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5972 lines
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"904d0e8e3a2b443f9e90652d78ecff95": {
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"model_module": "@jupyter-widgets/controls",
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{
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"source": [
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"# Instalacja pakietów"
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],
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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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"Installing collected packages: tokenizers, sentencepiece, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets\n",
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" Attempting uninstall: urllib3\n",
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" Found existing installation: urllib3 1.24.3\n",
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" Uninstalling 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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|||
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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": {
|
|||
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|
"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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"99a8b62c0eb9498a801d33b890ff7bed",
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"c359659559d34001a3c5efe76f537f02",
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"6f0e3b497e0841d685447d55249c7090",
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"0706cf17204148fd984f48cd67918fc1",
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"eef03577efaa4c059c19ef479dbfd01d",
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"91ad4f75ac8d45e1b351ca8ae14b6622",
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"ab3de39e3f5c4b7ca46d8951ae66dc2b",
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"23aefac177cb4da1a9185f9cec04389c",
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"0c7bf18286b4482d88e1fa60d592a41d",
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"0b9435ced6e64d5b9a8817c10800df73",
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"d93e145562a049b5b78939ddc21deca7",
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"a6ee0f4a0dfd4db892b7507b613413a2",
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"b474c2802a5e47b4a64e39caf3d27b49",
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"ef716218f5584978921c4e60778be480",
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"a007cf844ba149e79da42ebb2a8b6919",
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"48d02bab11db43b38d4210896e55499f",
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"3c9d0bf719e74eb28af657c968b2c20d",
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"f8b75f9fd51a4f199c885fbcaaa34acb",
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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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"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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"cell_type": "code",
|
|||
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|
"source": [
|
|||
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|
"dataset['train'][0]"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
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|
},
|
|||
|
|
"id": "JKFHPko3OnAV",
|
|||
|
|
"outputId": "a94fa7c1-ab93-4473-d06a-61a8c50d8783"
|
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},
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"execution_count": 4,
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"outputs": [
|
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{
|
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"output_type": "execute_result",
|
|||
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|
"data": {
|
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"text/plain": [
|
|||
|
|
"{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n',\n",
|
|||
|
|
" 'label': 0}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
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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"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "l140vJrgYxPr"
|
|||
|
|
}
|
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|
|
},
|
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|
|
{
|
|||
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|
"cell_type": "code",
|
|||
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|
"source": [
|
|||
|
|
"parsed_dataset = []\n",
|
|||
|
|
"\n",
|
|||
|
|
"for row in dataset['train']:\n",
|
|||
|
|
" text = row['sms']\n",
|
|||
|
|
" new_row = {}\n",
|
|||
|
|
" new_row['sms'] = text\n",
|
|||
|
|
" if row['label'] == 0:\n",
|
|||
|
|
" new_row['label'] = \"conversation\"\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" new_row['label'] = \"advertising\"\n",
|
|||
|
|
" parsed_dataset.append(new_row)\n",
|
|||
|
|
"\n",
|
|||
|
|
"parsed_dataset[0]"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "1boUF-YiY3_y",
|
|||
|
|
"outputId": "15412aef-de85-43ce-ad3b-f88283a242a0"
|
|||
|
|
},
|
|||
|
|
"execution_count": 5,
|
|||
|
|
"outputs": [
|
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|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n',\n",
|
|||
|
|
" 'label': 'conversation'}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 5
|
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|
}
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]
|
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},
|
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|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Tokenizer T5"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "O-J-jBDxPJcn"
|
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|
}
|
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|
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|
{
|
|||
|
|
"cell_type": "code",
|
|||
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"source": [
|
|||
|
|
"from transformers import T5Tokenizer"
|
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|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "P23AYPX1PZ6g"
|
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|
},
|
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|
"execution_count": 6,
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"outputs": []
|
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|
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},
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|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"tokenizer = T5Tokenizer.from_pretrained('t5-base')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
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|
|
"height": 185,
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"referenced_widgets": [
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"54ce84849a974330a66e1100086a8fed",
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"4efdba3661464381a034ba90eed898dc",
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"d6e70f45ef8f426e8a9f1646e4bfdb2f",
|
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"a6b434b5186341bbb2e3a6b5772f307f",
|
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"eb2079320deb4ef5aa5624e164689b4e",
|
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"88f741767e6845dda901e2f17fd978e1",
|
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|
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"d8bd3441499c405cbea7b6b61f7636ad",
|
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"af6d44105eb8435cbc34fa902e4303b9",
|
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"e5dc73086a7d48a5a75274ecf7e1e83e",
|
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"7bad80b954c74f8995f9c572a2831b6f",
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"6358c42c34a74dfc8df0101076eb2274",
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"3806842991b04a19a315223c4f0d05b0",
|
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"0f9e34991e634982a37ed4474939a614",
|
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"6fd3d1538419439db78ae1940e6ecd95",
|
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"968e624fbe4b49d8b22ba35237f29f04",
|
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|
"490d54e698024024a0376e0c5aa57afa",
|
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|
"9c921a01f9f44ef6b6b0867fbce29d4e",
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"d932c14e5642431da3490fda711b855a",
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"e66eea96fb8d467085ff05b14cef41f5",
|
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"8ff90b6f1b204ceaa41c09c48bdb4a95",
|
|||
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"1c3c1fd4ebbf4363aa9c7b7df4fb96a7",
|
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"7febee22767b48ed8ffb31b2e86e2bde"
|
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|
]
|
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|
},
|
|||
|
|
"id": "q5Jz0E_oPMBr",
|
|||
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|
"outputId": "57650e4c-558f-46aa-e08f-0362ab53e2e8"
|
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|
},
|
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|
"execution_count": 7,
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|
"outputs": [
|
|||
|
|
{
|
|||
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|
"output_type": "display_data",
|
|||
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|
"data": {
|
|||
|
|
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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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|
|
"output_type": "display_data",
|
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|
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|
}
|
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|
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|
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"metadata": {}
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{
|
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|
"output_type": "stream",
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|
"name": "stderr",
|
|||
|
|
"text": [
|
|||
|
|
"/usr/local/lib/python3.8/dist-packages/transformers/models/t5/tokenization_t5.py:163: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
|
|||
|
|
"For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
|
|||
|
|
"- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
|
|||
|
|
"- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
|
|||
|
|
"- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"sms = parsed_dataset[0]['sms']\n",
|
|||
|
|
"print('Original: ', sms)\n",
|
|||
|
|
"print('Tokenized: ', tokenizer.tokenize(sms))\n",
|
|||
|
|
"print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sms)))"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "dfxJQpoePsvI",
|
|||
|
|
"outputId": "713d0aed-1350-44f9-e59d-e4a2f8b7a0b3"
|
|||
|
|
},
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Original: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
|
|||
|
|
"\n",
|
|||
|
|
"Tokenized: ['▁Go', '▁until', '▁jur', 'ong', '▁point', ',', '▁crazy', '.', '.', '▁Available', '▁only', '▁in', '▁bug', 'is', '▁', 'n', '▁great', '▁world', '▁la', '▁', 'e', '▁buffet', '...', '▁Cine', '▁there', '▁got', '▁', 'a', 'more', '▁wa', 't', '...']\n",
|
|||
|
|
"Token IDs: [1263, 552, 10081, 2444, 500, 6, 6139, 5, 5, 8144, 163, 16, 8143, 159, 3, 29, 248, 296, 50, 3, 15, 15385, 233, 17270, 132, 530, 3, 9, 3706, 8036, 17, 233]\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# 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": "6a83d1cb-629d-4725-e3a2-5c60f9962108"
|
|||
|
|
},
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Max sentence length: 338\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": "38498917-ba97-472f-9012-5da83babab62"
|
|||
|
|
},
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"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 = 340,\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": "9b98f987-7176-48cc-d298-f09f69c6eab7"
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Original: {'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n', 'label': 'conversation'}\n",
|
|||
|
|
"Token IDs: tensor([ 1263, 552, 10081, 2444, 500, 6, 6139, 5, 5, 8144,\n",
|
|||
|
|
" 163, 16, 8143, 159, 3, 29, 248, 296, 50, 3,\n",
|
|||
|
|
" 15, 15385, 233, 17270, 132, 530, 3, 9, 3706, 8036,\n",
|
|||
|
|
" 17, 233, 1, 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([3634, 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": 13,
|
|||
|
|
"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": "cfb15fb6-1daa-4b3c-df1b-5d0c862e8821"
|
|||
|
|
},
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"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": 15,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"batch_size = 16\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": 16,
|
|||
|
|
"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": "8db82471-22b2-450d-cb9d-ba86ce765fa2"
|
|||
|
|
},
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"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 T5 model"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "okTx_ynMV0rH"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from transformers import T5ForConditionalGeneration"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Eu-7Eed8WgN0"
|
|||
|
|
},
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"model = T5ForConditionalGeneration.from_pretrained('t5-base')\n",
|
|||
|
|
"\n",
|
|||
|
|
"model.cuda()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 1000,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"c4238a30a23f4a4995a64596a076f639",
|
|||
|
|
"7008395271204caba9d2a70e886f3fb7",
|
|||
|
|
"91fe75c3703e49e2b9200eb32465979a",
|
|||
|
|
"605fe093ee154e1eafda5f92c07712ef",
|
|||
|
|
"63343a6f0a2a4f66b878afc18d408c1c",
|
|||
|
|
"fc24763fbd70457fbb2a67883ec38a67",
|
|||
|
|
"7ba8e63a4e1645ebad9829af1706fb1d",
|
|||
|
|
"1aeec94f6aed4d6fa9ef94b5e8011f95",
|
|||
|
|
"7ab80f2dc4bd4bc4b4d28bff719068e2",
|
|||
|
|
"ab5aa9e15b894bf6aec2ed8d7949d4fb",
|
|||
|
|
"5aff4f72159e4187adf00959d8863017",
|
|||
|
|
"52e83e2e748f4c79b789d0354f4e941b",
|
|||
|
|
"ef0f5971cb444d8f8c5c1ace4d8ebe81",
|
|||
|
|
"64885f8157264a1297bab372bf8a7fb5",
|
|||
|
|
"6b3444ddd6d24f448631e3e86d992241",
|
|||
|
|
"7e0aaf3e4b5b4bbc9c8120d9e5c00d8d",
|
|||
|
|
"51c2d890c6874141a0d5ecc0eb89a282",
|
|||
|
|
"1ec01f40f98f47e3aae79a9f871d9df1",
|
|||
|
|
"f208555568824dd9a800555cc17182be",
|
|||
|
|
"5fd3c6d56ef644ebaff4da4bffa470a5",
|
|||
|
|
"5167e08d346f47468249d2f40b262792",
|
|||
|
|
"904d0e8e3a2b443f9e90652d78ecff95"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "JKv9O8kfV2zZ",
|
|||
|
|
"outputId": "0d41faa2-6857-4a67-d581-41383ffc0378"
|
|||
|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Downloading (…)\"pytorch_model.bin\";: 0%| | 0.00/892M [00:00<?, ?B/s]"
|
|||
|
|
],
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"model_id": "c4238a30a23f4a4995a64596a076f639"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"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": "52e83e2e748f4c79b789d0354f4e941b"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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): T5DenseActDense(\n",
|
|||
|
|
" (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
|
|||
|
|
" (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (act): ReLU()\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": 19
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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": 20,
|
|||
|
|
"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": 21,
|
|||
|
|
"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": 22,
|
|||
|
|
"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": 23,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Training"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "DAzQWODja0A3"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"import random\n",
|
|||
|
|
"import time"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Hoa7NlU0bI7G"
|
|||
|
|
},
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"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",
|
|||
|
|
" total_train_loss = 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",
|
|||
|
|
" 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",
|
|||
|
|
" 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",
|
|||
|
|
" training_time = format_time(time.time() - t0)\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(\"\")\n",
|
|||
|
|
" print(\" Average training loss: {0:.2f}\".format(avg_train_loss))\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",
|
|||
|
|
" 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",
|
|||
|
|
" '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": "c1d90548-6d70-4172-e0e2-e916eea141a6"
|
|||
|
|
},
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 1 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 258. Elapsed: 0:00:46.\n",
|
|||
|
|
" Batch 80 of 258. Elapsed: 0:01:32.\n",
|
|||
|
|
" Batch 120 of 258. Elapsed: 0:02:17.\n",
|
|||
|
|
" Batch 160 of 258. Elapsed: 0:03:02.\n",
|
|||
|
|
" Batch 200 of 258. Elapsed: 0:03:47.\n",
|
|||
|
|
" Batch 240 of 258. Elapsed: 0:04:33.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.02\n",
|
|||
|
|
" Training epcoh took: 0:04:52\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.00\n",
|
|||
|
|
" Validation took: 0:00:24\n",
|
|||
|
|
" Validation Loss: 0.00\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 2 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 258. Elapsed: 0:00:45.\n",
|
|||
|
|
" Batch 80 of 258. Elapsed: 0:01:31.\n",
|
|||
|
|
" Batch 120 of 258. Elapsed: 0:02:16.\n",
|
|||
|
|
" Batch 160 of 258. Elapsed: 0:03:01.\n",
|
|||
|
|
" Batch 200 of 258. Elapsed: 0:03:46.\n",
|
|||
|
|
" Batch 240 of 258. Elapsed: 0:04:32.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.00\n",
|
|||
|
|
" Training epcoh took: 0:04:52\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.00\n",
|
|||
|
|
" Validation took: 0:00:24\n",
|
|||
|
|
" Validation Loss: 0.00\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 3 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 258. Elapsed: 0:00:45.\n",
|
|||
|
|
" Batch 80 of 258. Elapsed: 0:01:30.\n",
|
|||
|
|
" Batch 120 of 258. Elapsed: 0:02:15.\n",
|
|||
|
|
" Batch 160 of 258. Elapsed: 0:03:01.\n",
|
|||
|
|
" Batch 200 of 258. Elapsed: 0:03:46.\n",
|
|||
|
|
" Batch 240 of 258. Elapsed: 0:04:31.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.00\n",
|
|||
|
|
" Training epcoh took: 0:04:51\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.00\n",
|
|||
|
|
" Validation took: 0:00:24\n",
|
|||
|
|
" Validation Loss: 0.00\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 4 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 258. Elapsed: 0:00:45.\n",
|
|||
|
|
" Batch 80 of 258. Elapsed: 0:01:30.\n",
|
|||
|
|
" Batch 120 of 258. Elapsed: 0:02:16.\n",
|
|||
|
|
" Batch 160 of 258. Elapsed: 0:03:01.\n",
|
|||
|
|
" Batch 200 of 258. Elapsed: 0:03:46.\n",
|
|||
|
|
" Batch 240 of 258. Elapsed: 0:04:31.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.00\n",
|
|||
|
|
" Training epcoh took: 0:04:51\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.00\n",
|
|||
|
|
" Validation took: 0:00:24\n",
|
|||
|
|
" Validation Loss: 0.00\n",
|
|||
|
|
"\n",
|
|||
|
|
"Training complete!\n",
|
|||
|
|
"Total training took 0:21:01 (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": {
|
|||
|
|
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|
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|
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},
|
|||
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"execution_count": 26,
|
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"outputs": [
|
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{
|
|||
|
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"output_type": "execute_result",
|
|||
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"data": {
|
|||
|
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"text/plain": [
|
|||
|
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" Training Loss Valid. Loss Valid. Accur. Training Time Validation Time\n",
|
|||
|
|
"epoch \n",
|
|||
|
|
"1 1.84e-02 0.0 0.0 0:04:52 0:00:24\n",
|
|||
|
|
"2 1.49e-06 0.0 0.0 0:04:52 0:00:24\n",
|
|||
|
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"3 4.64e-07 0.0 0.0 0:04:51 0:00:24\n",
|
|||
|
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"4 1.43e-07 0.0 0.0 0:04:51 0:00:24"
|
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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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" <thead>\n",
|
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|
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" <tr style=\"text-align: right;\">\n",
|
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|
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" <th></th>\n",
|
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" <th>Training Loss</th>\n",
|
|||
|
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" <th>Valid. Loss</th>\n",
|
|||
|
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" <th>Valid. Accur.</th>\n",
|
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" <th>Training Time</th>\n",
|
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" <th>Validation Time</th>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>epoch</th>\n",
|
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" <th></th>\n",
|
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" <th></th>\n",
|
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" <th></th>\n",
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" <tbody>\n",
|
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" <tr>\n",
|
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" <th>1</th>\n",
|
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|
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" <td>1.84e-02</td>\n",
|
|||
|
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" <td>0.0</td>\n",
|
|||
|
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" <td>0.0</td>\n",
|
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" <td>0:04:52</td>\n",
|
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" <td>0:00:24</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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|
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" <th>2</th>\n",
|
|||
|
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" <td>1.49e-06</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
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|
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" <td>0:04:52</td>\n",
|
|||
|
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" <td>0:00:24</td>\n",
|
|||
|
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" </tr>\n",
|
|||
|
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" <tr>\n",
|
|||
|
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" <th>3</th>\n",
|
|||
|
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" <td>4.64e-07</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
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" <td>0:04:51</td>\n",
|
|||
|
|
" <td>0:00:24</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>1.43e-07</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0:04:51</td>\n",
|
|||
|
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" <td>0:00:24</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
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" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>\n",
|
|||
|
|
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29c2e0ae-3218-40c3-96ae-d94c05faa5b4')\"\n",
|
|||
|
|
" title=\"Convert this dataframe to an interactive table.\"\n",
|
|||
|
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" style=\"display:none;\">\n",
|
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|
|
" \n",
|
|||
|
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" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
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" width=\"24px\">\n",
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" </svg>\n",
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" </button>\n",
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" \n",
|
|||
|
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" <style>\n",
|
|||
|
|
" .colab-df-container {\n",
|
|||
|
|
" display:flex;\n",
|
|||
|
|
" flex-wrap:wrap;\n",
|
|||
|
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" gap: 12px;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .colab-df-convert {\n",
|
|||
|
|
" background-color: #E8F0FE;\n",
|
|||
|
|
" border: none;\n",
|
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|
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" border-radius: 50%;\n",
|
|||
|
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" cursor: pointer;\n",
|
|||
|
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" display: none;\n",
|
|||
|
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" fill: #1967D2;\n",
|
|||
|
|
" height: 32px;\n",
|
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|
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" padding: 0 0 0 0;\n",
|
|||
|
|
" width: 32px;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .colab-df-convert:hover {\n",
|
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|
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" background-color: #E2EBFA;\n",
|
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|
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
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|
|
" 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",
|
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|
|
" fill: #FFFFFF;\n",
|
|||
|
|
" }\n",
|
|||
|
|
" </style>\n",
|
|||
|
|
"\n",
|
|||
|
|
" <script>\n",
|
|||
|
|
" const buttonEl =\n",
|
|||
|
|
" document.querySelector('#df-29c2e0ae-3218-40c3-96ae-d94c05faa5b4 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-29c2e0ae-3218-40c3-96ae-d94c05faa5b4');\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": 26
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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": "fce44153-ea42-4563-e704-d44c8693422d"
|
|||
|
|
},
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"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": 28,
|
|||
|
|
"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": "9e25d954-7dd6-416a-f350-a06b7a6f6453"
|
|||
|
|
},
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"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": "0fd7d84a-7f00-4c0a-f125-e4e3f94ac230"
|
|||
|
|
},
|
|||
|
|
"execution_count": 30,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Correct predictions: 0, incorrect results: 1000, accuracy: 0.0\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": "0ac57805-e5d3-473d-a679-da032a7016f4"
|
|||
|
|
},
|
|||
|
|
"execution_count": 31,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Sample prediction: how, expected: conversation\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": "f44695cb-0c76-4373-c8c2-a7c6ce375496"
|
|||
|
|
},
|
|||
|
|
"execution_count": 32,
|
|||
|
|
"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": "4f105d74-f50e-4b33-ba55-35c2411cbdef"
|
|||
|
|
},
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"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": "41d38d16-d647-45b5-8afc-ea2e29ff82ec"
|
|||
|
|
},
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Total MCC: 0.000\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/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": "46e0d66e-ba84-485e-8188-beaee2a89d9e"
|
|||
|
|
},
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Mounted at /content/gdrive/\n",
|
|||
|
|
"Saving model to /content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/T5_Model\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"('/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/T5_Model/tokenizer_config.json',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/T5_Model/special_tokens_map.json',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/T5_Model/spiece.model',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/T5_Model/added_tokens.json')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 35
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Bibliografia\n",
|
|||
|
|
"- https://github.com/Shivanandroy/T5-Finetuning-PyTorch/blob/main/notebook/T5_Fine_tuning_with_PyTorch.ipynb\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-"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|