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"model_module": "@jupyter-widgets/controls",
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"state": {
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
|
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],
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"metadata": {
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"id": "ZXsOR6oJOJbd"
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}
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"id": "8l0hzptKNiZS",
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
|
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
|
||
"Collecting transformers\n",
|
||
" Downloading transformers-4.26.1-py3-none-any.whl (6.3 MB)\n",
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||
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||
"\u001b[?25hCollecting datasets\n",
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||
" Downloading datasets-2.9.0-py3-none-any.whl (462 kB)\n",
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||
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"\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n",
|
||
"Collecting sentencepiece\n",
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||
" Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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"Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\n",
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"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
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"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n",
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"Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\n",
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"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (23.0)\n",
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||
"Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
|
||
" Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
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"\u001b[?25hCollecting huggingface-hub<1.0,>=0.11.0\n",
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||
" Downloading huggingface_hub-0.12.1-py3-none-any.whl (190 kB)\n",
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"\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
|
||
"Collecting multiprocess\n",
|
||
" Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
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||
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||
"\u001b[?25hCollecting responses<0.19\n",
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||
" Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
|
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"Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.4)\n",
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||
"Requirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.3.6)\n",
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"Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0)\n",
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||
"Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2023.1.0)\n",
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||
"Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\n",
|
||
"Collecting xxhash\n",
|
||
" Downloading xxhash-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (213 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.5.0)\n",
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||
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (6.0.4)\n",
|
||
"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1)\n",
|
||
"Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2)\n",
|
||
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.3)\n",
|
||
"Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.8.2)\n",
|
||
"Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (3.0.1)\n",
|
||
"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (22.2.0)\n",
|
||
"Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (4.0.0)\n",
|
||
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.24.3)\n",
|
||
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
|
||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n",
|
||
"Collecting urllib3<1.27,>=1.21.1\n",
|
||
" Downloading urllib3-1.26.14-py2.py3-none-any.whl (140 kB)\n",
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n",
|
||
"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n",
|
||
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
|
||
"Installing collected packages: tokenizers, sentencepiece, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets\n",
|
||
" Attempting uninstall: urllib3\n",
|
||
" Found existing installation: urllib3 1.24.3\n",
|
||
" Uninstalling urllib3-1.24.3:\n",
|
||
" Successfully uninstalled urllib3-1.24.3\n",
|
||
"Successfully installed datasets-2.9.0 huggingface-hub-0.12.1 multiprocess-0.70.14 responses-0.18.0 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.1 urllib3-1.26.14 xxhash-3.2.0\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!pip install transformers datasets torch sentencepiece"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Załadowanie datasetu"
|
||
],
|
||
"metadata": {
|
||
"id": "dhN0rmb5Oi3d"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from datasets import load_dataset"
|
||
],
|
||
"metadata": {
|
||
"id": "tnaDkwZ2Pbnn"
|
||
},
|
||
"execution_count": 2,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"dataset = load_dataset(\"sms_spam\")"
|
||
],
|
||
"metadata": {
|
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"colab": {
|
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"base_uri": "https://localhost:8080/",
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"height": 231,
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"referenced_widgets": [
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"73f768ab287e4f328fe57ce0a0927421",
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"5a662f5392504b1086927daae497f2d8",
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"16dc82e83630455db87e5770a7091122",
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"4d90048e7c85439ab2010b70373f755e",
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"4dd0282a6a78470a9ec9c8a54afd23cf",
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"b99f35f8865e4aff9799f531f4742b91",
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"8a629a65f79a4cc0bcda69bb1169498a",
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"eec65f74a2794b0193d9b7fd50637c7d",
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"845ce63500824d968a630dbc5298c67d",
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"95ecf376b2814bf8b705b3ef4756209a",
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"bbcf5eb245cc47fcadfb2c2e42b9757d",
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|
||
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||
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|
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||
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||
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||
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||
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||
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|
||
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|
||
"id": "cCiAuRqrOkvV",
|
||
"outputId": "7694c09c-80a5-4b7f-dd31-57f5705242c8"
|
||
},
|
||
"execution_count": 3,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Downloading builder script: 0%| | 0.00/3.21k [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "ba71a0fb991849418b2e169e2774185b"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
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"Downloading metadata: 0%| | 0.00/1.69k [00:00<?, ?B/s]"
|
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],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "4d90048e7c85439ab2010b70373f755e"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
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"Downloading readme: 0%| | 0.00/4.87k [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "7a479cffc6a644c2b50a696ff72d4e8e"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Downloading and preparing dataset sms_spam/plain_text to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c...\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
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"Downloading data: 0%| | 0.00/203k [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "0cad760c82744f45a29dbf591953629d"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
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"Generating train split: 0%| | 0/5574 [00:00<?, ? examples/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "2181a8b3b8f349c4803784c65fa7f6d2"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Dataset sms_spam downloaded and prepared to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c. Subsequent calls will reuse this data.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
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"text/plain": [
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" 0%| | 0/1 [00:00<?, ?it/s]"
|
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],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "02ae7dbb11194f0e962ef177ef7b07fb"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"dataset['train'][0]"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "JKFHPko3OnAV",
|
||
"outputId": "c54f2007-8d9b-4d81-fd18-9b8493f8b995"
|
||
},
|
||
"execution_count": 4,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n',\n",
|
||
" 'label': 0}"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 4
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Przygotowanie datasetu"
|
||
],
|
||
"metadata": {
|
||
"id": "l140vJrgYxPr"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"parsed_dataset = []\n",
|
||
"\n",
|
||
"for row in dataset['train']:\n",
|
||
" text = row['sms'].replace(\"\\n\", \"\")\n",
|
||
" new_row = {}\n",
|
||
" new_row['sms'] = text\n",
|
||
" if row['label'] == 0:\n",
|
||
" new_row['label'] = \"False\"\n",
|
||
" else:\n",
|
||
" new_row['label'] = \"True\"\n",
|
||
" parsed_dataset.append(new_row)\n",
|
||
"\n",
|
||
"parsed_dataset[0]"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "1boUF-YiY3_y",
|
||
"outputId": "671699cc-459f-49f4-85da-08e560e58a21"
|
||
},
|
||
"execution_count": 5,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...',\n",
|
||
" 'label': 'False'}"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 5
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Tokenizer FLAN-T5"
|
||
],
|
||
"metadata": {
|
||
"id": "O-J-jBDxPJcn"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from transformers import AutoTokenizer"
|
||
],
|
||
"metadata": {
|
||
"id": "P23AYPX1PZ6g"
|
||
},
|
||
"execution_count": 6,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"tokenizer = AutoTokenizer.from_pretrained('google/flan-t5-base')"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 145,
|
||
"referenced_widgets": [
|
||
"f69c7f8b53cd4a9aa1a44a2f220f8124",
|
||
"436db3e83d4c454498bc795373a5c65a",
|
||
"06afa1bf473249dcbc5b134de98150c4",
|
||
"65d7557477024b139239d1302456153f",
|
||
"4725fcfe02864eab86c6a262e78d4780",
|
||
"5225f6a6c0fb4d868f47d3e1ea0b8231",
|
||
"30467358c0064d8c9120d81c443106a1",
|
||
"52c79b65b7e4428ba81eff3208823c96",
|
||
"ad34d606f9a04319ad658ad197ec286d",
|
||
"e4f4d6a4883241d994a6abf4b44f8512",
|
||
"824ca8a5e51f4ab589b5b44b42c157be",
|
||
"b5afbf6e7b2a4d6c89a696882c28d4ea",
|
||
"913f4ca89c6c4f26939633583a1a11f7",
|
||
"3f0d7098e94749f881a22e18390ba1fb",
|
||
"5c300d46cd4e4a1aad47f9ad125b1cde",
|
||
"c64029767f624b0b8659a7d18e385daa",
|
||
"3e4510548b0c419289cec1a1fb81042c",
|
||
"87a84314626a466e915643b432be6d8a",
|
||
"c82b8060b3f4441c8f5cf62555c01100",
|
||
"91eaed8fe62e462f93a9ec637f3dab53",
|
||
"cca593b390b043ff8dbb04136d572603",
|
||
"f797765fe659426bbc997e4a5f0e19da",
|
||
"c42d572cb8db484b862357539afcb3fe",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"8e72b89af13940afa31d069d072ca178",
|
||
"a6d374adc0be4bdc8c60961905554825",
|
||
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|
||
"c1a73269aeae4b459dfe6cdc1fb1d0fc",
|
||
"a93a627cf1404c6484006cb313bd561e",
|
||
"654d770c66e04e70a1ec1a9ec313d8b7",
|
||
"6415ae290c4040f7938e14d4260d452d",
|
||
"b82cd4e9e5da4e9d9285eb1cd88951a7",
|
||
"41961559d9df40d9bb5f628726a68029",
|
||
"61cd2f82626341e4bbdb09e90453b5fc",
|
||
"81385a7ae9614d3fbb21bf94287bd27f",
|
||
"af42d7ccf0064ca7938e0d6558b8b6f0",
|
||
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|
||
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|
||
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|
||
]
|
||
},
|
||
"id": "q5Jz0E_oPMBr",
|
||
"outputId": "ada39869-6643-4925-aea5-11672a2569ce"
|
||
},
|
||
"execution_count": 7,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Downloading (…)okenizer_config.json: 0%| | 0.00/2.54k [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
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"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "f69c7f8b53cd4a9aa1a44a2f220f8124"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Downloading (…)\"spiece.model\";: 0%| | 0.00/792k [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "b5afbf6e7b2a4d6c89a696882c28d4ea"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Downloading (…)/main/tokenizer.json: 0%| | 0.00/2.42M [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "c42d572cb8db484b862357539afcb3fe"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Downloading (…)cial_tokens_map.json: 0%| | 0.00/2.20k [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "a93a627cf1404c6484006cb313bd561e"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"sms = parsed_dataset[0]['sms']\n",
|
||
"print('Original: ', sms)\n",
|
||
"print('Tokenized: ', tokenizer.tokenize(sms))\n",
|
||
"print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sms)))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "dfxJQpoePsvI",
|
||
"outputId": "35709b9e-7919-4dc5-eae4-142e3ffd6326"
|
||
},
|
||
"execution_count": 8,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Original: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
|
||
"Tokenized: ['▁Go', '▁until', '▁jur', 'ong', '▁point', ',', '▁crazy', '.', '.', '▁Available', '▁only', '▁in', '▁bug', 'is', '▁', 'n', '▁great', '▁world', '▁la', '▁', 'e', '▁buffet', '...', '▁Cine', '▁there', '▁got', '▁', 'a', 'more', '▁wa', 't', '...']\n",
|
||
"Token IDs: [1263, 552, 10081, 2444, 500, 6, 6139, 5, 5, 8144, 163, 16, 8143, 159, 3, 29, 248, 296, 50, 3, 15, 15385, 233, 17270, 132, 530, 3, 9, 3706, 8036, 17, 233]\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Few shot learning"
|
||
],
|
||
"metadata": {
|
||
"id": "UpluhM8cU5Ir"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"print(parsed_dataset[0]) #0\n",
|
||
"print(parsed_dataset[123]) #1\n",
|
||
"print(parsed_dataset[2000]) #0\n",
|
||
"print(parsed_dataset[3002]) #1"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "7uNUkixPU85O",
|
||
"outputId": "2851ec7c-5446-446e-8a32-56a4101ac745"
|
||
},
|
||
"execution_count": 9,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...', 'label': 'False'}\n",
|
||
"{'sms': 'Todays Voda numbers ending 7548 are selected to receive a $350 award. If you have a match please call 08712300220 quoting claim code 4041 standard rates app', 'label': 'True'}\n",
|
||
"{'sms': \"LMAO where's your fish memory when I need it?\", 'label': 'False'}\n",
|
||
"{'sms': 'This message is free. Welcome to the new & improved Sex & Dogging club! To unsubscribe from this service reply STOP. msgs@150p 18+only', 'label': 'True'}\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"non_spam_1 = \"SMS: \" + parsed_dataset[0]['sms'] + \"\\nSpam: False\\n\\n\"\n",
|
||
"spam_1 = \"SMS: \" + parsed_dataset[123]['sms'] + \"\\nSpam: True\\n\\n\"\n",
|
||
"non_spam_2 = \"SMS: \" + parsed_dataset[2000]['sms'] + \"\\nSpam: False\\n\\n\"\n",
|
||
"spam_2 = \"SMS: \" + parsed_dataset[3002]['sms'] + \"\\nSpam: True\\n\\n\"\n",
|
||
"\n",
|
||
"few_shot_prefix = non_spam_1 + spam_1 + non_spam_2 + spam_2 + \"SMS: \"\n",
|
||
"print(few_shot_prefix)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "lj0issBznZfK",
|
||
"outputId": "bf21e565-f22d-48a6-d68b-422158de1f6b"
|
||
},
|
||
"execution_count": 10,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"SMS: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
|
||
"Spam: False\n",
|
||
"\n",
|
||
"SMS: Todays Voda numbers ending 7548 are selected to receive a $350 award. If you have a match please call 08712300220 quoting claim code 4041 standard rates app\n",
|
||
"Spam: True\n",
|
||
"\n",
|
||
"SMS: LMAO where's your fish memory when I need it?\n",
|
||
"Spam: False\n",
|
||
"\n",
|
||
"SMS: This message is free. Welcome to the new & improved Sex & Dogging club! To unsubscribe from this service reply STOP. msgs@150p 18+only\n",
|
||
"Spam: True\n",
|
||
"\n",
|
||
"SMS: \n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def check_class_balance(dataset):\n",
|
||
" spam_count = 0.0\n",
|
||
" not_spam_count = 0.0\n",
|
||
" for row in dataset:\n",
|
||
" if row['label'] == \"True\":\n",
|
||
" spam_count += 1.0\n",
|
||
" else:\n",
|
||
" not_spam_count += 1.0\n",
|
||
" return spam_count / not_spam_count"
|
||
],
|
||
"metadata": {
|
||
"id": "f2yGhxu80__k"
|
||
},
|
||
"execution_count": 11,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"parsed_dataset = parsed_dataset[1:123] + parsed_dataset[124:2000] + parsed_dataset[2001:3002] + parsed_dataset[3003:]\n",
|
||
"print(\"Spam to not spam messages ratio: {}\\n\".format(check_class_balance(parsed_dataset)))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "voYL96Ac1MQL",
|
||
"outputId": "bef4b6f9-7b4f-49e0-ac96-c8797a4ecc97"
|
||
},
|
||
"execution_count": 12,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Spam to not spam messages ratio: 0.1544041450777202\n",
|
||
"\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Load FLAN-T5 model"
|
||
],
|
||
"metadata": {
|
||
"id": "okTx_ynMV0rH"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from transformers import AutoModelForSeq2SeqLM"
|
||
],
|
||
"metadata": {
|
||
"id": "Eu-7Eed8WgN0"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"model = AutoModelForSeq2SeqLM.from_pretrained('google/flan-t5-base')\n",
|
||
"\n",
|
||
"model.cuda()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000,
|
||
"referenced_widgets": [
|
||
"8e60f2b1a9f7455db02eb846af52df63",
|
||
"71ff180cc81a4376b6a27455ed5cba26",
|
||
"4dc7c026d3e5469da3ada03bf77d7019",
|
||
"279308acf5be418ab7f25c3501739c9d",
|
||
"5c4c0f7bfb6c4a518cdfd3f1d70e77d5",
|
||
"7182f8c40174456db2d6f974e1a672a9",
|
||
"f34bf1f4156b4dec8990f16cbceccb00",
|
||
"bcf3f636355347338e1d57c191e2349f",
|
||
"e36598bd61874b4eb737d248df7c1212",
|
||
"f9c113d3c4f64aee969c897451b4522e",
|
||
"fc647c0c0bd14b87b59d539db3793d0a",
|
||
"3fcb6b4a0c454d59a6d5503cec84ba37",
|
||
"17d4993dc58949ca87a852e7e6c02e0c",
|
||
"db24a85aa3e24a2c9275b9aa4ac78faa",
|
||
"48a5bb74641a4a2fba370c0ddad1f6cb",
|
||
"1b0971b224444e2c8ddbf817f2f02131",
|
||
"07c9e3207eb04c5fb004744c3e4eb3b2",
|
||
"957e7a116bad4dee93ab449d7766d8ba",
|
||
"1a2ed54b1c584fa3bd2e0517a878790f",
|
||
"444add600a24462aa2272f2758ddb99d",
|
||
"98a9c4dc9bf349d1a8cf985c6a132530",
|
||
"4d94cb68eed54ffd97cc8272f70a92f3",
|
||
"c2a8099dd85e42599911991b467a7afe",
|
||
"72d0effc5d0948378103da4b2c8da5ae",
|
||
"cf3ac6e0e69643639d6b0a2577fff71a",
|
||
"d8f4949af74342d3b72200484ee3f1fb",
|
||
"ff42d87d84694bb2a6a7e63e43750029",
|
||
"ae619521ac64447b91b9fb26c46e696a",
|
||
"62a20c9c11f4452389dfdde24fa90d03",
|
||
"cb94c6469891495db93c332e2b707299",
|
||
"4e0b6f1f2b2f497d9b6e0183b0bc7ade",
|
||
"999d0e74507b4acfae447018abb03323",
|
||
"eaa726bb3b9d45d29c011053e844a97e"
|
||
]
|
||
},
|
||
"id": "JKv9O8kfV2zZ",
|
||
"outputId": "c902fc89-7c47-4eca-da89-411bc5b5e501"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Downloading (…)lve/main/config.json: 0%| | 0.00/1.40k [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "8e60f2b1a9f7455db02eb846af52df63"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Downloading (…)\"pytorch_model.bin\";: 0%| | 0.00/990M [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "3fcb6b4a0c454d59a6d5503cec84ba37"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Downloading (…)neration_config.json: 0%| | 0.00/147 [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "c2a8099dd85e42599911991b467a7afe"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"T5ForConditionalGeneration(\n",
|
||
" (shared): Embedding(32128, 768)\n",
|
||
" (encoder): T5Stack(\n",
|
||
" (embed_tokens): Embedding(32128, 768)\n",
|
||
" (block): ModuleList(\n",
|
||
" (0): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (relative_attention_bias): Embedding(32, 12)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (1): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (2): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (3): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (4): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (5): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (6): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (7): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (8): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (9): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (10): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (11): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (final_layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (decoder): T5Stack(\n",
|
||
" (embed_tokens): Embedding(32128, 768)\n",
|
||
" (block): ModuleList(\n",
|
||
" (0): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (relative_attention_bias): Embedding(32, 12)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (1): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (2): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (3): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (4): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (5): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (6): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (7): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (8): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (9): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (10): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (11): T5Block(\n",
|
||
" (layer): ModuleList(\n",
|
||
" (0): T5LayerSelfAttention(\n",
|
||
" (SelfAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (1): T5LayerCrossAttention(\n",
|
||
" (EncDecAttention): T5Attention(\n",
|
||
" (q): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (k): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (v): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" (o): Linear(in_features=768, out_features=768, bias=False)\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (2): T5LayerFF(\n",
|
||
" (DenseReluDense): T5DenseGatedActDense(\n",
|
||
" (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
|
||
" (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (act): NewGELUActivation()\n",
|
||
" )\n",
|
||
" (layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (final_layer_norm): T5LayerNorm()\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" )\n",
|
||
" (lm_head): Linear(in_features=768, out_features=32128, bias=False)\n",
|
||
")"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 12
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Helper functions"
|
||
],
|
||
"metadata": {
|
||
"id": "F_SDAwxoawDy"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import torch"
|
||
],
|
||
"metadata": {
|
||
"id": "rdWMg_KJZEZH"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def calculate_accuracy(preds, target):\n",
|
||
" results_ok = 0.0\n",
|
||
" results_false = 0.0\n",
|
||
"\n",
|
||
" for idx, pred in enumerate(preds):\n",
|
||
" if pred == target[idx]:\n",
|
||
" results_ok += 1.0\n",
|
||
" else:\n",
|
||
" results_false += 1.0\n",
|
||
"\n",
|
||
" return results_ok / (results_ok + results_false)"
|
||
],
|
||
"metadata": {
|
||
"id": "FzUi8908ax61"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"if torch.cuda.is_available(): \n",
|
||
" device = torch.device(\"cuda\")\n",
|
||
"\n",
|
||
" print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
|
||
" print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
|
||
"\n",
|
||
"else:\n",
|
||
" print('No GPU available, using the CPU instead.')\n",
|
||
" device = torch.device(\"cpu\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "86i7iRmtW-6L",
|
||
"outputId": "109072a9-ff0c-4ac0-fc74-4de30065af95"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"There are 1 GPU(s) available.\n",
|
||
"We will use the GPU: Tesla T4\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Predykcja"
|
||
],
|
||
"metadata": {
|
||
"id": "H_YI3bS3VHQE"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"predictions = []\n",
|
||
"expected = []\n",
|
||
"\n",
|
||
"for row in parsed_dataset:\n",
|
||
" input_text = few_shot_prefix + row['sms'] + \"\\nSpam: \"\n",
|
||
" input_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids.to(device)\n",
|
||
"\n",
|
||
" generated_ids = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=200)\n",
|
||
" generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)\n",
|
||
" \n",
|
||
" predictions.append(generated_text)\n",
|
||
" expected.append(row['label'])\n",
|
||
"\n",
|
||
"acc = calculate_accuracy(predictions, expected)\n",
|
||
"print(acc)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "vunLGuBGVGmh",
|
||
"outputId": "07945f48-6cf3-4762-a91a-df68ec0d599b"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"0.4601436265709156\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"print(\"Sample prediction: {}, expected: {}\".format(predictions[101], expected[101]))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "20WbYZmLaDl7",
|
||
"outputId": "6a5eb397-39ed-4095-8640-d76b6cdab520"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Sample prediction: False, expected: False\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# MCC Score"
|
||
],
|
||
"metadata": {
|
||
"id": "dLYc9WXz_B1o"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from sklearn.metrics import matthews_corrcoef\n",
|
||
"\n",
|
||
"print('Calculating Matthews Corr. Coef. for each batch...')\n",
|
||
"matthews = matthews_corrcoef(expected, predictions) \n",
|
||
"print('Total MCC: %.3f' % matthews)"
|
||
],
|
||
"metadata": {
|
||
"id": "hPEPpXXX_DXR",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"outputId": "9e0a324e-b880-4591-9243-dfb45ecb21cd"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Calculating Matthews Corr. Coef. for each batch...\n",
|
||
"Total MCC: -0.001\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Save model"
|
||
],
|
||
"metadata": {
|
||
"id": "GPhCp068_Iwq"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from google.colab import drive\n",
|
||
"\n",
|
||
"drive.mount('/content/gdrive/', force_remount=True)\n",
|
||
"\n",
|
||
"output_dir = '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model'\n",
|
||
"print(\"Saving model to %s\" % output_dir)\n",
|
||
"\n",
|
||
"model_to_save = model.module if hasattr(model, 'module') else model\n",
|
||
"model_to_save.save_pretrained(output_dir)\n",
|
||
"tokenizer.save_pretrained(output_dir)"
|
||
],
|
||
"metadata": {
|
||
"id": "avafCMoS_KDF",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"outputId": "16223c2c-299d-4059-8d6f-3ec2637f1185"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Mounted at /content/gdrive/\n",
|
||
"Saving model to /content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"('/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model/tokenizer_config.json',\n",
|
||
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model/special_tokens_map.json',\n",
|
||
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model/spiece.model',\n",
|
||
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model/added_tokens.json',\n",
|
||
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model/tokenizer.json')"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 23
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Bibliografia\n",
|
||
"- https://huggingface.co/docs/transformers/main/en/model_doc/flan-t5\n",
|
||
"- https://mccormickml.com/2019/07/22/BERT-fine-tuning/#a1-saving--loading-fine-tuned-model\n",
|
||
"- https://huggingface.co/docs/transformers/model_doc/t5#training"
|
||
],
|
||
"metadata": {
|
||
"id": "wHzm2_nDA6i-"
|
||
}
|
||
}
|
||
]
|
||
} |