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"base_uri": "https://localhost:8080/" }, "id": "SdT79WnDl6Tv", "outputId": "8a5475ba-b438-43b2-8169-bdf38f26b04a" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting transformers\n", " Downloading transformers-4.20.1-py3-none-any.whl (4.4 MB)\n", "\u001b[K |████████████████████████████████| 4.4 MB 25.8 MB/s \n", "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n", " Downloading huggingface_hub-0.8.1-py3-none-any.whl (101 kB)\n", "\u001b[K |████████████████████████████████| 101 kB 14.1 MB/s \n", "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n", "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n", "Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n", " Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n", "\u001b[K |████████████████████████████████| 6.6 MB 56.5 MB/s \n", "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n", "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.4)\n", "Collecting pyyaml>=5.1\n", " Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n", "\u001b[K |████████████████████████████████| 596 kB 41.7 MB/s \n", "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.1)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n", "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n", "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.0)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n", "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\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->transformers) (1.24.3)\n", "Installing collected packages: pyyaml, tokenizers, huggingface-hub, transformers\n", " Attempting uninstall: pyyaml\n", " Found existing installation: PyYAML 3.13\n", " Uninstalling PyYAML-3.13:\n", " Successfully uninstalled PyYAML-3.13\n", "Successfully installed huggingface-hub-0.8.1 pyyaml-6.0 tokenizers-0.12.1 transformers-4.20.1\n" ] } ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "wRXCvVeYk5bW" }, "outputs": [], "source": [ "import csv, lzma\n", "\n", "# Reads input from directory and returns a list\n", "def io_read(dir):\n", " X = []\n", " if 'xz' in dir:\n", " with lzma.open(dir) as f:\n", " for line in f:\n", " text = line.decode('utf-8')\n", " text = text.split('\\t')\n", " X.append(text)\n", " else:\n", " with open(dir) as f:\n", " for line in f:\n", " X.append(line.replace('\\n', ''))\n", " return X\n", "\n", "# Takes the output (list) and writes it into directory\n", "def io_write(output, dir):\n", " with open(dir, 'w', newline='') as f:\n", " writer = csv.writer(f)\n", " writer.writerows(output)" ] }, { "cell_type": "code", "source": [ "x_train = [x[0] for x in io_read('/content/drive/MyDrive/ai-tech/eki/paranormal-or-skeptic/train/in.tsv.xz')]\n", "y_train = [int(y) for y in io_read('/content/drive/MyDrive/ai-tech/eki/paranormal-or-skeptic/train/expected.tsv')]\n", "\n", "# x_dev = [x[0] for x in io_read('/content/drive/MyDrive/ai-tech/eki/paranormal-or-skeptic/dev-0/in.tsv.xz')]\n", "# y_dev = [int(y) for y in io_read('/content/drive/MyDrive/ai-tech/eki/paranormal-or-skeptic/dev-0/expected.tsv')]\n", "\n", "# x_test = [x[0] for x in io_read('/content/drive/MyDrive/ai-tech/eki/paranormal-or-skeptic/test-A/in.tsv.xz')]" ], "metadata": { "id": "KOogjYiElR5v" }, "execution_count": 4, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import BertTokenizerFast\n", "\n", "model_name = \"google/bert_uncased_L-4_H-512_A-8\"\n", "tokenizer = BertTokenizerFast.from_pretrained(model_name)\n", "\n", "train_encodings = tokenizer(x_train, truncation=True, padding='max_length', max_length=128)" ], "metadata": { "id": "2aIRZ0gcLBXg" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "import torch\n", "\n", "class Dataset(torch.utils.data.Dataset):\n", " def __init__(self, encodings, labels=None):\n", " self.encodings = encodings\n", " self.labels = labels\n", "\n", " def __getitem__(self, idx):\n", " item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n", " if self.labels:\n", " item[\"labels\"] = torch.tensor(self.labels[idx])\n", " return item\n", "\n", " def __len__(self):\n", " return len(self.encodings[\"input_ids\"])" ], "metadata": { "id": "9jNTGg77Elnu" }, "execution_count": 7, "outputs": [] }, { "cell_type": "code", "source": [ "train_dataset = Dataset(train_encodings, y_train)" ], "metadata": { "id": "h88hD-xvLKe0" }, "execution_count": 8, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import BertForSequenceClassification\n", "\n", "model = BertForSequenceClassification.from_pretrained(model_name)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 161, "referenced_widgets": [ "edb0a9e2b5624067bad7e49f7260304e", "3e3de49abd93447d97d630900c0998a6", "2d5a1f551f7747e79d7749b7e0bb06c7", "fca439193c054ca9b016cfe4233e0cef", "c6a52195933146748c69d158c5fecb36", "9f04902c9f414aafa3138c2ea24c79c4", "762b7b38f26547cfbc69a23c9caa05db", "98833c8af5c54a99bf5eea5717cb6907", "7e67bd6e851d4086b6e4c4c8599a1b0d", "84c7a3e70aa249c8bb4d007d934d2f8d", "c7994559d8754fdd8936c019beb304b1" ] }, "id": "iLovBI-GnEad", "outputId": "e2eb76cb-1143-4a8e-ec56-2d48a90427e3" }, "execution_count": 9, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading: 0%| | 0.00/111M [00:00" ], "text/html": [ "\n", "
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StepTraining Loss
5000.552400
10000.542700
15000.552900
20000.549600
25000.548200
30000.546700
35000.546100
40000.545600
45000.544800
50000.546000
55000.545800
60000.541200
65000.536100
70000.537200
75000.545700
80000.543100
85000.548600
90000.538600
95000.540100
100000.543100
105000.540700
110000.541200
115000.542100
120000.538600
125000.535000
130000.538300
135000.546500
140000.541200
145000.530200
150000.532000
155000.535000
160000.540900
165000.543100
170000.538300
175000.544900
180000.538400
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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to ./results/checkpoint-500\n", "Configuration saved in ./results/checkpoint-500/config.json\n", "Model weights saved in ./results/checkpoint-500/pytorch_model.bin\n", "Deleting older checkpoint [results/checkpoint-18000] due to args.save_total_limit\n", "Saving model checkpoint to ./results/checkpoint-1000\n", "Configuration saved in ./results/checkpoint-1000/config.json\n", "Model weights saved in ./results/checkpoint-1000/pytorch_model.bin\n", "Deleting older checkpoint [results/checkpoint-500] due to args.save_total_limit\n", "Saving model checkpoint to ./results/checkpoint-1500\n", "Configuration saved in ./results/checkpoint-1500/config.json\n", "Model weights saved in ./results/checkpoint-1500/pytorch_model.bin\n", "Deleting older checkpoint [results/checkpoint-1000] due to args.save_total_limit\n", "Saving model checkpoint to ./results/checkpoint-2000\n", "Configuration saved in ./results/checkpoint-2000/config.json\n", "Model weights saved in ./results/checkpoint-2000/pytorch_model.bin\n", "Deleting older checkpoint [results/checkpoint-1500] due to args.save_total_limit\n", "Saving model checkpoint to ./results/checkpoint-2500\n", "Configuration saved in ./results/checkpoint-2500/config.json\n", "Model weights saved in ./results/checkpoint-2500/pytorch_model.bin\n", "Deleting older checkpoint [results/checkpoint-2000] due to args.save_total_limit\n", "Saving model checkpoint to ./results/checkpoint-3000\n", "Configuration saved in ./results/checkpoint-3000/config.json\n", "Model weights saved in ./results/checkpoint-3000/pytorch_model.bin\n", "Deleting older checkpoint [results/checkpoint-2500] due to args.save_total_limit\n", "Saving model checkpoint to ./results/checkpoint-3500\n", "Configuration saved in ./results/checkpoint-3500/config.json\n", "Model weights saved in ./results/checkpoint-3500/pytorch_model.bin\n", "Deleting older checkpoint 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[results/checkpoint-52000] due to args.save_total_limit\n", "Saving model checkpoint to ./results/checkpoint-53000\n", "Configuration saved in ./results/checkpoint-53000/config.json\n", "Model weights saved in ./results/checkpoint-53000/pytorch_model.bin\n", "Deleting older checkpoint [results/checkpoint-52500] due to args.save_total_limit\n", "Saving model checkpoint to ./results/checkpoint-53500\n", "Configuration saved in ./results/checkpoint-53500/config.json\n", "Model weights saved in ./results/checkpoint-53500/pytorch_model.bin\n", "Deleting older checkpoint [results/checkpoint-53000] due to args.save_total_limit\n", "Saving model checkpoint to ./results/checkpoint-54000\n", "Configuration saved in ./results/checkpoint-54000/config.json\n", "Model weights saved in ./results/checkpoint-54000/pytorch_model.bin\n", "Deleting older checkpoint [results/checkpoint-53500] due to args.save_total_limit\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=54297, training_loss=0.537743896358476, metrics={'train_runtime': 1276.4291, 'train_samples_per_second': 680.599, 'train_steps_per_second': 42.538, 'total_flos': 8589565725967872.0, 'train_loss': 0.537743896358476, 'epoch': 3.0})" ] }, "metadata": {}, "execution_count": 14 } ] }, { "cell_type": "code", "source": [ "x_dev = [x[0] for x in io_read('/content/drive/MyDrive/ai-tech/eki/paranormal-or-skeptic/dev-0/in.tsv.xz')]\n", "y_dev = [int(y) for y in io_read('/content/drive/MyDrive/ai-tech/eki/paranormal-or-skeptic/dev-0/expected.tsv')]\n", "# x_test = [x[0] for x in io_read('/content/drive/MyDrive/ai-tech/eki/paranormal-or-skeptic/test-A/in.tsv.xz')]\n", "dev_encodings = tokenizer(x_dev, truncation=True, padding='max_length', max_length=128)\n", "dev_dataset = Dataset(dev_encodings, y_dev)" ], "metadata": { "id": "8EEK8D_2o9Xa" }, "execution_count": 15, "outputs": [] }, { "cell_type": "code", "source": [ "dev_predicted = trainer.predict(dev_dataset).predictions" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 92 }, "id": "vHsugxBnT1dt", "outputId": "d636a78d-5de4-4d7a-eb96-3906fe5b2efd" }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "***** Running Prediction *****\n", " Num examples = 5272\n", " Batch size = 8\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

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