mirror of
https://github.com/wassname/peft.git
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487 lines
16 KiB
Plaintext
487 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "5f93b7d1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"===================================BUG REPORT===================================\n",
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"Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
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"For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
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"================================================================================\n",
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"CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n",
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"CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
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"CUDA SETUP: Detected CUDA version 117\n",
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"CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
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]
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}
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],
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"source": [
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"from transformers import AutoModelForSeq2SeqLM\n",
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"from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, LoraConfig, TaskType\n",
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"import torch\n",
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"from datasets import load_dataset\n",
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"import os\n",
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"\n",
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"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
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"from transformers import AutoTokenizer\n",
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"from torch.utils.data import DataLoader\n",
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"from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
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"from tqdm import tqdm\n",
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"from datasets import load_dataset\n",
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"\n",
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"device = \"cuda\"\n",
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"model_name_or_path = \"bigscience/mt0-large\"\n",
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"tokenizer_name_or_path = \"bigscience/mt0-large\"\n",
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"\n",
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"checkpoint_name = \"financial_sentiment_analysis_lora_v1.pt\"\n",
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"text_column = \"sentence\"\n",
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"label_column = \"text_label\"\n",
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"max_length = 128\n",
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"lr = 1e-3\n",
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"num_epochs = 3\n",
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"batch_size = 8"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8d0850ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"# creating model\n",
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"peft_config = LoraConfig(task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1)\n",
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"\n",
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"model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n",
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"model = get_peft_model(model, peft_config)\n",
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"model.print_trainable_parameters()\n",
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"model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "4ee2babf",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Found cached dataset financial_phrasebank (/home/sourab/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141)\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "3403bf3d718042018b0531848cc30209",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/1 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d3d5c45e3776469f9560b6eaa9346f8f",
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"version_major": 2,
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"version_minor": 0
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},
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "e9736f26e9aa450b8d65f95c0b9c81cc",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/1 [00:00<?, ?ba/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"{'sentence': \"The 10,000-odd square metre plot that Stockmann has bought for the Nevsky Center shopping center is located on Nevsky Prospect , St Petersburg 's high street , next to the Vosstaniya Square underground station , in the immediate vicinity of Moscow Station .\",\n",
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" 'label': 1,\n",
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" 'text_label': 'neutral'}"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# loading dataset\n",
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"dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\")\n",
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"dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
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"dataset[\"validation\"] = dataset[\"test\"]\n",
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"del dataset[\"test\"]\n",
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"\n",
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"classes = dataset[\"train\"].features[\"label\"].names\n",
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"dataset = dataset.map(\n",
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" lambda x: {\"text_label\": [classes[label] for label in x[\"label\"]]},\n",
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" batched=True,\n",
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" num_proc=1,\n",
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")\n",
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"\n",
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"dataset[\"train\"][0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "adf9608c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "c460989d4ab24e3f97d81ef040b1d1b4",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Running tokenizer on dataset: 0%| | 0/3 [00:00<?, ?ba/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1acc389b08b94f8a87900b9fbdbccce4",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Running tokenizer on dataset: 0%| | 0/1 [00:00<?, ?ba/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# data preprocessing\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
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"\n",
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"\n",
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"def preprocess_function(examples):\n",
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" inputs = examples[text_column]\n",
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" targets = examples[label_column]\n",
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" model_inputs = tokenizer(inputs, max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
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" labels = tokenizer(targets, max_length=3, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
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" labels = labels[\"input_ids\"]\n",
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" labels[labels == tokenizer.pad_token_id] = -100\n",
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" model_inputs[\"labels\"] = labels\n",
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" return model_inputs\n",
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"\n",
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"\n",
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"processed_datasets = dataset.map(\n",
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" preprocess_function,\n",
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" batched=True,\n",
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" num_proc=1,\n",
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" remove_columns=dataset[\"train\"].column_names,\n",
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" load_from_cache_file=False,\n",
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" desc=\"Running tokenizer on dataset\",\n",
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")\n",
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"\n",
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"train_dataset = processed_datasets[\"train\"]\n",
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"eval_dataset = processed_datasets[\"validation\"]\n",
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"\n",
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"train_dataloader = DataLoader(\n",
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" train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
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")\n",
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"eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "f733a3c6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# optimizer and lr scheduler\n",
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"optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
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"lr_scheduler = get_linear_schedule_with_warmup(\n",
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" optimizer=optimizer,\n",
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" num_warmup_steps=0,\n",
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" num_training_steps=(len(train_dataloader) * num_epochs),\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "6b3a4090",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|████████████████████████████████████████████████████████████████████████████████████████| 255/255 [02:21<00:00, 1.81it/s]\n",
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"100%|██████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:07<00:00, 4.13it/s]\n"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch=0: train_ppl=tensor(14.6341, device='cuda:0') train_epoch_loss=tensor(2.6834, device='cuda:0') eval_ppl=tensor(1.0057, device='cuda:0') eval_epoch_loss=tensor(0.0057, device='cuda:0')\n"
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]
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{
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"name": "stderr",
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"output_type": "stream",
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch=1: train_ppl=tensor(1.7576, device='cuda:0') train_epoch_loss=tensor(0.5640, device='cuda:0') eval_ppl=tensor(1.0052, device='cuda:0') eval_epoch_loss=tensor(0.0052, device='cuda:0')\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|████████████████████████████████████████████████████████████████████████████████████████| 255/255 [01:33<00:00, 2.74it/s]\n",
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch=2: train_ppl=tensor(1.3830, device='cuda:0') train_epoch_loss=tensor(0.3243, device='cuda:0') eval_ppl=tensor(1.0035, device='cuda:0') eval_epoch_loss=tensor(0.0035, device='cuda:0')\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"# training and evaluation\n",
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"model = model.to(device)\n",
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"\n",
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"for epoch in range(num_epochs):\n",
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" model.train()\n",
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" total_loss = 0\n",
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" for step, batch in enumerate(tqdm(train_dataloader)):\n",
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" batch = {k: v.to(device) for k, v in batch.items()}\n",
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" outputs = model(**batch)\n",
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" loss = outputs.loss\n",
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" total_loss += loss.detach().float()\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" lr_scheduler.step()\n",
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" optimizer.zero_grad()\n",
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"\n",
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" model.eval()\n",
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" eval_loss = 0\n",
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" eval_preds = []\n",
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" for step, batch in enumerate(tqdm(eval_dataloader)):\n",
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" batch = {k: v.to(device) for k, v in batch.items()}\n",
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" with torch.no_grad():\n",
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" outputs = model(**batch)\n",
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" loss = outputs.loss\n",
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" eval_loss += loss.detach().float()\n",
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" eval_preds.extend(\n",
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" tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n",
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" )\n",
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"\n",
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" eval_epoch_loss = eval_loss / len(eval_dataloader)\n",
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" eval_ppl = torch.exp(eval_epoch_loss)\n",
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" train_epoch_loss = total_loss / len(train_dataloader)\n",
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" train_ppl = torch.exp(train_epoch_loss)\n",
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" print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "6cafa67b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"accuracy=97.3568281938326 % on the evaluation dataset\n",
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"eval_preds[:10]=['neutral', 'neutral', 'neutral', 'positive', 'neutral', 'positive', 'positive', 'neutral', 'neutral', 'neutral']\n",
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"dataset['validation']['text_label'][:10]=['neutral', 'neutral', 'neutral', 'positive', 'neutral', 'positive', 'positive', 'neutral', 'neutral', 'neutral']\n"
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]
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}
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],
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"source": [
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"# print accuracy\n",
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"correct = 0\n",
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"total = 0\n",
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"for pred, true in zip(eval_preds, dataset[\"validation\"][\"text_label\"]):\n",
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" if pred.strip() == true.strip():\n",
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" correct += 1\n",
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" total += 1\n",
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"accuracy = correct / total * 100\n",
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"print(f\"{accuracy=} % on the evaluation dataset\")\n",
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"print(f\"{eval_preds[:10]=}\")\n",
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"print(f\"{dataset['validation']['text_label'][:10]=}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "a8de6005",
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"metadata": {},
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"outputs": [],
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"source": [
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"# saving model\n",
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"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
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"model.save_pretrained(peft_model_id)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "bd20cd4c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"9,2M\tbigscience/mt0-large_LORA_SEQ_2_SEQ_LM/adapter_model.bin\r\n"
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]
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}
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],
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"source": [
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"ckpt = f\"{peft_model_id}/adapter_model.bin\"\n",
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"!du -h $ckpt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "76c2fc29",
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"metadata": {},
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"outputs": [],
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"source": [
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"from peft import PeftModel, PeftConfig\n",
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"\n",
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"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
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"\n",
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"config = PeftConfig.from_pretrained(peft_model_id)\n",
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"model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n",
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"model = PeftModel.from_pretrained(model, peft_model_id)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "37d712ce",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"- Demand for fireplace products was lower than expected , especially in Germany .\n",
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"{'input_ids': tensor([[ 259, 264, 259, 82903, 332, 1090, 10040, 10371, 639, 259,\n",
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" 19540, 2421, 259, 25505, 259, 261, 259, 21230, 281, 17052,\n",
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" 259, 260, 1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
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"tensor([[ 0, 259, 32588, 1]])\n",
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"['negative']\n"
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]
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|
}
|
|
],
|
|
"source": [
|
|
"model.eval()\n",
|
|
"i = 13\n",
|
|
"inputs = tokenizer(dataset[\"validation\"][text_column][i], return_tensors=\"pt\")\n",
|
|
"print(dataset[\"validation\"][text_column][i])\n",
|
|
"print(inputs)\n",
|
|
"\n",
|
|
"with torch.no_grad():\n",
|
|
" outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
|
|
" print(outputs)\n",
|
|
" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "66c65ea4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "65e71f78",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.5"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|