wehub-resource-sync
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383 lines
12 KiB
Plaintext
Executable File
383 lines
12 KiB
Plaintext
Executable File
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5f93b7d1",
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"metadata": {},
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"outputs": [],
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"source": "from transformers import AutoModelForSeq2SeqLM\nfrom peft import get_peft_config, get_peft_model, get_peft_model_state_dict, LoraConfig, TaskType\nimport torch\nfrom datasets import load_dataset\nimport os\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\nfrom transformers import AutoTokenizer\nfrom torch.utils.data import DataLoader\nfrom transformers import default_data_collator, get_linear_schedule_with_warmup\nfrom tqdm import tqdm\nfrom datasets import load_dataset\n\ndevice = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\nmodel_name_or_path = \"bigscience/mt0-large\"\ntokenizer_name_or_path = \"bigscience/mt0-large\"\n\ntext_column = \"text\"\nlabel_column = \"text_label\"\nmax_length = 128\nlr = 1e-3\nnum_epochs = 3\nbatch_size = 8"
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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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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"Using the latest cached version of the dataset since financial_phrasebank couldn't be found on the Hugging Face Hub\n",
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"Found the latest cached dataset configuration 'sentences_allagree' at /root/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141 (last modified on Thu Jul 31 05:47:32 2025).\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": "867f7bbb679d4b6eae344812fb797c19",
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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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"Map: 0%| | 0/2037 [00:00<?, ? examples/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": "a6964a9de5e64d4e80c1906e2bed9f21",
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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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"Map: 0%| | 0/227 [00:00<?, ? examples/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 bank VTB24 provides mortgage loans to buy apartments in the complex at 11-13 % per annum in rubles .',\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(\"zeroshot/twitter-financial-news-sentiment\")\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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"if hasattr(dataset[\"train\"].features[\"label\"], \"names\"):\n",
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" classes = dataset[\"train\"].features[\"label\"].names\n",
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"else:\n",
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" classes = [\"Bearish\", \"Bullish\", \"Neutral\"]\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": "a867fe83918c435ab8a52bee2737f4f3",
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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%| | 0/2037 [00:00<?, ? examples/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": "97ceaf1285f348bd8272e2bec54050c6",
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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%| | 0/227 [00:00<?, ? examples/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": null,
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"id": "6b3a4090",
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"metadata": {},
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"outputs": [],
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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": null,
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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.safetensors\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.safetensors\"\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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}
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],
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"source": [
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"model.eval()\n",
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"i = 13\n",
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"inputs = tokenizer(dataset[\"validation\"][text_column][i], return_tensors=\"pt\")\n",
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"print(dataset[\"validation\"][text_column][i])\n",
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"print(inputs)\n",
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"\n",
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"with torch.no_grad():\n",
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" outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
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" print(outputs)\n",
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" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=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": null,
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"id": "66c65ea4",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "65e71f78",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
|
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"name": "ipython",
|
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
|
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
|
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"version": "3.11.13"
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},
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"vscode": {
|
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"interpreter": {
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"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|