4673 lines
167 KiB
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4673 lines
167 KiB
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|||
|
|
"min_width": null,
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|
"object_fit": null,
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|||
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|
"object_position": null,
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|||
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|
"order": null,
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|||
|
|
"overflow": null,
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|
"overflow_x": null,
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|||
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|
"overflow_y": null,
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|||
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|
"padding": null,
|
|||
|
|
"right": null,
|
|||
|
|
"top": null,
|
|||
|
|
"visibility": null,
|
|||
|
|
"width": null
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"6ce98299d14e4871b39670dcc0a54e51": {
|
|||
|
|
"model_module": "@jupyter-widgets/controls",
|
|||
|
|
"model_name": "DescriptionStyleModel",
|
|||
|
|
"model_module_version": "1.5.0",
|
|||
|
|
"state": {
|
|||
|
|
"_model_module": "@jupyter-widgets/controls",
|
|||
|
|
"_model_module_version": "1.5.0",
|
|||
|
|
"_model_name": "DescriptionStyleModel",
|
|||
|
|
"_view_count": null,
|
|||
|
|
"_view_module": "@jupyter-widgets/base",
|
|||
|
|
"_view_module_version": "1.2.0",
|
|||
|
|
"_view_name": "StyleView",
|
|||
|
|
"description_width": ""
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"cells": [
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Setup"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "tZbwIaoXO3di"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from google.colab import drive\n",
|
|||
|
|
"drive.mount('/content/drive')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "n3ScGYli1QGA",
|
|||
|
|
"outputId": "f87405c6-21e2-4e44-9a14-452ba643cfe7"
|
|||
|
|
},
|
|||
|
|
"execution_count": 1,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Mounted at /content/drive\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"!pip install -Uq transformers bitsandbytes accelerate datasets==2.0.0"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "1qS0p25llt37",
|
|||
|
|
"outputId": "4f94a7c3-2efa-4686-bfef-e552811ceea4"
|
|||
|
|
},
|
|||
|
|
"execution_count": 2,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m109.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m97.1/97.1 MB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.6/227.6 kB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m325.5/325.5 kB\u001b[0m \u001b[31m36.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
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|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m25.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m58.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m28.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m33.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m81.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m32.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[?25h"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Create dataset"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "rDDe5DJCy2r_"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from datasets import load_dataset\n",
|
|||
|
|
"\n",
|
|||
|
|
"dataset = load_dataset(\"sedthh/ubuntu_dialogue_qa\", split=\"train\")"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 185,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"2c018927b9e74dee86fc97dee970cd6c",
|
|||
|
|
"4cc2436063404812b7114ba85939633e",
|
|||
|
|
"33e483d5bcd94e61a78a1dea33cbfda4",
|
|||
|
|
"1599dbba5cc9475c8ca80309bfb243de",
|
|||
|
|
"fbd3ab6145314c6eae8331fddfe6fe32",
|
|||
|
|
"2910d1f68d544062bcc469034a614e7e",
|
|||
|
|
"31084aeaa31f4545a0584a398898328a",
|
|||
|
|
"2c681eb305ed4f45bd0529a152bc647f",
|
|||
|
|
"889252306f5c472ca0c0cd5190db37b4",
|
|||
|
|
"a0d7509c91b24135ba535b4d0351838f",
|
|||
|
|
"836922f6474c406683c162b0eb835b57",
|
|||
|
|
"3a3b01ed6b984499996cce1f0869cdcd",
|
|||
|
|
"f71e573be8c94950bd4d35d2bc660948",
|
|||
|
|
"2ae8a29617494e0c9b995fee5588d357",
|
|||
|
|
"e0f96381a7a34ea7b22c19e563628bb6",
|
|||
|
|
"9160b0fb1ee843c5a6b0ef8a46006ccd",
|
|||
|
|
"ef21e3aea84245adb068aba34bb9bdba",
|
|||
|
|
"e91b15ed58de429d94ad374633405316",
|
|||
|
|
"08630d8a72ee45f594884b5000f13f0e",
|
|||
|
|
"911d1f3c33fb4818ba34c7ad5e746a02",
|
|||
|
|
"042e8af0aa2743c79b75d76a8b11d2c8",
|
|||
|
|
"2a03064d3fce4eff8efa0034c820f3c5",
|
|||
|
|
"edbd0b09b6d24c5da65b72cd47b226f0",
|
|||
|
|
"663c5afa1c6a46d7b65d20abf371c496",
|
|||
|
|
"0468182214384b8da06742fa47de5b35",
|
|||
|
|
"0fb69d57323f4e2abb625cefb873f703",
|
|||
|
|
"af9e157beac8420d83eb9ccc6d108f8b",
|
|||
|
|
"7b08d07fe43b44109129f9a37964f40a",
|
|||
|
|
"91246b254c724bae95428e72ddcb1587",
|
|||
|
|
"b9cd1f9c883948c7a06d78ef3118a110",
|
|||
|
|
"a5c9ac799e4c42fd9fd0480225a79818",
|
|||
|
|
"43df5fac01c94a30afc67a3e8237b37f",
|
|||
|
|
"4e03b116fed841999d0eed6561a0ad3c"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "tO-4h0ZY7WcU",
|
|||
|
|
"outputId": "5858f0cb-cd46-484d-bd78-05701871f1ba"
|
|||
|
|
},
|
|||
|
|
"execution_count": 3,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stderr",
|
|||
|
|
"text": [
|
|||
|
|
"WARNING:datasets.builder:Using custom data configuration sedthh--ubuntu_dialogue_qa-8763539f847553a9\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Downloading and preparing dataset parquet/sedthh--ubuntu_dialogue_qa to /root/.cache/huggingface/datasets/parquet/sedthh--ubuntu_dialogue_qa-8763539f847553a9/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901...\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]"
|
|||
|
|
],
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"model_id": "2c018927b9e74dee86fc97dee970cd6c"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Downloading data: 0%| | 0.00/2.16M [00:00<?, ?B/s]"
|
|||
|
|
],
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"model_id": "3a3b01ed6b984499996cce1f0869cdcd"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Extracting data files: 0%| | 0/1 [00:00<?, ?it/s]"
|
|||
|
|
],
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"model_id": "edbd0b09b6d24c5da65b72cd47b226f0"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
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|||
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|
"Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/parquet/sedthh--ubuntu_dialogue_qa-8763539f847553a9/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901. Subsequent calls will reuse this data.\n"
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]
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}
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]
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{
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"cell_type": "markdown",
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"source": [
|
|||
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"# Define training components"
|
|||
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],
|
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"metadata": {
|
|||
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"id": "GeSaTKE0y4_d"
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}
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{
|
|||
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"cell_type": "code",
|
|||
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"source": [
|
|||
|
|
"from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
|
|||
|
|
"\n",
|
|||
|
|
"model_name = 'google/flan-t5-large'\n",
|
|||
|
|
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
|
|||
|
|
"model = AutoModelForSeq2SeqLM.from_pretrained(model_name, device_map='auto')"
|
|||
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],
|
|||
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"metadata": {
|
|||
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|
"id": "qt0MadQYlO_l",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 241,
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"outputId": "7d26924f-1feb-4847-f242-85e96e130e9e"
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{
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},
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|
{
|
|||
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|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"def preprocess_function(sample):\n",
|
|||
|
|
" inputs = ['Answer the question: ' + item for item in sample['INSTRUCTION']]\n",
|
|||
|
|
" model_inputs = tokenizer(inputs, padding=True, truncation=True)\n",
|
|||
|
|
" labels = tokenizer(text_target=sample['RESPONSE'], padding=True, truncation=True)\n",
|
|||
|
|
" labels['input_ids'] = [[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels['input_ids']]\n",
|
|||
|
|
" model_inputs['labels'] = labels['input_ids']\n",
|
|||
|
|
" return model_inputs\n",
|
|||
|
|
"\n",
|
|||
|
|
"tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=['INSTRUCTION', 'RESPONSE', 'SOURCE', 'METADATA'])"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "hcRqB5TaQmgJ",
|
|||
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|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
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|
"height": 86,
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|||
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|
"referenced_widgets": [
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|
"d69feb4b1c4c4bbf85398d176c50d810",
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"44e07c680c2f43c4af9de57f8949fde7",
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"446090c41120445f8991a47b1824b076",
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"740b8d34fa474009a80559453326aa3a",
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"0ec3ed20bc7d4f0a9b404fbef5f2b882",
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"c4a1ae3c96954e7e9517038a98ba5e04",
|
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"1cc61ae70c104359b52d9536f8d9bd2c",
|
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"f3e9d178f39a4ad4aeb4ce5995c7a982",
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{
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|
"output_type": "stream",
|
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|
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|
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|
|
"text": [
|
|||
|
|
"WARNING:datasets.fingerprint:Parameter 'function'=<function preprocess_function at 0x7fe0df129ea0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
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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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|
"metadata": {}
|
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|
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|
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|
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|
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|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from transformers import DataCollatorForSeq2Seq\n",
|
|||
|
|
"\n",
|
|||
|
|
"data_collator = DataCollatorForSeq2Seq(\n",
|
|||
|
|
" tokenizer,\n",
|
|||
|
|
" model=model\n",
|
|||
|
|
")"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "YyDdFqpL09aA"
|
|||
|
|
},
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Training"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "unliUG2W1BDg"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
|
|||
|
|
"\n",
|
|||
|
|
"output_dir='flan-t5_large_v2'\n",
|
|||
|
|
"\n",
|
|||
|
|
"training_args = Seq2SeqTrainingArguments(\n",
|
|||
|
|
" output_dir=output_dir,\n",
|
|||
|
|
"\t auto_find_batch_size=True,\n",
|
|||
|
|
" num_train_epochs=3,\n",
|
|||
|
|
" logging_dir=f'{output_dir}/logs',\n",
|
|||
|
|
" save_steps=25000\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"trainer = Seq2SeqTrainer(\n",
|
|||
|
|
" model=model,\n",
|
|||
|
|
" args=training_args,\n",
|
|||
|
|
" data_collator=data_collator,\n",
|
|||
|
|
" train_dataset=tokenized_dataset\n",
|
|||
|
|
")\n",
|
|||
|
|
"model.config.use_cache = False"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "m8xxtxKw1BIZ"
|
|||
|
|
},
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"trainer.train()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 1000
|
|||
|
|
},
|
|||
|
|
"id": "aQS3QFMN1DD4",
|
|||
|
|
"outputId": "02ef0e99-3ade-4fb5-aa47-ce7b63fff534"
|
|||
|
|
},
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stderr",
|
|||
|
|
"text": [
|
|||
|
|
"/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<IPython.core.display.HTML object>"
|
|||
|
|
],
|
|||
|
|
"text/html": [
|
|||
|
|
"\n",
|
|||
|
|
" <div>\n",
|
|||
|
|
" \n",
|
|||
|
|
" <progress value='2' max='6066' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
|||
|
|
" [ 2/6066 : < :, Epoch 0.00/3]\n",
|
|||
|
|
" </div>\n",
|
|||
|
|
" <table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: left;\">\n",
|
|||
|
|
" <th>Step</th>\n",
|
|||
|
|
" <th>Training Loss</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table><p>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<IPython.core.display.HTML object>"
|
|||
|
|
],
|
|||
|
|
"text/html": [
|
|||
|
|
"\n",
|
|||
|
|
" <div>\n",
|
|||
|
|
" \n",
|
|||
|
|
" <progress value='2' max='12132' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
|||
|
|
" [ 2/12132 : < :, Epoch 0.00/3]\n",
|
|||
|
|
" </div>\n",
|
|||
|
|
" <table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: left;\">\n",
|
|||
|
|
" <th>Step</th>\n",
|
|||
|
|
" <th>Training Loss</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table><p>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<IPython.core.display.HTML object>"
|
|||
|
|
],
|
|||
|
|
"text/html": [
|
|||
|
|
"\n",
|
|||
|
|
" <div>\n",
|
|||
|
|
" \n",
|
|||
|
|
" <progress value='24261' max='24261' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
|||
|
|
" [24261/24261 4:52:22, Epoch 3/3]\n",
|
|||
|
|
" </div>\n",
|
|||
|
|
" <table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: left;\">\n",
|
|||
|
|
" <th>Step</th>\n",
|
|||
|
|
" <th>Training Loss</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>500</td>\n",
|
|||
|
|
" <td>3.320700</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>1000</td>\n",
|
|||
|
|
" <td>3.173000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>1500</td>\n",
|
|||
|
|
" <td>3.142500</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>2000</td>\n",
|
|||
|
|
" <td>3.070900</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>2500</td>\n",
|
|||
|
|
" <td>3.083600</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>3000</td>\n",
|
|||
|
|
" <td>3.057900</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>3500</td>\n",
|
|||
|
|
" <td>3.005900</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>4000</td>\n",
|
|||
|
|
" <td>3.016900</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>4500</td>\n",
|
|||
|
|
" <td>3.012300</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>5000</td>\n",
|
|||
|
|
" <td>2.957100</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>5500</td>\n",
|
|||
|
|
" <td>2.958700</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>6000</td>\n",
|
|||
|
|
" <td>2.976900</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>6500</td>\n",
|
|||
|
|
" <td>2.959700</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>7000</td>\n",
|
|||
|
|
" <td>2.975100</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>7500</td>\n",
|
|||
|
|
" <td>2.951100</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>8000</td>\n",
|
|||
|
|
" <td>2.945200</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>8500</td>\n",
|
|||
|
|
" <td>2.849600</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>9000</td>\n",
|
|||
|
|
" <td>2.722500</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>9500</td>\n",
|
|||
|
|
" <td>2.854400</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>10000</td>\n",
|
|||
|
|
" <td>2.782700</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>10500</td>\n",
|
|||
|
|
" <td>2.845900</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>11000</td>\n",
|
|||
|
|
" <td>2.803300</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>11500</td>\n",
|
|||
|
|
" <td>2.797800</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>12000</td>\n",
|
|||
|
|
" <td>2.827400</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>12500</td>\n",
|
|||
|
|
" <td>2.757800</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>13000</td>\n",
|
|||
|
|
" <td>2.823600</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>13500</td>\n",
|
|||
|
|
" <td>2.827200</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>14000</td>\n",
|
|||
|
|
" <td>2.832400</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>14500</td>\n",
|
|||
|
|
" <td>2.839800</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>15000</td>\n",
|
|||
|
|
" <td>2.783400</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>15500</td>\n",
|
|||
|
|
" <td>2.774200</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>16000</td>\n",
|
|||
|
|
" <td>2.785500</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>16500</td>\n",
|
|||
|
|
" <td>2.793600</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>17000</td>\n",
|
|||
|
|
" <td>2.801200</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>17500</td>\n",
|
|||
|
|
" <td>2.812800</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>18000</td>\n",
|
|||
|
|
" <td>2.813300</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>18500</td>\n",
|
|||
|
|
" <td>2.804800</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>19000</td>\n",
|
|||
|
|
" <td>2.777800</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>19500</td>\n",
|
|||
|
|
" <td>2.761100</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>20000</td>\n",
|
|||
|
|
" <td>2.832700</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>20500</td>\n",
|
|||
|
|
" <td>2.868300</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>21000</td>\n",
|
|||
|
|
" <td>2.757300</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>21500</td>\n",
|
|||
|
|
" <td>2.763300</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>22000</td>\n",
|
|||
|
|
" <td>2.817900</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>22500</td>\n",
|
|||
|
|
" <td>2.775300</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>23000</td>\n",
|
|||
|
|
" <td>2.796400</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>23500</td>\n",
|
|||
|
|
" <td>2.798700</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>24000</td>\n",
|
|||
|
|
" <td>2.778000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table><p>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"TrainOutput(global_step=24261, training_loss=2.8796593968087287, metrics={'train_runtime': 17543.4268, 'train_samples_per_second': 2.766, 'train_steps_per_second': 1.383, 'total_flos': 3.65470024955904e+16, 'train_loss': 2.8796593968087287, 'epoch': 3.0})"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 8
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Save the model"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "mDOXSmts1J-G"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"trainer.model.save_pretrained('se_flan-t5_v2')\n",
|
|||
|
|
"tokenizer.save_pretrained('se_flan-t5_v2')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "RN9rhtHw1KC9",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"outputId": "7f939af3-3fc2-406d-cf5c-d1cb558ba61d"
|
|||
|
|
},
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"('se_flan-t5_v2/tokenizer_config.json',\n",
|
|||
|
|
" 'se_flan-t5_v2/special_tokens_map.json',\n",
|
|||
|
|
" 'se_flan-t5_v2/tokenizer.json')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 9
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"!rm -r /content/drive/MyDrive/se; mkdir /content/drive/MyDrive/se\n",
|
|||
|
|
"!cp -r se_flan-t5_v2 /content/drive/MyDrive/se"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "FPUZs3SZPQ4z",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"outputId": "3dd3dd4f-65df-413c-ba5f-ec64a8244b83"
|
|||
|
|
},
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"rm: cannot remove '/content/drive/MyDrive/se': No such file or directory\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|