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"c014c271cd374f62a0d113efca14001a": {
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"_view_name": "StyleView",
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{
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"source": [
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"# Instalacja pakietów"
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"id": "ZXsOR6oJOJbd"
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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" Found existing installation: urllib3 1.24.3\n",
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" Successfully uninstalled urllib3-1.24.3\n",
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"Successfully installed datasets-2.9.0 huggingface-hub-0.12.0 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"
|
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]
|
|||
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|
}
|
|||
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|
],
|
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"source": [
|
|||
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|
"!pip install transformers datasets torch sentencepiece"
|
|||
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|
]
|
|||
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|
},
|
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{
|
|||
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"cell_type": "markdown",
|
|||
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"source": [
|
|||
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|
"# Załadowanie datasetu"
|
|||
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|
],
|
|||
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|
"metadata": {
|
|||
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|
"id": "dhN0rmb5Oi3d"
|
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}
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},
|
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|
{
|
|||
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|
"cell_type": "code",
|
|||
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|
"source": [
|
|||
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|
"from datasets import load_dataset"
|
|||
|
|
],
|
|||
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|
"metadata": {
|
|||
|
|
"id": "tnaDkwZ2Pbnn"
|
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|
},
|
|||
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"execution_count": 2,
|
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"outputs": []
|
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|
},
|
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{
|
|||
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|
"cell_type": "code",
|
|||
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|
"source": [
|
|||
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|
"dataset = load_dataset(\"sms_spam\")"
|
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|
],
|
|||
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|
"metadata": {
|
|||
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"colab": {
|
|||
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"height": 244,
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"e4bb6f2f32de48d4b1f6d7ecf97ce376",
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"3305b30f1bfa48e9b0c1ba3add06094e",
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"a343619acbb745baa6aa561271ec8815",
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"c493fc54d9eb421f96d6e8519c8a1b0e",
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"12fd6daa8e604ecd9ad731b6296815da",
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"805f497aa3304353a1be8b4cc87bfb54",
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"236979048eb8461f9b7d8c4103905eed",
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"4620faf461ca449a88de45e86467e265",
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"8dc2f0f2e66741edbccab7abaaf634ce",
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"0d49bfb55dfd4090ba57c544eaa97902",
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"4084f218fca64fc9b765e04d0cc073ac",
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"fec67d95a5904ac199f6648a34ef1ff9",
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"a734d44f6baf4a5f8dfda576c09fffca",
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"3fc2d009da594365854aa11be804b388",
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"45c7d35932ec44bcb2ccdc73f0ae6b46",
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"0c1c3db046f24599b27daa44217f48d2",
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"c3f9ed611a154e22b875d0696ef6022d",
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"7fd51c34909b41a8884fa358cccdbf48",
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"9d9edc5471de40c2b7c2b9156637997d",
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"02c64bf3ff95438cb43c7dc52034cc43",
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"f0b71ecb6b1c4d1190ca899af7d3e82c",
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"8cae9c49e18f43248072b5e059429f6d",
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"88c8adca128b44c1bbbec437630c2074",
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"0f73f5e2c8af41eaae5f1c27f212da8b",
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"text": [
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"Downloading and preparing dataset sms_spam/plain_text to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c...\n"
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"name": "stdout",
|
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"text": [
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|
"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"
|
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]
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}
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]
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|
},
|
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|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"dataset['train'][123]"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "JKFHPko3OnAV",
|
|||
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|
"outputId": "423375ec-0be7-431b-8a65-f531df1d94d8"
|
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},
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|
"execution_count": 4,
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|||
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"outputs": [
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{
|
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|
"output_type": "execute_result",
|
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|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"{'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',\n",
|
|||
|
|
" 'label': 1}"
|
|||
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|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 4
|
|||
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|
}
|
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]
|
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},
|
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|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Modyfikacja datasetu - klasyfikacja zero-shot"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "l140vJrgYxPr"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"parsed_dataset = []\n",
|
|||
|
|
"\n",
|
|||
|
|
"for row in dataset['train']:\n",
|
|||
|
|
" text = \"Answer the question in one word - true if provided text is spam or false, if provided text is not spam.\\n Q: Is this text spam? \\nText: \" + row['sms'] + \"A: \"\n",
|
|||
|
|
" new_row = {}\n",
|
|||
|
|
" new_row['sms'] = text\n",
|
|||
|
|
" if row['label'] == 0:\n",
|
|||
|
|
" new_row['label'] = \"true\"\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" new_row['label'] = \"false\"\n",
|
|||
|
|
" parsed_dataset.append(new_row)\n",
|
|||
|
|
"\n",
|
|||
|
|
"parsed_dataset[0]"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "1boUF-YiY3_y",
|
|||
|
|
"outputId": "a7ecfad8-7c97-4de2-8852-80b28ea7c965"
|
|||
|
|
},
|
|||
|
|
"execution_count": 5,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"{'sms': 'Answer the question in one word - true if provided text is spam or false, if provided text is not spam.\\n Q: Is this text spam? \\nText: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\nA: ',\n",
|
|||
|
|
" 'label': 'true'}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"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": [
|
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|
"040698c1b4be4adebfe751751a64c11d",
|
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"11b4ddede4494fe093329c50097b7df5",
|
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|
"7986c625c9c34d039fad240333d0da08",
|
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|
"b35a6f8e81184f81990a0111cc5c6ca8",
|
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|
"2968992398a94adeb626eec2f45a8699",
|
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|
"c9f8f67483fb45c8a6b59fceae40fe42",
|
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"c5a862e7d71c430c865fa87dc21b1f6d",
|
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|
|
"d69171bdd6544c8ab9e96f3f3dfe7f92",
|
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|
"9cca59c67c5f452eb1d9e8dd22296968",
|
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"0f18ab0690ce4bc3b9b7c2f4d10c2049",
|
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|
"af26b98c1e3440daa11e104ed1c8b7e4",
|
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|
"be9060616d68475ea2e4674f7e460566",
|
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|
"264afd978de54dbeaa1b711e4c0b1c07",
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"bbcefda6eea74b8ea8313e4f7167df65",
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"68d85f6957ea4807a1fe7c82c9a7bc06",
|
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"cdfaf060f34a4d23afe97243d5f4c709",
|
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|
"9411023294ab44b284894aebf3f8f589",
|
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|
"d8bf78a3d7ea47a9824d6e01e70b0237",
|
|||
|
|
"b95ece9507db467fad710338bdf29177",
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|
"34a31746a99c4916b1402a2829e71a98",
|
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|
"29b2a7a233a9484c92056034cc70b8ca",
|
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|
"c9a72e2599e94e9e9bc2b4912d25877b",
|
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|
|
"2c24af439f3e45db8a0e004686e912ed",
|
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|
"dcb13b35232145fd85be1b6e4442cc5a",
|
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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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|
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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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"8c6fa802ddd5491581c092e5422bfeab",
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]
|
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},
|
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|
"id": "q5Jz0E_oPMBr",
|
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|
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|
"execution_count": 7,
|
|||
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|
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|
{
|
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|
"output_type": "display_data",
|
|||
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|
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"output_type": "display_data",
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"data": {
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"metadata": {}
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]
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},
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{
|
|||
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|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"sms = parsed_dataset[0]['sms']\n",
|
|||
|
|
"print('Original: \\n', 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",
|
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|
|
"outputId": "2ce963db-ed94-467a-e859-fd0515849ee2"
|
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|
|
},
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Original: \n",
|
|||
|
|
" Answer the question in one word - true if provided text is spam or false, if provided text is not spam.\n",
|
|||
|
|
" Q: Is this text spam? \n",
|
|||
|
|
"Text: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
|
|||
|
|
"A: \n",
|
|||
|
|
"Tokenized: ['▁Answer', '▁the', '▁question', '▁in', '▁one', '▁word', '▁', '-', '▁true', '▁', 'if', '▁provided', '▁text', '▁is', '▁spam', '▁or', '▁false', ',', '▁', 'if', '▁provided', '▁text', '▁is', '▁not', '▁spam', '.', '▁Q', ':', '▁I', 's', '▁this', '▁text', '▁spam', '?', '▁Text', ':', '▁Go', '▁until', '▁jur', 'ong', '▁point', ',', '▁crazy', '.', '.', '▁Available', '▁only', '▁in', '▁bug', 'is', '▁', 'n', '▁great', '▁world', '▁la', '▁', 'e', '▁buffet', '...', '▁Cine', '▁there', '▁got', '▁', 'a', 'more', '▁wa', 't', '...', '▁A', ':', '▁']\n",
|
|||
|
|
"Token IDs: [11801, 8, 822, 16, 80, 1448, 3, 18, 1176, 3, 99, 937, 1499, 19, 13655, 42, 6136, 6, 3, 99, 937, 1499, 19, 59, 13655, 5, 1593, 10, 27, 7, 48, 1499, 13655, 58, 5027, 10, 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, 71, 10, 3]\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Check maximum lenght of a sentence"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "UpluhM8cU5Ir"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"max_len = 0\n",
|
|||
|
|
"\n",
|
|||
|
|
"for sentence in parsed_dataset:\n",
|
|||
|
|
" input_ids = tokenizer.encode(sentence['sms'], add_special_tokens=True)\n",
|
|||
|
|
" max_len = max(max_len, len(input_ids))\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('Max sentence length: ', max_len)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "7uNUkixPU85O",
|
|||
|
|
"outputId": "352b31bb-164e-42a8-a8b0-6be9d3eca0c4"
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Max sentence length: 377\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"max_label_len = 0\n",
|
|||
|
|
"\n",
|
|||
|
|
"for sentence in parsed_dataset:\n",
|
|||
|
|
" input_ids = tokenizer.encode(sentence['label'], add_special_tokens=True)\n",
|
|||
|
|
" max_label_len = max(max_label_len, len(input_ids))\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('Max sentence length: ', max_label_len)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "lj0issBznZfK",
|
|||
|
|
"outputId": "53defde8-ed8d-4927-add4-5d7d63f737df"
|
|||
|
|
},
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Max sentence length: 2\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Pre train tokenization"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "nfw62HdgSERb"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"import torch"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "KTXYalS1VLqH"
|
|||
|
|
},
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"input_ids = []\n",
|
|||
|
|
"target_ids = []\n",
|
|||
|
|
"attention_masks = []\n",
|
|||
|
|
"\n",
|
|||
|
|
"for sentence in parsed_dataset:\n",
|
|||
|
|
" encoded_dict = tokenizer.encode_plus(\n",
|
|||
|
|
" sentence['sms'],\n",
|
|||
|
|
" add_special_tokens = True,\n",
|
|||
|
|
" max_length = 380,\n",
|
|||
|
|
" padding = 'max_length',\n",
|
|||
|
|
" truncation=True,\n",
|
|||
|
|
" return_attention_mask = True,\n",
|
|||
|
|
" return_tensors = 'pt',\n",
|
|||
|
|
" )\n",
|
|||
|
|
" \n",
|
|||
|
|
" encoded_target_dict = tokenizer.encode_plus(\n",
|
|||
|
|
" sentence['label'],\n",
|
|||
|
|
" add_special_tokens = True,\n",
|
|||
|
|
" max_length = 2,\n",
|
|||
|
|
" padding = 'max_length',\n",
|
|||
|
|
" truncation=True,\n",
|
|||
|
|
" return_attention_mask = True,\n",
|
|||
|
|
" return_tensors = 'pt',\n",
|
|||
|
|
" )\n",
|
|||
|
|
" \n",
|
|||
|
|
" input_ids.append(encoded_dict['input_ids'])\n",
|
|||
|
|
" target_ids.append(encoded_target_dict['input_ids'])\n",
|
|||
|
|
" attention_masks.append(encoded_dict['attention_mask'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"input_ids = torch.cat(input_ids, dim=0)\n",
|
|||
|
|
"target_ids = torch.cat(target_ids, dim=0)\n",
|
|||
|
|
"attention_masks = torch.cat(attention_masks, dim=0)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('Original: ', parsed_dataset[0])\n",
|
|||
|
|
"print('Token IDs:', input_ids[0])\n",
|
|||
|
|
"print('Label token IDs:', target_ids[0])"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "Z28QYfLnSGxR",
|
|||
|
|
"outputId": "eb9d48d0-cb2d-4596-c752-14b18f6e3590"
|
|||
|
|
},
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Original: {'sms': 'Answer the question in one word - true if provided text is spam or false, if provided text is not spam.\\n Q: Is this text spam? \\nText: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\nA: ', 'label': 'true'}\n",
|
|||
|
|
"Token IDs: tensor([11801, 8, 822, 16, 80, 1448, 3, 18, 1176, 3,\n",
|
|||
|
|
" 99, 937, 1499, 19, 13655, 42, 6136, 6, 3, 99,\n",
|
|||
|
|
" 937, 1499, 19, 59, 13655, 5, 1593, 10, 27, 7,\n",
|
|||
|
|
" 48, 1499, 13655, 58, 5027, 10, 1263, 552, 10081, 2444,\n",
|
|||
|
|
" 500, 6, 6139, 5, 5, 8144, 163, 16, 8143, 159,\n",
|
|||
|
|
" 3, 29, 248, 296, 50, 3, 15, 15385, 233, 17270,\n",
|
|||
|
|
" 132, 530, 3, 9, 3706, 8036, 17, 233, 71, 10,\n",
|
|||
|
|
" 3, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|||
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
|
|||
|
|
"Label token IDs: tensor([1176, 1])\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Split dataset"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "qD_t0y0KVVSy"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from torch.utils.data import TensorDataset, random_split"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "vN_SatRIVa4c"
|
|||
|
|
},
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"dataset = TensorDataset(input_ids, attention_masks, target_ids)\n",
|
|||
|
|
"\n",
|
|||
|
|
"test_size = 1000\n",
|
|||
|
|
"dataset_len = len(dataset)\n",
|
|||
|
|
"train_size = int(0.9 * (dataset_len-test_size))\n",
|
|||
|
|
"val_size = (dataset_len-test_size) - train_size\n",
|
|||
|
|
"\n",
|
|||
|
|
"test_dataset, train_dataset, val_dataset = random_split(dataset, [test_size, train_size, val_size])\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('{:>5,} test samples'.format(test_size))\n",
|
|||
|
|
"print('{:>5,} training samples'.format(train_size))\n",
|
|||
|
|
"print('{:>5,} validation samples'.format(val_size))"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "Mm6vc6lLVW3l",
|
|||
|
|
"outputId": "023efb5b-eab3-4675-9900-3918aedae90f"
|
|||
|
|
},
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"1,000 test samples\n",
|
|||
|
|
"4,116 training samples\n",
|
|||
|
|
" 458 validation samples\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Create train and validation loaders"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "bmgQOP4EVfA1"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from torch.utils.data import DataLoader, RandomSampler, SequentialSampler"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "CxnQ3cmIVlNh"
|
|||
|
|
},
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"batch_size = 8\n",
|
|||
|
|
"\n",
|
|||
|
|
"train_dataloader = DataLoader(\n",
|
|||
|
|
" train_dataset,\n",
|
|||
|
|
" sampler = RandomSampler(train_dataset),\n",
|
|||
|
|
" batch_size = batch_size\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
"validation_dataloader = DataLoader(\n",
|
|||
|
|
" val_dataset,\n",
|
|||
|
|
" sampler = SequentialSampler(val_dataset),\n",
|
|||
|
|
" batch_size = batch_size\n",
|
|||
|
|
" )"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "0hcpO_onVjEC"
|
|||
|
|
},
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Device check"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "efwhqLyyVu9z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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": "ANBCfNGnVwVk",
|
|||
|
|
"outputId": "6192e88f-5e61-4de6-b476-de9a6e3a59a6"
|
|||
|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"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": [
|
|||
|
|
"# Load FLAN-T5 model"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "okTx_ynMV0rH"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from transformers import AutoModelForSeq2SeqLM"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Eu-7Eed8WgN0"
|
|||
|
|
},
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"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": [
|
|||
|
|
"3747d0aa68d642449ff32b7efd47d497",
|
|||
|
|
"2a84ccf3660a4e24b37bfc1d952f06d9",
|
|||
|
|
"43b98452768846eb9b773839a4681cc6",
|
|||
|
|
"2dfbcef644a84b558ce99b2ddaf091b6",
|
|||
|
|
"9dd02373a1994433a49e0015b76dd858",
|
|||
|
|
"42a620dfccb4416c95fbd7a76637dc73",
|
|||
|
|
"9d953b41e24c4263b18e1ff2174eb797",
|
|||
|
|
"cb409960af114fa690bac72c1d51d2ab",
|
|||
|
|
"995a94c35b4c441b86598d1976281711",
|
|||
|
|
"4ea2ccb5fa264dd5a042f570c22db7de",
|
|||
|
|
"4d5668e5b66d4993b935b077a1dc281c",
|
|||
|
|
"cf36c750119449e8a16aa97a9b66a124",
|
|||
|
|
"0de691bf20804830ba5e6710c205c9ac",
|
|||
|
|
"7ee532c002784554b12fede964c6c05a",
|
|||
|
|
"25e5c76c84a8414495b61663250825aa",
|
|||
|
|
"12bd0cd379754967bfd5481d5836b7a4",
|
|||
|
|
"d55d326ffbdc420da0a399c141f7fb56",
|
|||
|
|
"092323b59b734eada8d88197d9f0eb72",
|
|||
|
|
"622df288b2ae4f62b776cb101de18b9f",
|
|||
|
|
"c72abd216b9444ef9bdb0f8a0a6771b0",
|
|||
|
|
"03d5f2402e464b7cb5244db0f824df1d",
|
|||
|
|
"4a6e196f590d4b329e0edefc910d4730",
|
|||
|
|
"9a04f6723e2a40288eb42eea0134dfaf",
|
|||
|
|
"4065d0eed87e4d268de8f4536287e225",
|
|||
|
|
"bebca147a83446abaf19c3e3db11744e",
|
|||
|
|
"cf525a599ca245b19204bfff3fa1bd11",
|
|||
|
|
"b60759f0730343849ef3e51d2c8be38c",
|
|||
|
|
"dcc795e3fac0401b9a4d2aac3bd6e8cc",
|
|||
|
|
"ed7ca6da408842b38a466097ec9d4616",
|
|||
|
|
"0335f29f40564adf9b577707dfb40aa9",
|
|||
|
|
"a200da1478fa46a19695a5b2f2c77fd1",
|
|||
|
|
"e21e75bbbc4149b4ac85fece44aee355",
|
|||
|
|
"c014c271cd374f62a0d113efca14001a"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "JKv9O8kfV2zZ",
|
|||
|
|
"outputId": "6893e79a-48f7-4713-c4a4-a9558acbcf7c"
|
|||
|
|
},
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"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": "3747d0aa68d642449ff32b7efd47d497"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"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": "cf36c750119449e8a16aa97a9b66a124"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"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": "9a04f6723e2a40288eb42eea0134dfaf"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"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": 21
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Helper functions"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "F_SDAwxoawDy"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"import datetime\n",
|
|||
|
|
"import numpy as np"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "s-q6_F38bLVA"
|
|||
|
|
},
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"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)\n",
|
|||
|
|
"\n",
|
|||
|
|
"def format_time(elapsed):\n",
|
|||
|
|
" '''\n",
|
|||
|
|
" Takes a time in seconds and returns a string hh:mm:ss\n",
|
|||
|
|
" '''\n",
|
|||
|
|
" elapsed_rounded = int(round((elapsed)))\n",
|
|||
|
|
" return str(datetime.timedelta(seconds=elapsed_rounded))"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "FzUi8908ax61"
|
|||
|
|
},
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Init training"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "ucChBa-9bXJy"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from transformers import get_linear_schedule_with_warmup"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "c9e7rbGwbdEp"
|
|||
|
|
},
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"optimizer = torch.optim.AdamW(model.parameters(),\n",
|
|||
|
|
" lr = 3e-4,\n",
|
|||
|
|
" eps = 1e-8\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
"epochs = 4\n",
|
|||
|
|
"\n",
|
|||
|
|
"total_steps = len(train_dataloader) * epochs\n",
|
|||
|
|
"\n",
|
|||
|
|
"scheduler = get_linear_schedule_with_warmup(optimizer, \n",
|
|||
|
|
" num_warmup_steps = 0,\n",
|
|||
|
|
" num_training_steps = total_steps)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "A7XUF4PNbYy8"
|
|||
|
|
},
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Training"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "DAzQWODja0A3"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"import random\n",
|
|||
|
|
"import time"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Hoa7NlU0bI7G"
|
|||
|
|
},
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"# This training code is based on the `run_glue.py` script here:\n",
|
|||
|
|
"# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128\n",
|
|||
|
|
"\n",
|
|||
|
|
"seed_val = 42\n",
|
|||
|
|
"\n",
|
|||
|
|
"random.seed(seed_val)\n",
|
|||
|
|
"np.random.seed(seed_val)\n",
|
|||
|
|
"torch.manual_seed(seed_val)\n",
|
|||
|
|
"torch.cuda.manual_seed_all(seed_val)\n",
|
|||
|
|
"\n",
|
|||
|
|
"training_stats = []\n",
|
|||
|
|
"total_t0 = time.time()\n",
|
|||
|
|
"\n",
|
|||
|
|
"for epoch_i in range(0, epochs):\n",
|
|||
|
|
" \n",
|
|||
|
|
" # ========================================\n",
|
|||
|
|
" # Training\n",
|
|||
|
|
" # ========================================\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(\"\")\n",
|
|||
|
|
" print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n",
|
|||
|
|
" print('Training...')\n",
|
|||
|
|
"\n",
|
|||
|
|
" t0 = time.time()\n",
|
|||
|
|
"\n",
|
|||
|
|
" total_train_loss = 0\n",
|
|||
|
|
" total_train_acc = 0\n",
|
|||
|
|
"\n",
|
|||
|
|
" model.train()\n",
|
|||
|
|
"\n",
|
|||
|
|
" for step, batch in enumerate(train_dataloader):\n",
|
|||
|
|
" if step % 40 == 0 and not step == 0:\n",
|
|||
|
|
" elapsed = format_time(time.time() - t0)\n",
|
|||
|
|
" print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
" b_input_ids = batch[0].to(device)\n",
|
|||
|
|
" b_input_mask = batch[1].to(device)\n",
|
|||
|
|
"\n",
|
|||
|
|
" y = batch[2].to(device)\n",
|
|||
|
|
" y_ids = y[:, :-1].contiguous()\n",
|
|||
|
|
" lm_labels = y[:, 1:].clone().detach()\n",
|
|||
|
|
" lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100\n",
|
|||
|
|
"\n",
|
|||
|
|
" model.zero_grad() \n",
|
|||
|
|
"\n",
|
|||
|
|
" outputs = model(\n",
|
|||
|
|
" input_ids=b_input_ids,\n",
|
|||
|
|
" attention_mask=b_input_mask,\n",
|
|||
|
|
" decoder_input_ids=y_ids,\n",
|
|||
|
|
" labels=lm_labels\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" generated_ids = model.generate(\n",
|
|||
|
|
" input_ids = b_input_ids,\n",
|
|||
|
|
" attention_mask = b_input_mask, \n",
|
|||
|
|
" max_length=2, \n",
|
|||
|
|
" num_beams=2,\n",
|
|||
|
|
" repetition_penalty=2.5, \n",
|
|||
|
|
" length_penalty=1.0, \n",
|
|||
|
|
" early_stopping=True\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n",
|
|||
|
|
" target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]\n",
|
|||
|
|
" total_train_acc += calculate_accuracy(preds, target) \n",
|
|||
|
|
"\n",
|
|||
|
|
" loss = outputs['loss']\n",
|
|||
|
|
" total_train_loss += loss.item()\n",
|
|||
|
|
"\n",
|
|||
|
|
" loss.backward()\n",
|
|||
|
|
" torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
|
|||
|
|
"\n",
|
|||
|
|
" optimizer.step()\n",
|
|||
|
|
" scheduler.step()\n",
|
|||
|
|
"\n",
|
|||
|
|
" avg_train_loss = total_train_loss / len(train_dataloader) \n",
|
|||
|
|
" avg_train_acc = total_train_acc / len(train_dataloader) \n",
|
|||
|
|
" \n",
|
|||
|
|
" training_time = format_time(time.time() - t0)\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(\"\")\n",
|
|||
|
|
" print(\" Average training loss: {0:.2f}\".format(avg_train_loss))\n",
|
|||
|
|
" print(\" Average training acc: {0:.2f}\".format(avg_train_acc))\n",
|
|||
|
|
" print(\" Training epcoh took: {:}\".format(training_time))\n",
|
|||
|
|
" \n",
|
|||
|
|
" # ========================================\n",
|
|||
|
|
" # Validation\n",
|
|||
|
|
" # ========================================\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(\"\")\n",
|
|||
|
|
" print(\"Running Validation...\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" t0 = time.time()\n",
|
|||
|
|
" model.eval()\n",
|
|||
|
|
"\n",
|
|||
|
|
" total_eval_loss = 0\n",
|
|||
|
|
" total_eval_accuracy = 0\n",
|
|||
|
|
"\n",
|
|||
|
|
" for batch in validation_dataloader:\n",
|
|||
|
|
"\n",
|
|||
|
|
" b_input_ids = batch[0].to(device)\n",
|
|||
|
|
" b_input_mask = batch[1].to(device)\n",
|
|||
|
|
"\n",
|
|||
|
|
" y = batch[2].to(device)\n",
|
|||
|
|
" y_ids = y[:, :-1].contiguous()\n",
|
|||
|
|
" lm_labels = y[:, 1:].clone().detach()\n",
|
|||
|
|
" lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100\n",
|
|||
|
|
" \n",
|
|||
|
|
" with torch.no_grad(): \n",
|
|||
|
|
"\n",
|
|||
|
|
" outputs = model(\n",
|
|||
|
|
" input_ids=b_input_ids,\n",
|
|||
|
|
" attention_mask=b_input_mask,\n",
|
|||
|
|
" decoder_input_ids=y_ids,\n",
|
|||
|
|
" labels=lm_labels\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" loss = outputs['loss']\n",
|
|||
|
|
" total_eval_loss += loss.item()\n",
|
|||
|
|
"\n",
|
|||
|
|
" generated_ids = model.generate(\n",
|
|||
|
|
" input_ids = b_input_ids,\n",
|
|||
|
|
" attention_mask = b_input_mask, \n",
|
|||
|
|
" max_length=2, \n",
|
|||
|
|
" num_beams=2,\n",
|
|||
|
|
" repetition_penalty=2.5, \n",
|
|||
|
|
" length_penalty=1.0, \n",
|
|||
|
|
" early_stopping=True\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n",
|
|||
|
|
" target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]\n",
|
|||
|
|
" total_eval_accuracy += calculate_accuracy(preds, target) \n",
|
|||
|
|
"\n",
|
|||
|
|
" avg_val_loss = total_eval_loss / len(validation_dataloader)\n",
|
|||
|
|
"\n",
|
|||
|
|
" avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)\n",
|
|||
|
|
" print(\" Accuracy: {0:.2f}\".format(avg_val_accuracy))\n",
|
|||
|
|
" \n",
|
|||
|
|
" validation_time = format_time(time.time() - t0)\n",
|
|||
|
|
" print(\" Validation took: {:}\".format(validation_time))\n",
|
|||
|
|
" print(\" Validation Loss: {0:.2f}\".format(avg_val_loss))\n",
|
|||
|
|
"\n",
|
|||
|
|
" training_stats.append(\n",
|
|||
|
|
" {\n",
|
|||
|
|
" 'epoch': epoch_i + 1,\n",
|
|||
|
|
" 'Training Loss': avg_train_loss,\n",
|
|||
|
|
" 'Training Accur.': avg_train_acc,\n",
|
|||
|
|
" 'Valid. Loss': avg_val_loss,\n",
|
|||
|
|
" 'Valid. Accur.': avg_val_accuracy,\n",
|
|||
|
|
" 'Training Time': training_time,\n",
|
|||
|
|
" 'Validation Time': validation_time\n",
|
|||
|
|
" }\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"\")\n",
|
|||
|
|
"print(\"Training complete!\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "xsHxfslka1u5",
|
|||
|
|
"outputId": "28c30ee0-6f41-4ede-eb3a-eebd4269c332"
|
|||
|
|
},
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 1 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 515. Elapsed: 0:00:46.\n",
|
|||
|
|
" Batch 80 of 515. Elapsed: 0:01:30.\n",
|
|||
|
|
" Batch 120 of 515. Elapsed: 0:02:14.\n",
|
|||
|
|
" Batch 160 of 515. Elapsed: 0:02:59.\n",
|
|||
|
|
" Batch 200 of 515. Elapsed: 0:03:44.\n",
|
|||
|
|
" Batch 240 of 515. Elapsed: 0:04:28.\n",
|
|||
|
|
" Batch 280 of 515. Elapsed: 0:05:13.\n",
|
|||
|
|
" Batch 320 of 515. Elapsed: 0:05:57.\n",
|
|||
|
|
" Batch 360 of 515. Elapsed: 0:06:42.\n",
|
|||
|
|
" Batch 400 of 515. Elapsed: 0:07:26.\n",
|
|||
|
|
" Batch 440 of 515. Elapsed: 0:08:11.\n",
|
|||
|
|
" Batch 480 of 515. Elapsed: 0:08:55.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.01\n",
|
|||
|
|
" Average training acc: 0.59\n",
|
|||
|
|
" Training epcoh took: 0:09:34\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.47\n",
|
|||
|
|
" Validation took: 0:00:31\n",
|
|||
|
|
" Validation Loss: 0.00\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 2 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 515. Elapsed: 0:00:44.\n",
|
|||
|
|
" Batch 80 of 515. Elapsed: 0:01:29.\n",
|
|||
|
|
" Batch 120 of 515. Elapsed: 0:02:13.\n",
|
|||
|
|
" Batch 160 of 515. Elapsed: 0:02:58.\n",
|
|||
|
|
" Batch 200 of 515. Elapsed: 0:03:42.\n",
|
|||
|
|
" Batch 240 of 515. Elapsed: 0:04:27.\n",
|
|||
|
|
" Batch 280 of 515. Elapsed: 0:05:11.\n",
|
|||
|
|
" Batch 320 of 515. Elapsed: 0:05:56.\n",
|
|||
|
|
" Batch 360 of 515. Elapsed: 0:06:40.\n",
|
|||
|
|
" Batch 400 of 515. Elapsed: 0:07:25.\n",
|
|||
|
|
" Batch 440 of 515. Elapsed: 0:08:09.\n",
|
|||
|
|
" Batch 480 of 515. Elapsed: 0:08:54.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.00\n",
|
|||
|
|
" Average training acc: 0.59\n",
|
|||
|
|
" Training epcoh took: 0:09:32\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.46\n",
|
|||
|
|
" Validation took: 0:00:31\n",
|
|||
|
|
" Validation Loss: 0.00\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 3 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 515. Elapsed: 0:00:44.\n",
|
|||
|
|
" Batch 80 of 515. Elapsed: 0:01:34.\n",
|
|||
|
|
" Batch 120 of 515. Elapsed: 0:02:20.\n",
|
|||
|
|
" Batch 160 of 515. Elapsed: 0:03:05.\n",
|
|||
|
|
" Batch 200 of 515. Elapsed: 0:03:49.\n",
|
|||
|
|
" Batch 240 of 515. Elapsed: 0:04:34.\n",
|
|||
|
|
" Batch 280 of 515. Elapsed: 0:05:18.\n",
|
|||
|
|
" Batch 320 of 515. Elapsed: 0:06:03.\n",
|
|||
|
|
" Batch 360 of 515. Elapsed: 0:06:47.\n",
|
|||
|
|
" Batch 400 of 515. Elapsed: 0:07:32.\n",
|
|||
|
|
" Batch 440 of 515. Elapsed: 0:08:16.\n",
|
|||
|
|
" Batch 480 of 515. Elapsed: 0:09:00.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.00\n",
|
|||
|
|
" Average training acc: 0.59\n",
|
|||
|
|
" Training epcoh took: 0:09:39\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.46\n",
|
|||
|
|
" Validation took: 0:00:31\n",
|
|||
|
|
" Validation Loss: 0.00\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 4 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 515. Elapsed: 0:00:45.\n",
|
|||
|
|
" Batch 80 of 515. Elapsed: 0:01:29.\n",
|
|||
|
|
" Batch 120 of 515. Elapsed: 0:02:14.\n",
|
|||
|
|
" Batch 160 of 515. Elapsed: 0:02:58.\n",
|
|||
|
|
" Batch 200 of 515. Elapsed: 0:03:42.\n",
|
|||
|
|
" Batch 240 of 515. Elapsed: 0:04:27.\n",
|
|||
|
|
" Batch 280 of 515. Elapsed: 0:05:11.\n",
|
|||
|
|
" Batch 320 of 515. Elapsed: 0:05:56.\n",
|
|||
|
|
" Batch 360 of 515. Elapsed: 0:06:40.\n",
|
|||
|
|
" Batch 400 of 515. Elapsed: 0:07:24.\n",
|
|||
|
|
" Batch 440 of 515. Elapsed: 0:08:09.\n",
|
|||
|
|
" Batch 480 of 515. Elapsed: 0:08:53.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.00\n",
|
|||
|
|
" Average training acc: 0.58\n",
|
|||
|
|
" Training epcoh took: 0:09:32\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.46\n",
|
|||
|
|
" Validation took: 0:00:31\n",
|
|||
|
|
" Validation Loss: 0.00\n",
|
|||
|
|
"\n",
|
|||
|
|
"Training complete!\n",
|
|||
|
|
"Total training took 0:40:22 (h:mm:ss)\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Train summary"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "xIpFPoRb91Or"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"\n",
|
|||
|
|
"pd.set_option('precision', 2)\n",
|
|||
|
|
"df_stats = pd.DataFrame(data=training_stats)\n",
|
|||
|
|
"\n",
|
|||
|
|
"df_stats = df_stats.set_index('epoch')\n",
|
|||
|
|
"df_stats"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 206
|
|||
|
|
},
|
|||
|
|
"id": "GjYqBrrO93Oh",
|
|||
|
|
"outputId": "0087ee68-c017-41fd-db84-ca6e0d25fb12"
|
|||
|
|
},
|
|||
|
|
"execution_count": 28,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
" Training Loss Training Accur. Valid. Loss Valid. Accur. \\\n",
|
|||
|
|
"epoch \n",
|
|||
|
|
"1 5.27e-03 0.59 0.0 0.47 \n",
|
|||
|
|
"2 2.74e-08 0.59 0.0 0.46 \n",
|
|||
|
|
"3 1.58e-08 0.59 0.0 0.46 \n",
|
|||
|
|
"4 1.55e-08 0.58 0.0 0.46 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Training Time Validation Time \n",
|
|||
|
|
"epoch \n",
|
|||
|
|
"1 0:09:34 0:00:31 \n",
|
|||
|
|
"2 0:09:32 0:00:31 \n",
|
|||
|
|
"3 0:09:39 0:00:31 \n",
|
|||
|
|
"4 0:09:32 0:00:31 "
|
|||
|
|
],
|
|||
|
|
"text/html": [
|
|||
|
|
"\n",
|
|||
|
|
" <div id=\"df-ca1b78cf-f532-4d4d-b437-30d6fbef7e05\">\n",
|
|||
|
|
" <div class=\"colab-df-container\">\n",
|
|||
|
|
" <div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Training Loss</th>\n",
|
|||
|
|
" <th>Training Accur.</th>\n",
|
|||
|
|
" <th>Valid. Loss</th>\n",
|
|||
|
|
" <th>Valid. Accur.</th>\n",
|
|||
|
|
" <th>Training Time</th>\n",
|
|||
|
|
" <th>Validation Time</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>epoch</th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>5.27e-03</td>\n",
|
|||
|
|
" <td>0.59</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.47</td>\n",
|
|||
|
|
" <td>0:09:34</td>\n",
|
|||
|
|
" <td>0:00:31</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>2.74e-08</td>\n",
|
|||
|
|
" <td>0.59</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.46</td>\n",
|
|||
|
|
" <td>0:09:32</td>\n",
|
|||
|
|
" <td>0:00:31</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>1.58e-08</td>\n",
|
|||
|
|
" <td>0.59</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.46</td>\n",
|
|||
|
|
" <td>0:09:39</td>\n",
|
|||
|
|
" <td>0:00:31</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>1.55e-08</td>\n",
|
|||
|
|
" <td>0.58</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.46</td>\n",
|
|||
|
|
" <td>0:09:32</td>\n",
|
|||
|
|
" <td>0:00:31</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>\n",
|
|||
|
|
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca1b78cf-f532-4d4d-b437-30d6fbef7e05')\"\n",
|
|||
|
|
" title=\"Convert this dataframe to an interactive table.\"\n",
|
|||
|
|
" style=\"display:none;\">\n",
|
|||
|
|
" \n",
|
|||
|
|
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
|||
|
|
" width=\"24px\">\n",
|
|||
|
|
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
|
|||
|
|
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
|
|||
|
|
" </svg>\n",
|
|||
|
|
" </button>\n",
|
|||
|
|
" \n",
|
|||
|
|
" <style>\n",
|
|||
|
|
" .colab-df-container {\n",
|
|||
|
|
" display:flex;\n",
|
|||
|
|
" flex-wrap:wrap;\n",
|
|||
|
|
" gap: 12px;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .colab-df-convert {\n",
|
|||
|
|
" background-color: #E8F0FE;\n",
|
|||
|
|
" border: none;\n",
|
|||
|
|
" border-radius: 50%;\n",
|
|||
|
|
" cursor: pointer;\n",
|
|||
|
|
" display: none;\n",
|
|||
|
|
" fill: #1967D2;\n",
|
|||
|
|
" height: 32px;\n",
|
|||
|
|
" padding: 0 0 0 0;\n",
|
|||
|
|
" width: 32px;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .colab-df-convert:hover {\n",
|
|||
|
|
" background-color: #E2EBFA;\n",
|
|||
|
|
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
|||
|
|
" fill: #174EA6;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" [theme=dark] .colab-df-convert {\n",
|
|||
|
|
" background-color: #3B4455;\n",
|
|||
|
|
" fill: #D2E3FC;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" [theme=dark] .colab-df-convert:hover {\n",
|
|||
|
|
" background-color: #434B5C;\n",
|
|||
|
|
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
|||
|
|
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
|||
|
|
" fill: #FFFFFF;\n",
|
|||
|
|
" }\n",
|
|||
|
|
" </style>\n",
|
|||
|
|
"\n",
|
|||
|
|
" <script>\n",
|
|||
|
|
" const buttonEl =\n",
|
|||
|
|
" document.querySelector('#df-ca1b78cf-f532-4d4d-b437-30d6fbef7e05 button.colab-df-convert');\n",
|
|||
|
|
" buttonEl.style.display =\n",
|
|||
|
|
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
|||
|
|
"\n",
|
|||
|
|
" async function convertToInteractive(key) {\n",
|
|||
|
|
" const element = document.querySelector('#df-ca1b78cf-f532-4d4d-b437-30d6fbef7e05');\n",
|
|||
|
|
" const dataTable =\n",
|
|||
|
|
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
|||
|
|
" [key], {});\n",
|
|||
|
|
" if (!dataTable) return;\n",
|
|||
|
|
"\n",
|
|||
|
|
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
|||
|
|
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
|||
|
|
" + ' to learn more about interactive tables.';\n",
|
|||
|
|
" element.innerHTML = '';\n",
|
|||
|
|
" dataTable['output_type'] = 'display_data';\n",
|
|||
|
|
" await google.colab.output.renderOutput(dataTable, element);\n",
|
|||
|
|
" const docLink = document.createElement('div');\n",
|
|||
|
|
" docLink.innerHTML = docLinkHtml;\n",
|
|||
|
|
" element.appendChild(docLink);\n",
|
|||
|
|
" }\n",
|
|||
|
|
" </script>\n",
|
|||
|
|
" </div>\n",
|
|||
|
|
" </div>\n",
|
|||
|
|
" "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 28
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"%matplotlib inline\n",
|
|||
|
|
"\n",
|
|||
|
|
"import seaborn as sns\n",
|
|||
|
|
"\n",
|
|||
|
|
"sns.set(style='darkgrid')\n",
|
|||
|
|
"\n",
|
|||
|
|
"sns.set(font_scale=1.5)\n",
|
|||
|
|
"plt.rcParams[\"figure.figsize\"] = (12,6)\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.plot(df_stats['Training Loss'], 'b-o', label=\"Training\")\n",
|
|||
|
|
"plt.plot(df_stats['Valid. Loss'], 'g-o', label=\"Validation\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.title(\"Training & Validation Loss\")\n",
|
|||
|
|
"plt.xlabel(\"Epoch\")\n",
|
|||
|
|
"plt.ylabel(\"Loss\")\n",
|
|||
|
|
"plt.legend()\n",
|
|||
|
|
"plt.xticks([1, 2, 3, 4])\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 427
|
|||
|
|
},
|
|||
|
|
"id": "Xk3gzkeU96v3",
|
|||
|
|
"outputId": "aa447af5-09f3-4bc2-e234-8a74bba87c05"
|
|||
|
|
},
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 864x432 with 1 Axes>"
|
|||
|
|
],
|
|||
|
|
"image/png": "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
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Create test loader"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "UJlKxl0r-W-m"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"prediction_dataloader = DataLoader(\n",
|
|||
|
|
" test_dataset,\n",
|
|||
|
|
" sampler = SequentialSampler(test_dataset),\n",
|
|||
|
|
" batch_size = batch_size\n",
|
|||
|
|
" )"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "eQGsEEDh-YxG"
|
|||
|
|
},
|
|||
|
|
"execution_count": 30,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Evaluate on test dataset"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "gHSDNWvA-aq9"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"print('Predicting labels for {:,} test sentences...'.format(len(test_dataset)))\n",
|
|||
|
|
"\n",
|
|||
|
|
"model.eval()\n",
|
|||
|
|
"predictions , true_labels = [], []\n",
|
|||
|
|
"\n",
|
|||
|
|
"for batch in prediction_dataloader:\n",
|
|||
|
|
"\n",
|
|||
|
|
" b_input_ids = batch[0].to(device)\n",
|
|||
|
|
" b_input_mask = batch[1].to(device)\n",
|
|||
|
|
" y = batch[2].to(device)\n",
|
|||
|
|
" \n",
|
|||
|
|
" with torch.no_grad(): \n",
|
|||
|
|
"\n",
|
|||
|
|
" generated_ids = model.generate(\n",
|
|||
|
|
" input_ids = b_input_ids,\n",
|
|||
|
|
" attention_mask = b_input_mask, \n",
|
|||
|
|
" max_length=2, \n",
|
|||
|
|
" num_beams=2,\n",
|
|||
|
|
" repetition_penalty=2.5, \n",
|
|||
|
|
" length_penalty=1.0, \n",
|
|||
|
|
" early_stopping=True\n",
|
|||
|
|
" )\n",
|
|||
|
|
" \n",
|
|||
|
|
" preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n",
|
|||
|
|
" target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]\n",
|
|||
|
|
"\n",
|
|||
|
|
" predictions.append(preds)\n",
|
|||
|
|
" true_labels.append(target)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(' DONE.')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "OPcQkHnJ-c9A",
|
|||
|
|
"outputId": "768230ca-117a-423c-9e5d-058a63fa8838"
|
|||
|
|
},
|
|||
|
|
"execution_count": 31,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Predicting labels for 1,000 test sentences...\n",
|
|||
|
|
" DONE.\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"results_ok = 0\n",
|
|||
|
|
"results_false = 0\n",
|
|||
|
|
"for idx, true_labels_batch in enumerate(true_labels):\n",
|
|||
|
|
" for bidx, true_label in enumerate(true_labels_batch):\n",
|
|||
|
|
" if true_label == predictions[idx][bidx]:\n",
|
|||
|
|
" results_ok += 1\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" results_false += 1\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Correct predictions: {}, incorrect results: {}, accuracy: {}\".format(results_ok, results_false, float(results_ok) / (results_ok + results_false)))"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "ifz56jYW-zBN",
|
|||
|
|
"outputId": "0d8585e1-c0d7-4cca-a0d9-2f1c0f3dd1a9"
|
|||
|
|
},
|
|||
|
|
"execution_count": 32,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Correct predictions: 431, incorrect results: 569, accuracy: 0.431\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"print(\"Sample prediction: {}, expected: {}\".format(predictions[2][0], true_labels[2][0]))"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "1LqVo4wW-2g-",
|
|||
|
|
"outputId": "f777b5ba-8c10-466b-c9be-d61382478d77"
|
|||
|
|
},
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Sample prediction: I, expected: true\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# MCC Score"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "dLYc9WXz_B1o"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.metrics import matthews_corrcoef\n",
|
|||
|
|
"\n",
|
|||
|
|
"matthews_set = []\n",
|
|||
|
|
"print('Calculating Matthews Corr. Coef. for each batch...')\n",
|
|||
|
|
"\n",
|
|||
|
|
"for i in range(len(true_labels)):\n",
|
|||
|
|
" matthews = matthews_corrcoef(true_labels[i], predictions[i]) \n",
|
|||
|
|
" matthews_set.append(matthews)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "hPEPpXXX_DXR",
|
|||
|
|
"outputId": "215f4970-5e0d-4477-cf5b-95ec1b27317f"
|
|||
|
|
},
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Calculating Matthews Corr. Coef. for each batch...\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"ax = sns.barplot(x=list(range(len(matthews_set))), y=matthews_set, ci=None)\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.title('MCC Score per Batch')\n",
|
|||
|
|
"plt.ylabel('MCC Score (-1 to +1)')\n",
|
|||
|
|
"plt.xlabel('Batch #')\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 427
|
|||
|
|
},
|
|||
|
|
"id": "qjtAYcme_EyM",
|
|||
|
|
"outputId": "00d36441-d6fb-4398-dad1-dfed14b8c7e6"
|
|||
|
|
},
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 864x432 with 1 Axes>"
|
|||
|
|
],
|
|||
|
|
"image/png": "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
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"flat_predictions = np.concatenate(predictions, axis=0)\n",
|
|||
|
|
"flat_true_labels = np.concatenate(true_labels, axis=0)\n",
|
|||
|
|
"\n",
|
|||
|
|
"mcc = matthews_corrcoef(flat_true_labels, flat_predictions)\n",
|
|||
|
|
"print('Total MCC: %.3f' % mcc)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "rkonN244_HPz",
|
|||
|
|
"outputId": "cbf3d43c-f453-4146-ee38-58e45475aeb2"
|
|||
|
|
},
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Total MCC: -0.033\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": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "avafCMoS_KDF",
|
|||
|
|
"outputId": "c1148369-1c8e-4448-de94-af6ea058c171"
|
|||
|
|
},
|
|||
|
|
"execution_count": 36,
|
|||
|
|
"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": 36
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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-"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|