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],
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"text": [
"Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (1.11.0)\n",
"Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.10.2)\n",
"Requirement already satisfied: gdown in /usr/local/lib/python3.7/dist-packages (3.6.4)\n",
"Requirement already satisfied: tqdm>=4.42 in /usr/local/lib/python3.7/dist-packages (from datasets) (4.62.0)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets) (1.1.5)\n",
"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from datasets) (4.6.4)\n",
"Requirement already satisfied: fsspec>=2021.05.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2021.8.1)\n",
"Requirement already satisfied: xxhash in /usr/local/lib/python3.7/dist-packages (from datasets) (2.0.2)\n",
"Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.4)\n",
"Requirement already satisfied: pyarrow!=4.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (3.0.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from datasets) (21.0)\n",
"Requirement already satisfied: huggingface-hub<0.1.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.0.16)\n",
"Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2.23.0)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.19.5)\n",
"Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets) (0.70.12.2)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\n",
"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",
"Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->datasets) (2.4.7)\n",
"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",
"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",
"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",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (5.4.1)\n",
"Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.45)\n",
"Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from gdown) (1.15.0)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.5.0)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2018.9)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jMpji8WE8ERV"
},
"source": [
"import transformers\n",
"from transformers import AutoTokenizer, AutoModelForQuestionAnswering, TrainingArguments, Trainer, default_data_collator\n",
"from datasets import load_dataset, load_metric"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TIbI5xnn_XvN",
"outputId": "48647dcf-144c-4689-b6c0-3a695f8091c6"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5CnAFQ3DAzaJ",
"outputId": "4d8dfa4a-9fe3-45e0-c144-a777f408e8a5"
},
"source": [
"!gdown https://drive.google.com/uc?id=10663oMohQRDuNCQ0REMIeY8IAKoCiK3A"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=10663oMohQRDuNCQ0REMIeY8IAKoCiK3A\n",
"To: /content/checkpoint-final-mlm-albert.zip\n",
"42.2MB [00:00, 134MB/s] \n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lddjlLEZA2nu",
"outputId": "03e069f5-4d1d-46e7-939a-858764a20fd7"
},
"source": [
"!unzip checkpoint-final-mlm-albert.zip"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Archive: checkpoint-final-mlm-albert.zip\n",
"replace drive/MyDrive/albert-mlm/checkpoint-final-mlm-albert/config.json? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6dBfG_vVAt9i",
"outputId": "ec5e1985-d8de-4de0-d1af-b35563b45765"
},
"source": [
"!ls drive/MyDrive/albert-mlm/checkpoint-final-mlm-albert"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"all_results.json special_tokens_map.json training_args.bin\n",
"config.json\t tokenizer_config.json train_results.json\n",
"eval_results.json tokenizer.json\n",
"pytorch_model.bin trainer_state.json\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "sw5JuNBI8c8p"
},
"source": [
"max_length = 384 \n",
"doc_stride = 128 \n",
"model_checkpoint = \"drive/MyDrive/albert-mlm/checkpoint-final-mlm-albert\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 290,
"referenced_widgets": [
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]
},
"id": "vlnhdtQp9E6w",
"outputId": "0610fa28-830a-426a-f6d5-af3045372433"
},
"source": [
"datasets = load_dataset(\"squad_v2\")\n",
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
"pad_on_right = tokenizer.padding_side == \"right\"\n",
"model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "96f412ee972242ccb6d651c3cc8b6e5a",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/1.87k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d27eab2202a48319faf42b9de85ebad",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/1.02k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"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"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "307dd3c4bb8940878c96e9a559e4f8c5",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/9.55M [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c39cdd8823ec43adb768efe255968373",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/801k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "80d7f12578e04833af509cf682375353",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"0 examples [00:00, ? examples/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd46c5e6d6f54c4ea61b010d0d3d1e6d",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"0 examples [00:00, ? examples/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"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"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"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",
"- 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",
"- 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",
"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",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "u9PNvMvc9nCR"
},
"source": [
"def prepare_train_features(examples):\n",
" examples[\"question\"] = [q.lstrip() for q in examples[\"question\"]]\n",
"\n",
" tokenized_examples = tokenizer(\n",
" examples[\"question\" if pad_on_right else \"context\"],\n",
" examples[\"context\" if pad_on_right else \"question\"],\n",
" truncation=\"only_second\" if pad_on_right else \"only_first\",\n",
" max_length=max_length,\n",
" stride=doc_stride,\n",
" return_overflowing_tokens=True,\n",
" return_offsets_mapping=True,\n",
" padding=\"max_length\",\n",
" )\n",
"\n",
" sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n",
" offset_mapping = tokenized_examples.pop(\"offset_mapping\")\n",
"\n",
" tokenized_examples[\"start_positions\"] = []\n",
" tokenized_examples[\"end_positions\"] = []\n",
"\n",
" for i, offsets in enumerate(offset_mapping):\n",
" input_ids = tokenized_examples[\"input_ids\"][i]\n",
" cls_index = input_ids.index(tokenizer.cls_token_id)\n",
"\n",
" sequence_ids = tokenized_examples.sequence_ids(i)\n",
"\n",
" sample_index = sample_mapping[i]\n",
" answers = examples[\"answers\"][sample_index]\n",
"\n",
" if len(answers[\"answer_start\"]) == 0:\n",
" tokenized_examples[\"start_positions\"].append(cls_index)\n",
" tokenized_examples[\"end_positions\"].append(cls_index)\n",
" else:\n",
" start_char = answers[\"answer_start\"][0]\n",
" end_char = start_char + len(answers[\"text\"][0])\n",
"\n",
" token_start_index = 0\n",
" while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\n",
" token_start_index += 1\n",
"\n",
" token_end_index = len(input_ids) - 1\n",
" while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\n",
" token_end_index -= 1\n",
"\n",
" if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\n",
" tokenized_examples[\"start_positions\"].append(cls_index)\n",
" tokenized_examples[\"end_positions\"].append(cls_index)\n",
" else:\n",
" while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\n",
" token_start_index += 1\n",
" tokenized_examples[\"start_positions\"].append(token_start_index - 1)\n",
" while offsets[token_end_index][1] >= end_char:\n",
" token_end_index -= 1\n",
" tokenized_examples[\"end_positions\"].append(token_end_index + 1)\n",
"\n",
" return tokenized_examples"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JrwtE3WhD3-D"
},
"source": [
"# !rm -rf drive/MyDrive/albert-qa"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6m4wTO48_xnt"
},
"source": [
"!mkdir -p drive/MyDrive/albert-qa"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DuMBJxFV_gLF",
"outputId": "e2007da3-b0ab-4f40-a55b-2868c7a1ce05"
},
"source": [
"!ls drive/MyDrive/"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" albert-mlm\t\t\t MirasText.zip\n",
" albert-qa\t\t\t model\n",
" all_sentences.txt\t\t qa.json\n",
" bert-base-finetuned\t\t qa_static.csv\n",
" bert-large-finetuned\t\t runs\n",
" cached_train_checkpoint-final_384 titles_word2vec.model\n",
"'Colab Notebooks'\t\t transformers\n",
" documents_word2vec.model\t 'Untitled document.gdoc'\n",
" drug_dataset.csv\t\t تایمشیت.gsheet\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "0DwWS3JO-lJt"
},
"source": [
"model_name = model_checkpoint.split(\"/\")[-1]\n",
"\n",
"args = TrainingArguments(\n",
" evaluation_strategy = \"epoch\",\n",
" learning_rate=2e-5,\n",
" per_device_train_batch_size=16,\n",
" num_train_epochs=2,\n",
" weight_decay=0.01,\n",
" save_steps=500,\n",
" resume_from_checkpoint=True,\n",
" output_dir='drive/MyDrive/albert-qa',\n",
" do_train=True,\n",
" do_eval=False,\n",
" save_total_limit=3\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hSVJuMSB_DiV",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"27cb5ccf2c9249c389ea9218af245274",
"b83dcd8153c14fb4b2ea3158b72c396a",
"1c1fef0ee40b4c3bb3f8e55d256c2b33",
"6298a9bdaf0248bb84afd55ac8b3a6ae",
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"242b2460846b4f72802c4dfcd2f45620",
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"1d5dd5d69064453bb91f237c1a3f4104",
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"2f14543841684e589c8964f497afbe3b",
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"a88dcd0696d849d9b2e23484e8e4c342",
"3ad5943a7a3d4a158a4bd682550126f2",
"bba1fdb19b854852ac04b4f59d4da185",
"af345e1fd5414deca969d5ba7b1c582f",
"971fa5cc540f40a2b699390c3ee7c4ec",
"252cca28b41a48a0b6e011370da46a23",
"d6f1e6fee5774871ae1d862612cee6c6"
]
},
"outputId": "4b29c0d0-4de8-47a2-8bdd-4f0ca10ceea1"
},
"source": [
"tokenized_datasets = datasets.map(prepare_train_features, batched=True, remove_columns=datasets[\"train\"].column_names)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "27cb5ccf2c9249c389ea9218af245274",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/131 [00:00<?, ?ba/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8637f3a9e37a4f0eab2eaba0543e60ae",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/12 [00:00<?, ?ba/s]"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "MzLk5x_g-5WQ"
},
"source": [
"data_collator = default_data_collator"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "N2-iKs4pD4fp"
},
"source": [
"# part_train_data = tokenized_datasets[\"train\"].train_test_split(0.9)['train']\n",
"# part_validation_data = tokenized_datasets[\"validation\"].train_test_split(0.9)['train']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Mpclus1y-8TL"
},
"source": [
"trainer = Trainer(\n",
" model,\n",
" args,\n",
" train_dataset=tokenized_datasets[\"train\"],\n",
" eval_dataset=tokenized_datasets[\"validation\"],\n",
" data_collator=data_collator,\n",
" tokenizer=tokenizer,\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ex_pzs1i4MZZ",
"outputId": "9a950100-c34f-4ea4-f5ee-680dabe493a7"
},
"source": [
"!free -h"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" total used free shared buff/cache available\n",
"Mem: 12G 2.2G 5.5G 4.2M 5.0G 10G\n",
"Swap: 0B 0B 0B\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zdipHhuc7CEK",
"outputId": "14460103-aab8-4b2a-bca5-30e60c94eb9e"
},
"source": [
"!nvidia-smi"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sun Sep 12 22:02:37 2021 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 470.63.01 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 75C P0 76W / 149W | 504MiB / 11441MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 469
},
"id": "IK1JAfN_--B9",
"outputId": "eb58b22f-3306-48b4-f4b1-341d235b5f05"
},
"source": [
"trainer.train()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"***** Running training *****\n",
" Num examples = 131958\n",
" Num Epochs = 2\n",
" Instantaneous batch size per device = 16\n",
" Total train batch size (w. parallel, distributed & accumulation) = 16\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 16496\n"
]
},
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1701' max='16496' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [ 1701/16496 1:05:12 < 9:27:52, 0.43 it/s, Epoch 0.21/2]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table><p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Saving model checkpoint to drive/MyDrive/albert-qa/checkpoint-500\n",
"Configuration saved in drive/MyDrive/albert-qa/checkpoint-500/config.json\n",
"Model weights saved in drive/MyDrive/albert-qa/checkpoint-500/pytorch_model.bin\n",
"tokenizer config file saved in drive/MyDrive/albert-qa/checkpoint-500/tokenizer_config.json\n",
"Special tokens file saved in drive/MyDrive/albert-qa/checkpoint-500/special_tokens_map.json\n",
"Saving model checkpoint to drive/MyDrive/albert-qa/checkpoint-1000\n",
"Configuration saved in drive/MyDrive/albert-qa/checkpoint-1000/config.json\n",
"Model weights saved in drive/MyDrive/albert-qa/checkpoint-1000/pytorch_model.bin\n",
"tokenizer config file saved in drive/MyDrive/albert-qa/checkpoint-1000/tokenizer_config.json\n",
"Special tokens file saved in drive/MyDrive/albert-qa/checkpoint-1000/special_tokens_map.json\n",
"Saving model checkpoint to drive/MyDrive/albert-qa/checkpoint-1500\n",
"Configuration saved in drive/MyDrive/albert-qa/checkpoint-1500/config.json\n",
"Model weights saved in drive/MyDrive/albert-qa/checkpoint-1500/pytorch_model.bin\n",
"tokenizer config file saved in drive/MyDrive/albert-qa/checkpoint-1500/tokenizer_config.json\n",
"Special tokens file saved in drive/MyDrive/albert-qa/checkpoint-1500/special_tokens_map.json\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "qmLnsey8A9lI"
},
"source": [
"rainer.save_model(\"albert-base-squad-trained\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Giu6u6JJ-WV6"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
Datasets
Models
