wehub-resource-sync
caf324b09d
tests / check_code_quality (push) Waiting to run
tests / tests (ubuntu-latest, 3.10) (push) Blocked by required conditions
tests / tests (ubuntu-latest, 3.11) (push) Blocked by required conditions
Deploy "method_comparison" Gradio to Spaces / deploy (push) Waiting to run
Deploy "PEFT shop" Gradio app to Spaces / deploy (push) Waiting to run
tests on transformers main / tests (push) Waiting to run
tests / tests (ubuntu-latest, 3.12) (push) Blocked by required conditions
tests / tests (ubuntu-latest, 3.13) (push) Blocked by required conditions
tests / tests (windows-latest, 3.10) (push) Blocked by required conditions
tests / tests (windows-latest, 3.11) (push) Blocked by required conditions
tests / tests (windows-latest, 3.12) (push) Blocked by required conditions
tests / tests (windows-latest, 3.13) (push) Blocked by required conditions
Secret Leaks / trufflehog (push) Waiting to run
CI security linting / zizmor latest via Cargo (push) Waiting to run
Build documentation / build (push) Failing after 0s
405 lines
13 KiB
Plaintext
Executable File
405 lines
13 KiB
Plaintext
Executable File
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "5f93b7d1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": "from transformers import AutoModelForSeq2SeqLM\nfrom peft import get_peft_config, get_peft_model, get_peft_model_state_dict, PrefixTuningConfig, 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 = \"t5-large\"\ntokenizer_name_or_path = \"t5-large\"\n\ntext_column = \"text\"\nlabel_column = \"text_label\"\nmax_length = 128\nlr = 1e-2\nnum_epochs = 5\nbatch_size = 8"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "8d0850ac",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# creating model\n",
|
|
"peft_config = PrefixTuningConfig(task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, num_virtual_tokens=20)\n",
|
|
"\n",
|
|
"model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n",
|
|
"model = get_peft_model(model, peft_config)\n",
|
|
"model.print_trainable_parameters()\n",
|
|
"model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "4ee2babf",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Using the latest cached version of the dataset since financial_phrasebank couldn't be found on the Hugging Face Hub\n",
|
|
"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 06:23:15 2025).\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "3b321971d6f942418bd5ef6105a1aa65",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Map: 0%| | 0/2037 [00:00<?, ? examples/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "5997543529a849bf97719e59a5ec95b2",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Map: 0%| | 0/227 [00:00<?, ? examples/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'sentence': \"Progress Group , QPR 's representative in Saudi Arabia and North Africa , has signed a framework agreement for a long term strategic relationship with ISE .\",\n",
|
|
" 'label': 2,\n",
|
|
" 'text_label': 'positive'}"
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# loading dataset\n",
|
|
"dataset = load_dataset(\"zeroshot/twitter-financial-news-sentiment\")\n",
|
|
"dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
|
|
"dataset[\"validation\"] = dataset[\"test\"]\n",
|
|
"del dataset[\"test\"]\n",
|
|
"\n",
|
|
"if hasattr(dataset[\"train\"].features[\"label\"], \"names\"):\n",
|
|
" classes = dataset[\"train\"].features[\"label\"].names\n",
|
|
"else:\n",
|
|
" classes = [\"Bearish\", \"Bullish\", \"Neutral\"]\n",
|
|
"dataset = dataset.map(\n",
|
|
" lambda x: {\"text_label\": [classes[label] for label in x[\"label\"]]},\n",
|
|
" batched=True,\n",
|
|
" num_proc=1,\n",
|
|
")\n",
|
|
"\n",
|
|
"dataset[\"train\"][0]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "adf9608c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "ee9bf13a2e3f4812a51a87346a4614f3",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"spiece.model: 0%| | 0.00/792k [00:00<?, ?B/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "332fcaa33dc343e7a20b24cec7ec97e9",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"tokenizer.json: 0%| | 0.00/1.39M [00:00<?, ?B/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "a2135262c7a44377b35fe32b8d86d6c6",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Running tokenizer on dataset: 0%| | 0/2037 [00:00<?, ? examples/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "dc8598b160484939b27c65be001c694c",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Running tokenizer on dataset: 0%| | 0/227 [00:00<?, ? examples/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# data preprocessing\n",
|
|
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
|
|
"\n",
|
|
"\n",
|
|
"def preprocess_function(examples):\n",
|
|
" inputs = examples[text_column]\n",
|
|
" targets = examples[label_column]\n",
|
|
" model_inputs = tokenizer(inputs, max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
|
|
" labels = tokenizer(targets, max_length=2, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
|
|
" labels = labels[\"input_ids\"]\n",
|
|
" labels[labels == tokenizer.pad_token_id] = -100\n",
|
|
" model_inputs[\"labels\"] = labels\n",
|
|
" return model_inputs\n",
|
|
"\n",
|
|
"\n",
|
|
"processed_datasets = dataset.map(\n",
|
|
" preprocess_function,\n",
|
|
" batched=True,\n",
|
|
" num_proc=1,\n",
|
|
" remove_columns=dataset[\"train\"].column_names,\n",
|
|
" load_from_cache_file=False,\n",
|
|
" desc=\"Running tokenizer on dataset\",\n",
|
|
")\n",
|
|
"\n",
|
|
"train_dataset = processed_datasets[\"train\"]\n",
|
|
"eval_dataset = processed_datasets[\"validation\"]\n",
|
|
"\n",
|
|
"train_dataloader = DataLoader(\n",
|
|
" train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
|
|
")\n",
|
|
"eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "f733a3c6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# optimizer and lr scheduler\n",
|
|
"optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
|
|
"lr_scheduler = get_linear_schedule_with_warmup(\n",
|
|
" optimizer=optimizer,\n",
|
|
" num_warmup_steps=0,\n",
|
|
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "6b3a4090",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# training and evaluation\n",
|
|
"model = model.to(device)\n",
|
|
"\n",
|
|
"for epoch in range(num_epochs):\n",
|
|
" model.train()\n",
|
|
" total_loss = 0\n",
|
|
" for step, batch in enumerate(tqdm(train_dataloader)):\n",
|
|
" batch = {k: v.to(device) for k, v in batch.items()}\n",
|
|
" outputs = model(**batch)\n",
|
|
" loss = outputs.loss\n",
|
|
" total_loss += loss.detach().float()\n",
|
|
" loss.backward()\n",
|
|
" optimizer.step()\n",
|
|
" lr_scheduler.step()\n",
|
|
" optimizer.zero_grad()\n",
|
|
"\n",
|
|
" model.eval()\n",
|
|
" eval_loss = 0\n",
|
|
" eval_preds = []\n",
|
|
" for step, batch in enumerate(tqdm(eval_dataloader)):\n",
|
|
" batch = {k: v.to(device) for k, v in batch.items()}\n",
|
|
" with torch.no_grad():\n",
|
|
" outputs = model(**batch)\n",
|
|
" loss = outputs.loss\n",
|
|
" eval_loss += loss.detach().float()\n",
|
|
" eval_preds.extend(\n",
|
|
" tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n",
|
|
" )\n",
|
|
"\n",
|
|
" eval_epoch_loss = eval_loss / len(eval_dataloader)\n",
|
|
" eval_ppl = torch.exp(eval_epoch_loss)\n",
|
|
" train_epoch_loss = total_loss / len(train_dataloader)\n",
|
|
" train_ppl = torch.exp(train_epoch_loss)\n",
|
|
" print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "6cafa67b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"accuracy=96.91629955947137 % on the evaluation dataset\n",
|
|
"eval_preds[:10]=['negative', 'positive', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral']\n",
|
|
"dataset['validation']['text_label'][:10]=['negative', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# print accuracy\n",
|
|
"correct = 0\n",
|
|
"total = 0\n",
|
|
"for pred, true in zip(eval_preds, dataset[\"validation\"][\"text_label\"]):\n",
|
|
" if pred.strip() == true.strip():\n",
|
|
" correct += 1\n",
|
|
" total += 1\n",
|
|
"accuracy = correct / total * 100\n",
|
|
"print(f\"{accuracy=} % on the evaluation dataset\")\n",
|
|
"print(f\"{eval_preds[:10]=}\")\n",
|
|
"print(f\"{dataset['validation']['text_label'][:10]=}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "a8de6005",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# saving model\n",
|
|
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
|
|
"model.save_pretrained(peft_model_id)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "bd20cd4c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"3,8M\tt5-large_PREFIX_TUNING_SEQ_2_SEQ_LM/adapter_model.safetensors\r\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"ckpt = f\"{peft_model_id}/adapter_model.safetensors\"\n",
|
|
"!du -h $ckpt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "76c2fc29",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from peft import PeftModel, PeftConfig\n",
|
|
"\n",
|
|
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
|
|
"\n",
|
|
"config = PeftConfig.from_pretrained(peft_model_id)\n",
|
|
"model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n",
|
|
"model = PeftModel.from_pretrained(model, peft_model_id)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "d997f1cc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Acando AB ( ACANB SS ) fell 8.9 percent to 13.35 kronor , the lowest close since Dec. 11 .\n",
|
|
"{'input_ids': tensor([[ 4292, 232, 32, 3, 5359, 41, 3, 22029, 14972, 3,\n",
|
|
" 4256, 3, 61, 4728, 4848, 1298, 1093, 12, 8808, 2469,\n",
|
|
" 3, 22318, 29, 127, 3, 6, 8, 7402, 885, 437,\n",
|
|
" 4451, 5, 850, 3, 5, 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, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
|
|
"tensor([[ 0, 2841, 1]])\n",
|
|
"['negative']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.eval()\n",
|
|
"i = 107\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": "fb746c1e",
|
|
"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": "3.11.13"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|