
3182 |
"\n", |
|
|
3183 |
" token_end_index = len(input_ids) - 1\n", |
|
|
3184 |
" while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\n", |
|
|
3185 |
" token_end_index -= 1\n", |
|
|
3186 |
"\n", |
|
|
3187 |
" if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\n", |
|
|
3188 |
" tokenized_examples[\"start_positions\"].append(cls_index)\n", |
|
|
3189 |
" tokenized_examples[\"end_positions\"].append(cls_index)\n", |
|
|
3190 |
" else:\n", |
|
|
3191 |
" while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\n", |
|
|
3192 |
" token_start_index += 1\n", |
|
|
3193 |
" tokenized_examples[\"start_positions\"].append(token_start_index - 1)\n", |
|
|
3194 |
" while offsets[token_end_index][1] >= end_char:\n", |
|
|
3195 |
" token_end_index -= 1\n", |
|
|
3196 |
" tokenized_examples[\"end_positions\"].append(token_end_index + 1)\n", |
|
|
3197 |
"\n", |
|
|
3198 |
" return tokenized_examples" |
|
|
3199 |
], |
|
|
3200 |
"execution_count": null, |
|
|
3201 |
"outputs": [] |
|
|
3202 |
}, |
|
|
3203 |
{ |
|
|
3204 |
"cell_type": "code", |
|
|
3205 |
"metadata": { |
|
|
3206 |
"id": "JrwtE3WhD3-D" |
|
|
3207 |
}, |
|
|
3208 |
"source": [ |
|
|
3209 |
"# !rm -rf drive/MyDrive/albert-qa" |
|
|
3210 |
], |
|
|
3211 |
"execution_count": null, |
|
|
3212 |
"outputs": [] |
|
|
3213 |
}, |
|
|
3214 |
{ |
|
|
3215 |
"cell_type": "code", |
|
|
3216 |
"metadata": { |
|
|
3217 |
"id": "6m4wTO48_xnt" |
|
|
3218 |
}, |
|
|
3219 |
"source": [ |
|
|
3220 |
"!mkdir -p drive/MyDrive/albert-qa" |
|
|
3221 |
], |
|
|
3222 |
"execution_count": null, |
|
|
3223 |
"outputs": [] |
|
|
3224 |
}, |
|
|
3225 |
{ |
|
|
3226 |
"cell_type": "code", |
|
|
3227 |
"metadata": { |
|
|
3228 |
"colab": { |
|
|
3229 |
"base_uri": "https://localhost:8080/" |
|
|
3230 |
}, |
|
|
3231 |
"id": "DuMBJxFV_gLF", |
|
|
3232 |
"outputId": "e2007da3-b0ab-4f40-a55b-2868c7a1ce05" |
|
|
3233 |
}, |
|
|
3234 |
"source": [ |
|
|
3235 |
"!ls drive/MyDrive/" |
|
|
3236 |
], |
|
|
3237 |
"execution_count": null, |
|
|
3238 |
"outputs": [ |
|
|
3239 |
{ |
|
|
3240 |
"output_type": "stream", |
|
|
3241 |
"name": "stdout", |
|
|
3242 |
"text": [ |
|
|
3243 |
" albert-mlm\t\t\t MirasText.zip\n", |
|
|
3244 |
" albert-qa\t\t\t model\n", |
|
|
3245 |
" all_sentences.txt\t\t qa.json\n", |
|
|
3246 |
" bert-base-finetuned\t\t qa_static.csv\n", |
|
|
3247 |
" bert-large-finetuned\t\t runs\n", |
|
|
3248 |
" cached_train_checkpoint-final_384 titles_word2vec.model\n", |
|
|
3249 |
"'Colab Notebooks'\t\t transformers\n", |
|
|
3250 |
" documents_word2vec.model\t 'Untitled document.gdoc'\n", |
|
|
3251 |
" drug_dataset.csv\t\t تایمشیت.gsheet\n" |
|
|
3252 |
] |
|
|
3253 |
} |
|
|
3254 |
] |
|
|
3255 |
}, |
|
|
3256 |
{ |
|
|
3257 |
"cell_type": "code", |
|
|
3258 |
"metadata": { |
|
|
3259 |
"id": "0DwWS3JO-lJt" |
|
|
3260 |
}, |
|
|
3261 |
"source": [ |
|
|
3262 |
"model_name = model_checkpoint.split(\"/\")[-1]\n", |
|
|
3263 |
"\n", |
|
|
3264 |
"args = TrainingArguments(\n", |
|
|
3265 |
" evaluation_strategy = \"epoch\",\n", |
|
|
3266 |
" learning_rate=2e-5,\n", |
|
|
3267 |
" per_device_train_batch_size=16,\n", |
|
|
3268 |
" num_train_epochs=2,\n", |
|
|
3269 |
" weight_decay=0.01,\n", |
|
|
3270 |
" save_steps=500,\n", |
|
|
3271 |
" resume_from_checkpoint=True,\n", |
|
|
3272 |
" output_dir='drive/MyDrive/albert-qa',\n", |
|
|
3273 |
" do_train=True,\n", |
|
|
3274 |
" do_eval=False,\n", |
|
|
3275 |
" save_total_limit=3\n", |
|
|
3276 |
")" |
|
|
3277 |
], |
|
|
3278 |
"execution_count": null, |
|
|
3279 |
"outputs": [] |
|
|
3280 |
}, |
|
|
3281 |
{ |
|
|
3282 |
"cell_type": "code", |
|
|
3283 |
"metadata": { |
|
|
3284 |
"id": "hSVJuMSB_DiV", |
|
|
3285 |
"colab": { |
|
|
3286 |
"base_uri": "https://localhost:8080/", |
|
|
3287 |
"height": 81, |
|
|
3288 |
"referenced_widgets": [ |
|
|
3289 |
"27cb5ccf2c9249c389ea9218af245274", |
|
|
3290 |
"b83dcd8153c14fb4b2ea3158b72c396a", |
|
|
3291 |
"1c1fef0ee40b4c3bb3f8e55d256c2b33", |
|
|
3292 |
"6298a9bdaf0248bb84afd55ac8b3a6ae", |
|
|
3293 |
"008edff18cc6412d96bda4cddb555ff8", |
|
|
3294 |
"242b2460846b4f72802c4dfcd2f45620", |
|
|
3295 |
"ac6081e4d788442f8f48ab386065a145", |
|
|
3296 |
"72c8117947a449e8910ac9d59d73b8f7", |
|
|
3297 |
"d7ca4d84903449baa1607a50e987a3c9", |
|
|
3298 |
"af9d41d2a2004c24b8bea89bf59f3c10", |
|
|
3299 |
"1d5dd5d69064453bb91f237c1a3f4104", |
|
|
3300 |
"8637f3a9e37a4f0eab2eaba0543e60ae", |
|
|
3301 |
"2f14543841684e589c8964f497afbe3b", |
|
|
3302 |
"c298f0ac2c1e4b1393a2b4151f6a5b86", |
|
|
3303 |
"ae6e62e28365467194b8d490de67bb47", |
|
|
3304 |
"a88dcd0696d849d9b2e23484e8e4c342", |
|
|
3305 |
"3ad5943a7a3d4a158a4bd682550126f2", |
|
|
3306 |
"bba1fdb19b854852ac04b4f59d4da185", |
|
|
3307 |
"af345e1fd5414deca969d5ba7b1c582f", |
|
|
3308 |
"971fa5cc540f40a2b699390c3ee7c4ec", |
|
|
3309 |
"252cca28b41a48a0b6e011370da46a23", |
|
|
3310 |
"d6f1e6fee5774871ae1d862612cee6c6" |
|
|
3311 |
] |
|
|
3312 |
}, |
|
|
3313 |
"outputId": "4b29c0d0-4de8-47a2-8bdd-4f0ca10ceea1" |
|
|
3314 |
}, |
|
|
3315 |
"source": [ |
|
|
3316 |
"tokenized_datasets = datasets.map(prepare_train_features, batched=True, remove_columns=datasets[\"train\"].column_names)" |
|
|
3317 |
], |
|
|
3318 |
"execution_count": null, |
|
|
3319 |
"outputs": [ |
|
|
3320 |
{ |
|
|
3321 |
"output_type": "display_data", |
|
|
3322 |
"data": { |
|
|
3323 |
"application/vnd.jupyter.widget-view+json": { |
|
|
3324 |
"model_id": "27cb5ccf2c9249c389ea9218af245274", |
|
|
3325 |
"version_minor": 0, |
|
|
3326 |
"version_major": 2 |
|
|
3327 |
}, |
|
|
3328 |
"text/plain": [ |
|
|
3329 |
" 0%| | 0/131 [00:00<?, ?ba/s]" |
|
|
3330 |
] |
|
|
3331 |
}, |
|
|
3332 |
"metadata": {} |
|
|
3333 |
}, |
|
|
3334 |
{ |
|
|
3335 |
"output_type": "display_data", |
|
|
3336 |
"data": { |
|
|
3337 |
"application/vnd.jupyter.widget-view+json": { |
|
|
3338 |
"model_id": "8637f3a9e37a4f0eab2eaba0543e60ae", |
|
|
3339 |
"version_minor": 0, |
|
|
3340 |
"version_major": 2 |
|
|
3341 |
}, |
|
|
3342 |
"text/plain": [ |
|
|
3343 |
" 0%| | 0/12 [00:00<?, ?ba/s]" |
|
|
3344 |
] |
|
|
3345 |
}, |
|
|
3346 |
"metadata": {} |
|
|
3347 |
} |
|
|
3348 |
] |
|
|
3349 |
}, |
|
|
3350 |
{ |
|
|
3351 |
"cell_type": "code", |
|
|
3352 |
"metadata": { |
|
|
3353 |
"id": "MzLk5x_g-5WQ" |
|
|
3354 |
}, |
|
|
3355 |
"source": [ |
|
|
3356 |
"data_collator = default_data_collator" |
|
|
3357 |
], |
|
|
3358 |
"execution_count": null, |
|
|
3359 |
"outputs": [] |
|
|
3360 |
}, |
|
|
3361 |
{ |
|
|
3362 |
"cell_type": "code", |
|
|
3363 |
"metadata": { |
|
|
3364 |
"id": "N2-iKs4pD4fp" |
|
|
3365 |
}, |
|
|
3366 |
"source": [ |
|
|
3367 |
"# part_train_data = tokenized_datasets[\"train\"].train_test_split(0.9)['train']\n", |
|
|
3368 |
"# part_validation_data = tokenized_datasets[\"validation\"].train_test_split(0.9)['train']" |
|
|
3369 |
], |
|
|
3370 |
"execution_count": null, |
|
|
3371 |
"outputs": [] |
|
|
3372 |
}, |
|
|
3373 |
{ |
|
|
3374 |
"cell_type": "code", |
|
|
3375 |
"metadata": { |
|
|
3376 |
"id": "Mpclus1y-8TL" |
|
|
3377 |
}, |
|
|
3378 |
"source": [ |
|
|
3379 |
"trainer = Trainer(\n", |
|
|
3380 |
" model,\n", |
|
|
3381 |
" args,\n", |
|
|
3382 |
" train_dataset=tokenized_datasets[\"train\"],\n", |
|
|
3383 |
" eval_dataset=tokenized_datasets[\"validation\"],\n", |
|
|
3384 |
" data_collator=data_collator,\n", |
|
|
3385 |
" tokenizer=tokenizer,\n", |
|
|
3386 |
")" |
|
|
3387 |
], |
|
|
3388 |
"execution_count": null, |
|
|
3389 |
"outputs": [] |
|
|
3390 |
}, |
|
|
3391 |
{ |
|
|
3392 |
"cell_type": "code", |
|
|
3393 |
"metadata": { |
|
|
3394 |
"colab": { |
|
|
3395 |
"base_uri": "https://localhost:8080/" |
|
|
3396 |
}, |
|
|
3397 |
"id": "ex_pzs1i4MZZ", |
|
|
3398 |
"outputId": "9a950100-c34f-4ea4-f5ee-680dabe493a7" |
|
|
3399 |
}, |
|
|
3400 |
"source": [ |
|
|
3401 |
"!free -h" |
|
|
3402 |
], |
|
|
3403 |
"execution_count": null, |
|
|
3404 |
"outputs": [ |
|
|
3405 |
{ |
|
|
3406 |
"output_type": "stream", |
|
|
3407 |
"name": "stdout", |
|
|
3408 |
"text": [ |
|
|
3409 |
" total used free shared buff/cache available\n", |
|
|
3410 |
"Mem: 12G 2.2G 5.5G 4.2M 5.0G 10G\n", |
|
|
3411 |
"Swap: 0B 0B 0B\n" |
|
|
3412 |
] |
|
|
3413 |
} |
|
|
3414 |
] |
|
|
3415 |
}, |
|
|
3416 |
{ |
|
|
3417 |
"cell_type": "code", |
|
|
3418 |
"metadata": { |
|
|
3419 |
"colab": { |
|
|
3420 |
"base_uri": "https://localhost:8080/" |
|
|
3421 |
}, |
|
|
3422 |
"id": "zdipHhuc7CEK", |
|
|
3423 |
"outputId": "14460103-aab8-4b2a-bca5-30e60c94eb9e" |
|
|
3424 |
}, |
|
|
3425 |
"source": [ |
|
|
3426 |
"!nvidia-smi" |
|
|
3427 |
], |
|
|
3428 |
"execution_count": null, |
|
|
3429 |
"outputs": [ |
|
|
3430 |
{ |
|
|
3431 |
"output_type": "stream", |
|
|
3432 |
"name": "stdout", |
|
|
3433 |
"text": [ |
|
|
3434 |
"Sun Sep 12 22:02:37 2021 \n", |
|
|
3435 |
"+-----------------------------------------------------------------------------+\n", |
|
|
3436 |
"| NVIDIA-SMI 470.63.01 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", |
|
|
3437 |
"|-------------------------------+----------------------+----------------------+\n", |
|
|
3438 |
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", |
|
|
3439 |
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", |
|
|
3440 |
"| | | MIG M. |\n", |
|
|
3441 |
"|===============================+======================+======================|\n", |
|
|
3442 |
"| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n", |
|
|
3443 |
"| N/A 75C P0 76W / 149W | 504MiB / 11441MiB | 0% Default |\n", |
|
|
3444 |
"| | | N/A |\n", |
|
|
3445 |
"+-------------------------------+----------------------+----------------------+\n", |
|
|
3446 |
" \n", |
|
|
3447 |
"+-----------------------------------------------------------------------------+\n", |
|
|
3448 |
"| Processes: |\n", |
|
|
3449 |
"| GPU GI CI PID Type Process name GPU Memory |\n", |
|
|
3450 |
"| ID ID Usage |\n", |
|
|
3451 |
"|=============================================================================|\n", |
|
|
3452 |
"| No running processes found |\n", |
|
|
3453 |
"+-----------------------------------------------------------------------------+\n" |
|
|
3454 |
] |
|
|
3455 |
} |
|
|
3456 |
] |
|
|
3457 |
}, |
|
|
3458 |
{ |
|
|
3459 |
"cell_type": "code", |
|
|
3460 |
"metadata": { |
|
|
3461 |
"colab": { |
|
|
3462 |
"base_uri": "https://localhost:8080/", |
|
|
3463 |
"height": 469 |
|
|
3464 |
}, |
|
|
3465 |
"id": "IK1JAfN_--B9", |
|
|
3466 |
"outputId": "eb58b22f-3306-48b4-f4b1-341d235b5f05" |
|
|
3467 |
}, |
|
|
3468 |
"source": [ |
|
|
3469 |
"trainer.train()" |
|
|
3470 |
], |
|
|
3471 |
"execution_count": null, |
|
|
3472 |
"outputs": [ |
|
|
3473 |
{ |
|
|
3474 |
"output_type": "stream", |
|
|
3475 |
"name": "stderr", |
|
|
3476 |
"text": [ |
|
|
3477 |
"***** Running training *****\n", |
|
|
3478 |
" Num examples = 131958\n", |
|
|
3479 |
" Num Epochs = 2\n", |
|
|
3480 |
" Instantaneous batch size per device = 16\n", |
|
|
3481 |
" Total train batch size (w. parallel, distributed & accumulation) = 16\n", |
|
|
3482 |
" Gradient Accumulation steps = 1\n", |
|
|
3483 |
" Total optimization steps = 16496\n" |
|
|
3484 |
] |
|
|
3485 |
}, |
|
|
3486 |
{ |
|
|
3487 |
"output_type": "display_data", |
|
|
3488 |
"data": { |
|
|
3489 |
"text/html": [ |
|
|
3490 |
"\n", |
|
|
3491 |
" <div>\n", |
|
|
3492 |
" \n", |
|
|
3493 |
" <progress value='1701' max='16496' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", |
|
|
3494 |
" [ 1701/16496 1:05:12 < 9:27:52, 0.43 it/s, Epoch 0.21/2]\n", |
|
|
3495 |
" </div>\n", |
|
|
3496 |
" <table border=\"1\" class=\"dataframe\">\n", |
|
|
3497 |
" <thead>\n", |
|
|
3498 |
" <tr style=\"text-align: left;\">\n", |
|
|
3499 |
" <th>Epoch</th>\n", |
|
|
3500 |
" <th>Training Loss</th>\n", |
|
|
3501 |
" <th>Validation Loss</th>\n", |
|
|
3502 |
" </tr>\n", |
|
|
3503 |
" </thead>\n", |
|
|
3504 |
" <tbody>\n", |
|
|
3505 |
" </tbody>\n", |
|
|
3506 |
"</table><p>" |
|
|
3507 |
], |
|
|
3508 |
"text/plain": [ |
|
|
3509 |
"<IPython.core.display.HTML object>" |
|
|
3510 |
] |
|
|
3511 |
}, |
|
|
3512 |
"metadata": {} |
|
|
3513 |
}, |
|
|
3514 |
{ |
|
|
3515 |
"output_type": "stream", |
|
|
3516 |
"name": "stderr", |
|
|
3517 |
"text": [ |
|
|
3518 |
"Saving model checkpoint to drive/MyDrive/albert-qa/checkpoint-500\n", |
|
|
3519 |
"Configuration saved in drive/MyDrive/albert-qa/checkpoint-500/config.json\n", |
|
|
3520 |
"Model weights saved in drive/MyDrive/albert-qa/checkpoint-500/pytorch_model.bin\n", |
|
|
3521 |
"tokenizer config file saved in drive/MyDrive/albert-qa/checkpoint-500/tokenizer_config.json\n", |
|
|
3522 |
"Special tokens file saved in drive/MyDrive/albert-qa/checkpoint-500/special_tokens_map.json\n", |
|
|
3523 |
"Saving model checkpoint to drive/MyDrive/albert-qa/checkpoint-1000\n", |
|
|
3524 |
"Configuration saved in drive/MyDrive/albert-qa/checkpoint-1000/config.json\n", |
|
|
3525 |
"Model weights saved in drive/MyDrive/albert-qa/checkpoint-1000/pytorch_model.bin\n", |
|
|
3526 |
"tokenizer config file saved in drive/MyDrive/albert-qa/checkpoint-1000/tokenizer_config.json\n", |
|
|
3527 |
"Special tokens file saved in drive/MyDrive/albert-qa/checkpoint-1000/special_tokens_map.json\n", |
|
|
3528 |
"Saving model checkpoint to drive/MyDrive/albert-qa/checkpoint-1500\n", |
|
|
3529 |
"Configuration saved in drive/MyDrive/albert-qa/checkpoint-1500/config.json\n", |
|
|
3530 |
"Model weights saved in drive/MyDrive/albert-qa/checkpoint-1500/pytorch_model.bin\n", |
|
|
3531 |
"tokenizer config file saved in drive/MyDrive/albert-qa/checkpoint-1500/tokenizer_config.json\n", |
|
|
3532 |
"Special tokens file saved in drive/MyDrive/albert-qa/checkpoint-1500/special_tokens_map.json\n" |
|
|
3533 |
] |
|
|
3534 |
} |
|
|
3535 |
] |
|
|
3536 |
}, |
|
|
3537 |
{ |
|
|
3538 |
"cell_type": "code", |
|
|
3539 |
"metadata": { |
|
|
3540 |
"id": "qmLnsey8A9lI" |
|
|
3541 |
}, |
|
|
3542 |
"source": [ |
|
|
3543 |
"rainer.save_model(\"albert-base-squad-trained\")" |
|
|
3544 |
], |
|
|
3545 |
"execution_count": null, |
|
|
3546 |
"outputs": [] |
|
|
3547 |
}, |
|
|
3548 |
{ |
|
|
3549 |
"cell_type": "code", |
|
|
3550 |
"metadata": { |
|
|
3551 |
"id": "Giu6u6JJ-WV6" |
|
|
3552 |
}, |
|
|
3553 |
"source": [ |
|
|
3554 |
"" |
|
|
3555 |
], |
|
|
3556 |
"execution_count": null, |
|
|
3557 |
"outputs": [] |
|
|
3558 |
} |
|
|
3559 |
] |
|
|
3560 |
} |