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fine-tuning-turkish-bert/fine_tuned_BERT_Turkish.ipynb
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}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "r7J7f7i4Loyq",
"outputId": "84587e74-4bb8-472d-8026-6f49f1eb64aa"
},
"source": [
"!pip install transformers\n",
"!pip install datasets"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting transformers\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/98/87/ef312eef26f5cecd8b17ae9654cdd8d1fae1eb6dbd87257d6d73c128a4d0/transformers-4.3.2-py3-none-any.whl (1.8MB)\n",
"\u001b[K |████████████████████████████████| 1.8MB 5.9MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (1.19.5)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
"Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from transformers) (3.4.0)\n",
"Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.8)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.9)\n",
"Collecting sacremoses\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
"\u001b[K |████████████████████████████████| 890kB 47.8MB/s \n",
"\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
"Collecting tokenizers<0.11,>=0.10.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/fd/5b/44baae602e0a30bcc53fbdbc60bd940c15e143d252d658dfdefce736ece5/tokenizers-0.10.1-cp36-cp36m-manylinux2010_x86_64.whl (3.2MB)\n",
"\u001b[K |████████████████████████████████| 3.2MB 54.1MB/s \n",
"\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.0)\n",
"Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.15.0)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.0.0)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.12.5)\n",
"Building wheels for collected packages: sacremoses\n",
" Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893261 sha256=9a78a1a1ee0a2b301b151e7f0fa1783d69294fdd820f28e785074befdf105ac5\n",
" Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
"Successfully built sacremoses\n",
"Installing collected packages: sacremoses, tokenizers, transformers\n",
"Successfully installed sacremoses-0.0.43 tokenizers-0.10.1 transformers-4.3.2\n",
"Collecting datasets\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a2/12/5fd53adc5ba8a8d562b19f2c1c859547659e96b87a767cd52556538d205e/datasets-1.3.0-py3-none-any.whl (181kB)\n",
"\u001b[K |████████████████████████████████| 184kB 4.3MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.6/dist-packages (from datasets) (1.19.5)\n",
"Requirement already satisfied: dill in /usr/local/lib/python3.6/dist-packages (from datasets) (0.3.3)\n",
"Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from datasets) (3.4.0)\n",
"Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from datasets) (0.8)\n",
"Requirement already satisfied: multiprocess in /usr/local/lib/python3.6/dist-packages (from datasets) (0.70.11.1)\n",
"Requirement already satisfied: tqdm<4.50.0,>=4.27 in /usr/local/lib/python3.6/dist-packages (from datasets) (4.41.1)\n",
"Collecting xxhash\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/f7/73/826b19f3594756cb1c6c23d2fbd8ca6a77a9cd3b650c9dec5acc85004c38/xxhash-2.0.0-cp36-cp36m-manylinux2010_x86_64.whl (242kB)\n",
"\u001b[K |████████████████████████████████| 245kB 23.7MB/s \n",
"\u001b[?25hCollecting fsspec\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/ec/80/72ac0982cc833945fada4b76c52f0f65435ba4d53bc9317d1c70b5f7e7d5/fsspec-0.8.5-py3-none-any.whl (98kB)\n",
"\u001b[K |████████████████████████████████| 102kB 11.0MB/s \n",
"\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from datasets) (1.1.5)\n",
"Collecting huggingface-hub==0.0.2\n",
" Downloading https://files.pythonhosted.org/packages/b5/93/7cb0755c62c36cdadc70c79a95681df685b52cbaf76c724facb6ecac3272/huggingface_hub-0.0.2-py3-none-any.whl\n",
"Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.6/dist-packages (from datasets) (2.23.0)\n",
"Collecting pyarrow>=0.17.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/33/67/2f4fcce1b41bcc7e88a6bfdb42046597ae72e5bc95c2789b7c5ac893c433/pyarrow-3.0.0-cp36-cp36m-manylinux2014_x86_64.whl (20.7MB)\n",
"\u001b[K |████████████████████████████████| 20.7MB 1.6MB/s \n",
"\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->datasets) (3.7.4.3)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->datasets) (3.4.0)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->datasets) (2.8.1)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->datasets) (2018.9)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from huggingface-hub==0.0.2->datasets) (3.0.12)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets) (2020.12.5)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets) (3.0.4)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
"Installing collected packages: xxhash, fsspec, huggingface-hub, pyarrow, datasets\n",
" Found existing installation: pyarrow 0.14.1\n",
" Uninstalling pyarrow-0.14.1:\n",
" Successfully uninstalled pyarrow-0.14.1\n",
"Successfully installed datasets-1.3.0 fsspec-0.8.5 huggingface-hub-0.0.2 pyarrow-3.0.0 xxhash-2.0.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235,
"referenced_widgets": [
"a186ec4ef0874b488da90b004f1f6a6d",
"d92c68c4c91045978a844a385fabf460",
"0f9255adf0d34f7696e63ee2df38bf84",
"149c9d18ed4345f0a25a8e53b5f9a5e0",
"0dc6a42170bf44f3a7969e742743e855",
"b36ec7216ff14185ab25062a493ea359",
"d3069e27bca648efb28e933633e3b08c",
"f1df8695f1cc435295f9dc1132e5873f",
"94ab26fcfb824d059bcbd2c069110ee6",
"7810ded833c44426a5cb0c9393fecd48",
"3c0e822201404757b3d882fd2780c11a",
"7f47e2ff3afb436f8dbad2e7bc2998e6",
"bb5ec38fda87415188120ed9835b8310",
"d873cce996a34de88f8f143eb6799f69",
"bfac220e7be240c9bdb14ca22da686f0",
"9405c7e2488344278cd3e4e1d4514adc",
"a4dd327fd61b4f5ebb2d71aa3d1305ee",
"12bdd5a63eae4adcb75e5d57c72d5286",
"865434d5312b41cd9455466994fac4ca",
"42aab02b4bbb4bb9910b5d6384e29dc8",
"46a349c086c540378613377ee041794e",
"64d616eb4e544b95b21e1ba4664ceb0a",
"69e9437309224134b533763be1aeba23",
"75536bd9f2d34090b9c3952dad2c3874",
"404c29f8b297461e9217d62398197b32",
"d9739f394b544eecab6d93bead4dbd2b",
"8325c18400f944fea3c6de047bc04d64",
"ad9aa41420b942f9bdf5514cf29d69d5",
"ac0f1d19b7dc495bb42d6642ad12763b",
"f69b5090b61d4f91af985580f9687535",
"145181b596ce42dab7f7998e5478bbf3",
"ecbcaeb68a364434851dfea480f7ccd0"
]
},
"id": "HIBZ9xZiK3St",
"outputId": "917bf05a-32d2-45e4-fc93-1984e51cd572"
},
"source": [
"from datasets import load_dataset\n",
"dataset = load_dataset('turkish_product_reviews', split = \"train[:10%]\")"
],
"execution_count": 2,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a186ec4ef0874b488da90b004f1f6a6d",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=900.0, style=ProgressStyle(description_…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "94ab26fcfb824d059bcbd2c069110ee6",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=576.0, style=ProgressStyle(description_…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Using custom data configuration default\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\n",
"Downloading and preparing dataset turkish_product_reviews/default (download: 12.57 MiB, generated: 41.36 MiB, post-processed: Unknown size, total: 53.93 MiB) to /root/.cache/huggingface/datasets/turkish_product_reviews/default/1.0.0/d1cbeaf7a7a02807d8e59d4af128f3186586e7e3f146c444e7e9e3ed38906511...\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a4dd327fd61b4f5ebb2d71aa3d1305ee",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=13184332.0, style=ProgressStyle(descrip…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "404c29f8b297461e9217d62398197b32",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\rDataset turkish_product_reviews downloaded and prepared to /root/.cache/huggingface/datasets/turkish_product_reviews/default/1.0.0/d1cbeaf7a7a02807d8e59d4af128f3186586e7e3f146c444e7e9e3ed38906511. Subsequent calls will reuse this data.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XJ72cXzCLDhw",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 262,
"referenced_widgets": [
"de5f26d5660f4fb499e7bc7a7ce9830a",
"1009d17d794f4a038aab2c8dba6a442e",
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"916054fca76b420e9ed3a4d0eea0709e",
"29a366d3bb7a453cb439ae999c76708a",
"ad8043f9ecff4b138c84b1f89834152a",
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"b56e1ec04ee2490d931ca472060711cd",
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]
},
"outputId": "273df393-d7a4-4acf-e715-0f47d1fc14e0"
},
"source": [
"from transformers import AutoTokenizer, AutoModel\n",
"\n",
"PRE_TRAINED_MODEL_NAME = 'savasy/bert-base-turkish-sentiment-cased'\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)\n",
"model = AutoModel.from_pretrained(PRE_TRAINED_MODEL_NAME)"
],
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "de5f26d5660f4fb499e7bc7a7ce9830a",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=596.0, style=ProgressStyle(description_…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "61aba16ed2874c36a53791127e9c5d21",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=262620.0, style=ProgressStyle(descripti…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "15d26adae4ac417e98368e1f630e856b",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=112.0, style=ProgressStyle(description_…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0656c8140b5b4f3ba681a11bdc75c4e4",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=39.0, style=ProgressStyle(description_w…"
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},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef8400c2714f45d78b0dae7b65852f6e",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=442523755.0, style=ProgressStyle(descri…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Pk5Xpm3XO7qN",
"outputId": "e33e709f-6b18-493b-be8f-2456a08743b0"
},
"source": [
"dataset"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['sentence', 'sentiment'],\n",
" num_rows: 23516\n",
"})"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "dXx_JX6hcitv",
"outputId": "a3d3244a-47a4-4f15-f246-5aa6b24ef162"
},
"source": [
"dataset[0][\"sentence\"]"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'beklentimin altında bir ürün kaliteli değil'"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "WgPCZakQPh7A"
},
"source": [
"def read_split(split_dir):\n",
" texts = []\n",
" labels = []\n",
" #values_dataset = list(dataset.values())\n",
" for i in dataset:\n",
" texts.append(i[\"sentence\"])\n",
" labels.append(i[\"sentiment\"])\n",
"\n",
" return texts, labels"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "rkAblmb9Pcxg"
},
"source": [
"texts, labels = read_split(dataset)"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "E3q2qWuyP3ls"
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"train_texts, test_texts, train_labels, test_labels = train_test_split(texts, labels, test_size=.2)"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "MZBLdEW1PWNw"
},
"source": [
"train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3PD9wQUTUzSe"
},
"source": [
"train_encodings = tokenizer(train_texts, truncation=True, padding=True)\n",
"val_encodings = tokenizer(val_texts, truncation=True, padding=True)\n",
"test_encodings = tokenizer(test_texts, truncation=True, padding=True)"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mvG9dbjZU686"
},
"source": [
"import torch\n",
"\n",
"class ReviewDataset(torch.utils.data.Dataset):\n",
" def __init__(self, encodings, labels):\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",
" item['labels'] = torch.tensor(self.labels[idx])\n",
" return item\n",
"\n",
" def __len__(self):\n",
" return len(self.labels)\n",
"\n",
"train_dataset = ReviewDataset(train_encodings, train_labels)\n",
"val_dataset = ReviewDataset(val_encodings, val_labels)\n",
"test_dataset = ReviewDataset(test_encodings, test_labels)"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NgV2GJqLlI6U",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "00f20032-4982-4c19-8919-a16a60f3e8b5"
},
"source": [
"from torch.utils.data import DataLoader\n",
"from transformers import AdamW\n",
"\n",
"device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
"\n",
"model = AutoModel.from_pretrained(PRE_TRAINED_MODEL_NAME)\n",
"model.to(device)\n",
"model.train()\n",
"\n",
"train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)\n",
"\n",
"optim = AdamW(model.parameters(), lr=5e-5)\n",
"\n",
"for epoch in range(3):\n",
" for batch in train_loader:\n",
" optim.zero_grad()\n",
" input_ids = batch['input_ids'].to(device)\n",
" attention_mask = batch['attention_mask'].to(device)\n",
" labels = batch['labels'].to(device)\n",
" outputs = model(input_ids, attention_mask=attention_mask)\n",
" loss = outputs[0]\n",
" loss.sum().backward()\n",
" optim.step()\n",
"\n",
"model.eval()"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"BertModel(\n",
" (embeddings): BertEmbeddings(\n",
" (word_embeddings): Embedding(32000, 768, padding_idx=0)\n",
" (position_embeddings): Embedding(512, 768)\n",
" (token_type_embeddings): Embedding(2, 768)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (encoder): BertEncoder(\n",
" (layer): ModuleList(\n",
" (0): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (1): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (2): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (3): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (4): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (5): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (6): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (7): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (8): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (9): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (10): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (11): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (pooler): BertPooler(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (activation): Tanh()\n",
" )\n",
")"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GOfMgeON39vs"
},
"source": [
"torch.save(model, \"/content/drive/MyDrive/Colab_Notebooks/pytorch_model\")"
],
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_vX9RGTffd5r"
},
"source": [
"review_text = \"Bu ürünü çok sevdim\"\n",
"encoded_review = tokenizer.encode_plus(review_text)"
],
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZI4a6SCrhzRo"
},
"source": [
"input_ids = encoded_review['input_ids']\n",
"attention_mask = encoded_review['attention_mask']"
],
"execution_count": 18,
"outputs": []
}
]
}
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