wehub-resource-sync
caf324b09d
Build documentation / build (push) Failing after 0s
Deploy "method_comparison" Gradio to Spaces / deploy (push) Has been cancelled
Deploy "PEFT shop" Gradio app to Spaces / deploy (push) Has been cancelled
tests on transformers main / tests (push) Has been cancelled
tests / check_code_quality (push) Has been cancelled
tests / tests (ubuntu-latest, 3.10) (push) Has been cancelled
tests / tests (ubuntu-latest, 3.11) (push) Has been cancelled
tests / tests (ubuntu-latest, 3.12) (push) Has been cancelled
tests / tests (ubuntu-latest, 3.13) (push) Has been cancelled
tests / tests (windows-latest, 3.10) (push) Has been cancelled
tests / tests (windows-latest, 3.11) (push) Has been cancelled
tests / tests (windows-latest, 3.12) (push) Has been cancelled
tests / tests (windows-latest, 3.13) (push) Has been cancelled
Secret Leaks / trufflehog (push) Has been cancelled
CI security linting / zizmor latest via Cargo (push) Has been cancelled
1376 lines
70 KiB
Plaintext
1376 lines
70 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "71fbfca2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from transformers import AutoModelForCausalLM\n",
|
|
"from peft import get_peft_config, get_peft_model, PrefixTuningConfig, TaskType, PeftType\n",
|
|
"import torch\n",
|
|
"from datasets import load_dataset\n",
|
|
"import os\n",
|
|
"from transformers import AutoTokenizer\n",
|
|
"from torch.utils.data import DataLoader\n",
|
|
"from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
|
|
"from tqdm import tqdm\n",
|
|
"\n",
|
|
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
|
|
"model_name_or_path = \"bigscience/bloomz-560m\"\n",
|
|
"tokenizer_name_or_path = \"bigscience/bloomz-560m\"\n",
|
|
"peft_config = PrefixTuningConfig(task_type=TaskType.CAUSAL_LM, num_virtual_tokens=30)\n",
|
|
"\n",
|
|
"dataset_name = \"twitter_complaints\"\n",
|
|
"checkpoint_name = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}_v1.pt\".replace(\n",
|
|
" \"/\", \"_\"\n",
|
|
")\n",
|
|
"text_column = \"Tweet text\"\n",
|
|
"label_column = \"text_label\"\n",
|
|
"max_length = 64\n",
|
|
"lr = 3e-2\n",
|
|
"num_epochs = 50\n",
|
|
"batch_size = 8"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e1a3648b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Found cached dataset raft (/home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "56d9908a2c8944b484348cc46b16a261",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
" 0%| | 0/2 [00:00<?, ?it/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84/cache-20a7622c86d80cdf.arrow\n",
|
|
"Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84/cache-5f1431311da05803.arrow\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['Unlabeled', 'complaint', 'no complaint']\n",
|
|
"DatasetDict({\n",
|
|
" train: Dataset({\n",
|
|
" features: ['Tweet text', 'ID', 'Label', 'text_label'],\n",
|
|
" num_rows: 50\n",
|
|
" })\n",
|
|
" test: Dataset({\n",
|
|
" features: ['Tweet text', 'ID', 'Label', 'text_label'],\n",
|
|
" num_rows: 3399\n",
|
|
" })\n",
|
|
"})\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'Tweet text': '@HMRCcustomers No this is my first job',\n",
|
|
" 'ID': 0,\n",
|
|
" 'Label': 2,\n",
|
|
" 'text_label': 'no complaint'}"
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"dataset = load_dataset(\n",
|
|
" \"parquet\",\n",
|
|
" data_files={\n",
|
|
" \"train\": f\"hf://datasets/ought/raft@refs/convert/parquet/{dataset_name}/train/0000.parquet\",\n",
|
|
" \"test\": f\"hf://datasets/ought/raft@refs/convert/parquet/{dataset_name}/test/0000.parquet\"\n",
|
|
" }\n",
|
|
")\n",
|
|
"\n",
|
|
"\n",
|
|
"classes = [k.replace(\"_\", \" \") for k in dataset[\"train\"].features[\"Label\"].names]\n",
|
|
"print(classes)\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",
|
|
"print(dataset)\n",
|
|
"dataset[\"train\"][0]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "fe12d4d3",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"3\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "5a0e3242324842fb941950df38b459fe",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Running tokenizer on dataset: 0%| | 0/1 [00:00<?, ?ba/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "133df817b7b9468cabd5353d4d2b675b",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Running tokenizer on dataset: 0%| | 0/4 [00:00<?, ?ba/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# data preprocessing\n",
|
|
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
|
|
"if tokenizer.pad_token_id is None:\n",
|
|
" tokenizer.pad_token_id = tokenizer.eos_token_id\n",
|
|
"target_max_length = max([len(tokenizer(class_label)[\"input_ids\"]) for class_label in classes])\n",
|
|
"print(target_max_length)\n",
|
|
"\n",
|
|
"\n",
|
|
"def preprocess_function(examples):\n",
|
|
" batch_size = len(examples[text_column])\n",
|
|
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
|
|
" targets = [str(x) for x in examples[label_column]]\n",
|
|
" model_inputs = tokenizer(inputs)\n",
|
|
" labels = tokenizer(targets, add_special_tokens=False) # don't add bos token because we concatenate with inputs\n",
|
|
" for i in range(batch_size):\n",
|
|
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
|
|
" label_input_ids = labels[\"input_ids\"][i] + [tokenizer.eos_token_id]\n",
|
|
" # print(i, sample_input_ids, label_input_ids)\n",
|
|
" model_inputs[\"input_ids\"][i] = sample_input_ids + label_input_ids\n",
|
|
" labels[\"input_ids\"][i] = [-100] * len(sample_input_ids) + label_input_ids\n",
|
|
" model_inputs[\"attention_mask\"][i] = [1] * len(model_inputs[\"input_ids\"][i])\n",
|
|
" # print(model_inputs)\n",
|
|
" for i in range(batch_size):\n",
|
|
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
|
|
" label_input_ids = labels[\"input_ids\"][i]\n",
|
|
" model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
|
|
" max_length - len(sample_input_ids)\n",
|
|
" ) + sample_input_ids\n",
|
|
" model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
|
|
" \"attention_mask\"\n",
|
|
" ][i]\n",
|
|
" labels[\"input_ids\"][i] = [-100] * (max_length - len(sample_input_ids)) + label_input_ids\n",
|
|
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
|
|
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
|
|
" labels[\"input_ids\"][i] = torch.tensor(labels[\"input_ids\"][i][:max_length])\n",
|
|
" model_inputs[\"labels\"] = labels[\"input_ids\"]\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[\"train\"]\n",
|
|
"\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": null,
|
|
"id": "641b21fe",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def test_preprocess_function(examples):\n",
|
|
" batch_size = len(examples[text_column])\n",
|
|
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
|
|
" model_inputs = tokenizer(inputs)\n",
|
|
" # print(model_inputs)\n",
|
|
" for i in range(batch_size):\n",
|
|
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
|
|
" model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
|
|
" max_length - len(sample_input_ids)\n",
|
|
" ) + sample_input_ids\n",
|
|
" model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
|
|
" \"attention_mask\"\n",
|
|
" ][i]\n",
|
|
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
|
|
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
|
|
" return model_inputs\n",
|
|
"\n",
|
|
"\n",
|
|
"test_dataset = dataset[\"test\"].map(\n",
|
|
" test_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",
|
|
"test_dataloader = DataLoader(test_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n",
|
|
"next(iter(test_dataloader))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "accc5012",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"next(iter(train_dataloader))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "218df807",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"425"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(test_dataloader)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "47d1fedf",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"next(iter(test_dataloader))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "a773e092",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"trainable params: 1474560 || all params: 560689152 || trainable%: 0.26299064191632515\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# creating model\n",
|
|
"model = AutoModelForCausalLM.from_pretrained(model_name_or_path)\n",
|
|
"model = get_peft_model(model, peft_config)\n",
|
|
"model.print_trainable_parameters()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "22822901",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "023cb942",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"PrefixTuningConfig(peft_type=<PeftType.PREFIX_TUNING: 'PREFIX_TUNING'>, base_model_name_or_path='bigscience/bloomz-560m', task_type=<TaskType.CAUSAL_LM: 'CAUSAL_LM'>, inference_mode=False, num_virtual_tokens=30, token_dim=1024, num_transformer_submodules=1, num_attention_heads=16, num_layers=24, encoder_hidden_size=1024, prefix_projection=False)"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.peft_config"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "b2f91568",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# model\n",
|
|
"# 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": 14,
|
|
"id": "e4fb69fc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:01<00:00, 5.79it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.51it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=0: train_ppl=tensor(1.8325e+09, device='cuda:0') train_epoch_loss=tensor(21.3289, device='cuda:0') eval_ppl=tensor(2713.4180, device='cuda:0') eval_epoch_loss=tensor(7.9060, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.53it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=1: train_ppl=tensor(341.0600, device='cuda:0') train_epoch_loss=tensor(5.8321, device='cuda:0') eval_ppl=tensor(80.8206, device='cuda:0') eval_epoch_loss=tensor(4.3922, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.55it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=2: train_ppl=tensor(59.8778, device='cuda:0') train_epoch_loss=tensor(4.0923, device='cuda:0') eval_ppl=tensor(34.4593, device='cuda:0') eval_epoch_loss=tensor(3.5398, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.45it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.55it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=3: train_ppl=tensor(22.3307, device='cuda:0') train_epoch_loss=tensor(3.1060, device='cuda:0') eval_ppl=tensor(12.5947, device='cuda:0') eval_epoch_loss=tensor(2.5333, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.45it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.56it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=4: train_ppl=tensor(9.1697, device='cuda:0') train_epoch_loss=tensor(2.2159, device='cuda:0') eval_ppl=tensor(4.5289, device='cuda:0') eval_epoch_loss=tensor(1.5105, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.45it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.52it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=5: train_ppl=tensor(3.0172, device='cuda:0') train_epoch_loss=tensor(1.1043, device='cuda:0') eval_ppl=tensor(1.8092, device='cuda:0') eval_epoch_loss=tensor(0.5929, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.45it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=6: train_ppl=tensor(1.4885, device='cuda:0') train_epoch_loss=tensor(0.3978, device='cuda:0') eval_ppl=tensor(1.4449, device='cuda:0') eval_epoch_loss=tensor(0.3680, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.48it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=7: train_ppl=tensor(1.2967, device='cuda:0') train_epoch_loss=tensor(0.2598, device='cuda:0') eval_ppl=tensor(1.1587, device='cuda:0') eval_epoch_loss=tensor(0.1473, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.47it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=8: train_ppl=tensor(1.1305, device='cuda:0') train_epoch_loss=tensor(0.1227, device='cuda:0') eval_ppl=tensor(1.0874, device='cuda:0') eval_epoch_loss=tensor(0.0838, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.45it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.46it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=9: train_ppl=tensor(1.1608, device='cuda:0') train_epoch_loss=tensor(0.1491, device='cuda:0') eval_ppl=tensor(1.1461, device='cuda:0') eval_epoch_loss=tensor(0.1364, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.45it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.45it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=10: train_ppl=tensor(1.3172, device='cuda:0') train_epoch_loss=tensor(0.2755, device='cuda:0') eval_ppl=tensor(1.1320, device='cuda:0') eval_epoch_loss=tensor(0.1240, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.46it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=11: train_ppl=tensor(1.1437, device='cuda:0') train_epoch_loss=tensor(0.1343, device='cuda:0') eval_ppl=tensor(1.0676, device='cuda:0') eval_epoch_loss=tensor(0.0654, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.43it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=12: train_ppl=tensor(1.0651, device='cuda:0') train_epoch_loss=tensor(0.0630, device='cuda:0') eval_ppl=tensor(1.0735, device='cuda:0') eval_epoch_loss=tensor(0.0710, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.46it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.47it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=13: train_ppl=tensor(1.0607, device='cuda:0') train_epoch_loss=tensor(0.0589, device='cuda:0') eval_ppl=tensor(1.0399, device='cuda:0') eval_epoch_loss=tensor(0.0391, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.44it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=14: train_ppl=tensor(1.0351, device='cuda:0') train_epoch_loss=tensor(0.0345, device='cuda:0') eval_ppl=tensor(1.0260, device='cuda:0') eval_epoch_loss=tensor(0.0257, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.43it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=15: train_ppl=tensor(1.0217, device='cuda:0') train_epoch_loss=tensor(0.0215, device='cuda:0') eval_ppl=tensor(1.0168, device='cuda:0') eval_epoch_loss=tensor(0.0167, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.28it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=16: train_ppl=tensor(1.0152, device='cuda:0') train_epoch_loss=tensor(0.0151, device='cuda:0') eval_ppl=tensor(1.0117, device='cuda:0') eval_epoch_loss=tensor(0.0116, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.41it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=17: train_ppl=tensor(1.0102, device='cuda:0') train_epoch_loss=tensor(0.0101, device='cuda:0') eval_ppl=tensor(1.0088, device='cuda:0') eval_epoch_loss=tensor(0.0088, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.29it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.25it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=18: train_ppl=tensor(1.0083, device='cuda:0') train_epoch_loss=tensor(0.0083, device='cuda:0') eval_ppl=tensor(1.0073, device='cuda:0') eval_epoch_loss=tensor(0.0073, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.46it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=19: train_ppl=tensor(1.0070, device='cuda:0') train_epoch_loss=tensor(0.0070, device='cuda:0') eval_ppl=tensor(1.0064, device='cuda:0') eval_epoch_loss=tensor(0.0063, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.51it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=20: train_ppl=tensor(1.0059, device='cuda:0') train_epoch_loss=tensor(0.0059, device='cuda:0') eval_ppl=tensor(1.0057, device='cuda:0') eval_epoch_loss=tensor(0.0057, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.47it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=21: train_ppl=tensor(1.0056, device='cuda:0') train_epoch_loss=tensor(0.0056, device='cuda:0') eval_ppl=tensor(1.0052, device='cuda:0') eval_epoch_loss=tensor(0.0052, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.41it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.33it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=22: train_ppl=tensor(1.0050, device='cuda:0') train_epoch_loss=tensor(0.0050, device='cuda:0') eval_ppl=tensor(1.0049, device='cuda:0') eval_epoch_loss=tensor(0.0049, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.39it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.44it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=23: train_ppl=tensor(1.0049, device='cuda:0') train_epoch_loss=tensor(0.0049, device='cuda:0') eval_ppl=tensor(1.0045, device='cuda:0') eval_epoch_loss=tensor(0.0045, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.42it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.49it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=24: train_ppl=tensor(1.0043, device='cuda:0') train_epoch_loss=tensor(0.0043, device='cuda:0') eval_ppl=tensor(1.0043, device='cuda:0') eval_epoch_loss=tensor(0.0043, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.46it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.47it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=25: train_ppl=tensor(1.0042, device='cuda:0') train_epoch_loss=tensor(0.0042, device='cuda:0') eval_ppl=tensor(1.0040, device='cuda:0') eval_epoch_loss=tensor(0.0040, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.52it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=26: train_ppl=tensor(1.0039, device='cuda:0') train_epoch_loss=tensor(0.0039, device='cuda:0') eval_ppl=tensor(1.0039, device='cuda:0') eval_epoch_loss=tensor(0.0039, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.48it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=27: train_ppl=tensor(1.0038, device='cuda:0') train_epoch_loss=tensor(0.0038, device='cuda:0') eval_ppl=tensor(1.0037, device='cuda:0') eval_epoch_loss=tensor(0.0037, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.46it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.54it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=28: train_ppl=tensor(1.0036, device='cuda:0') train_epoch_loss=tensor(0.0036, device='cuda:0') eval_ppl=tensor(1.0035, device='cuda:0') eval_epoch_loss=tensor(0.0035, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.45it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.53it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=29: train_ppl=tensor(1.0034, device='cuda:0') train_epoch_loss=tensor(0.0034, device='cuda:0') eval_ppl=tensor(1.0034, device='cuda:0') eval_epoch_loss=tensor(0.0034, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.47it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=30: train_ppl=tensor(1.0034, device='cuda:0') train_epoch_loss=tensor(0.0034, device='cuda:0') eval_ppl=tensor(1.0033, device='cuda:0') eval_epoch_loss=tensor(0.0033, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.47it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=31: train_ppl=tensor(1.0033, device='cuda:0') train_epoch_loss=tensor(0.0033, device='cuda:0') eval_ppl=tensor(1.0032, device='cuda:0') eval_epoch_loss=tensor(0.0032, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.46it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.51it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=32: train_ppl=tensor(1.0031, device='cuda:0') train_epoch_loss=tensor(0.0031, device='cuda:0') eval_ppl=tensor(1.0031, device='cuda:0') eval_epoch_loss=tensor(0.0031, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.43it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=33: train_ppl=tensor(1.0030, device='cuda:0') train_epoch_loss=tensor(0.0030, device='cuda:0') eval_ppl=tensor(1.0030, device='cuda:0') eval_epoch_loss=tensor(0.0030, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.45it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.46it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=34: train_ppl=tensor(1.0029, device='cuda:0') train_epoch_loss=tensor(0.0029, device='cuda:0') eval_ppl=tensor(1.0029, device='cuda:0') eval_epoch_loss=tensor(0.0029, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.45it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.47it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=35: train_ppl=tensor(1.0028, device='cuda:0') train_epoch_loss=tensor(0.0028, device='cuda:0') eval_ppl=tensor(1.0029, device='cuda:0') eval_epoch_loss=tensor(0.0029, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.45it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.45it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=36: train_ppl=tensor(1.0027, device='cuda:0') train_epoch_loss=tensor(0.0027, device='cuda:0') eval_ppl=tensor(1.0028, device='cuda:0') eval_epoch_loss=tensor(0.0028, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.45it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.47it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=37: train_ppl=tensor(1.0027, device='cuda:0') train_epoch_loss=tensor(0.0027, device='cuda:0') eval_ppl=tensor(1.0027, device='cuda:0') eval_epoch_loss=tensor(0.0027, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.45it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.46it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=38: train_ppl=tensor(1.0027, device='cuda:0') train_epoch_loss=tensor(0.0027, device='cuda:0') eval_ppl=tensor(1.0027, device='cuda:0') eval_epoch_loss=tensor(0.0027, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.43it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=39: train_ppl=tensor(1.0025, device='cuda:0') train_epoch_loss=tensor(0.0025, device='cuda:0') eval_ppl=tensor(1.0026, device='cuda:0') eval_epoch_loss=tensor(0.0026, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.47it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=40: train_ppl=tensor(1.0026, device='cuda:0') train_epoch_loss=tensor(0.0026, device='cuda:0') eval_ppl=tensor(1.0026, device='cuda:0') eval_epoch_loss=tensor(0.0026, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.33it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=41: train_ppl=tensor(1.0025, device='cuda:0') train_epoch_loss=tensor(0.0025, device='cuda:0') eval_ppl=tensor(1.0025, device='cuda:0') eval_epoch_loss=tensor(0.0025, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.42it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.49it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=42: train_ppl=tensor(1.0024, device='cuda:0') train_epoch_loss=tensor(0.0024, device='cuda:0') eval_ppl=tensor(1.0025, device='cuda:0') eval_epoch_loss=tensor(0.0025, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.47it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=43: train_ppl=tensor(1.0024, device='cuda:0') train_epoch_loss=tensor(0.0024, device='cuda:0') eval_ppl=tensor(1.0025, device='cuda:0') eval_epoch_loss=tensor(0.0025, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.44it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.43it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=44: train_ppl=tensor(1.0025, device='cuda:0') train_epoch_loss=tensor(0.0024, device='cuda:0') eval_ppl=tensor(1.0024, device='cuda:0') eval_epoch_loss=tensor(0.0024, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.50it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=45: train_ppl=tensor(1.0024, device='cuda:0') train_epoch_loss=tensor(0.0024, device='cuda:0') eval_ppl=tensor(1.0024, device='cuda:0') eval_epoch_loss=tensor(0.0024, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.43it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.49it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=46: train_ppl=tensor(1.0024, device='cuda:0') train_epoch_loss=tensor(0.0024, device='cuda:0') eval_ppl=tensor(1.0024, device='cuda:0') eval_epoch_loss=tensor(0.0024, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.42it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.39it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=47: train_ppl=tensor(1.0023, device='cuda:0') train_epoch_loss=tensor(0.0023, device='cuda:0') eval_ppl=tensor(1.0024, device='cuda:0') eval_epoch_loss=tensor(0.0024, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.40it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 22.40it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=48: train_ppl=tensor(1.0023, device='cuda:0') train_epoch_loss=tensor(0.0023, device='cuda:0') eval_ppl=tensor(1.0024, device='cuda:0') eval_epoch_loss=tensor(0.0024, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.41it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 21.87it/s]"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch=49: train_ppl=tensor(1.0023, device='cuda:0') train_epoch_loss=tensor(0.0023, device='cuda:0') eval_ppl=tensor(1.0024, device='cuda:0') eval_epoch_loss=tensor(0.0024, device='cuda:0')\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"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",
|
|
" # print(batch)\n",
|
|
" # print(batch[\"input_ids\"].shape)\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": 36,
|
|
"id": "53752a7b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Hey @nytimes your link to cancel my subscription isn't working and nobody is answering the chat. Please don't play that kind of stupid game.\n",
|
|
"{'input_ids': tensor([[227985, 5484, 915, 54078, 2566, 7782, 24502, 2632, 8989,\n",
|
|
" 427, 36992, 2670, 140711, 21994, 10789, 530, 88399, 632,\n",
|
|
" 183542, 368, 44799, 17, 29901, 5926, 7229, 861, 11596,\n",
|
|
" 461, 78851, 14775, 17, 77658, 915, 210]]), '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]])}\n",
|
|
"tensor([[227985, 5484, 915, 54078, 2566, 7782, 24502, 2632, 8989,\n",
|
|
" 427, 36992, 2670, 140711, 21994, 10789, 530, 88399, 632,\n",
|
|
" 183542, 368, 44799, 17, 29901, 5926, 7229, 861, 11596,\n",
|
|
" 461, 78851, 14775, 17, 77658, 915, 210, 16449, 5952,\n",
|
|
" 3]], device='cuda:0')\n",
|
|
"[\"Tweet text : Hey @nytimes your link to cancel my subscription isn't working and nobody is answering the chat. Please don't play that kind of stupid game. Label : complaint\"]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.eval()\n",
|
|
"i = 16\n",
|
|
"inputs = tokenizer(f'{text_column} : {dataset[\"test\"][i][\"Tweet text\"]} Label : ', return_tensors=\"pt\")\n",
|
|
"print(dataset[\"test\"][i][\"Tweet text\"])\n",
|
|
"print(inputs)\n",
|
|
"\n",
|
|
"with torch.no_grad():\n",
|
|
" inputs = {k: v.to(device) for k, v in inputs.items()}\n",
|
|
" outputs = model.generate(\n",
|
|
" input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n",
|
|
" )\n",
|
|
" print(outputs)\n",
|
|
" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0e21c49b",
|
|
"metadata": {},
|
|
"source": [
|
|
"You can push model to hub or save model locally. \n",
|
|
"\n",
|
|
"- Option1: Pushing the model to Hugging Face Hub\n",
|
|
"```python\n",
|
|
"model.push_to_hub(\n",
|
|
" f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\".replace(\"/\", \"_\"),\n",
|
|
" token = \"hf_...\"\n",
|
|
")\n",
|
|
"```\n",
|
|
"token (`bool` or `str`, *optional*):\n",
|
|
" `token` is to be used for HTTP Bearer authorization when accessing remote files. If `True`, will use the token generated\n",
|
|
" when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url`\n",
|
|
" is not specified.\n",
|
|
" Or you can get your token from https://huggingface.co/settings/token\n",
|
|
"```\n",
|
|
"- Or save model locally\n",
|
|
"```python\n",
|
|
"peft_model_id = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\".replace(\"/\", \"_\")\n",
|
|
"model.save_pretrained(peft_model_id)\n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "24041ee1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# saving model\n",
|
|
"peft_model_id = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\".replace(\n",
|
|
" \"/\", \"_\"\n",
|
|
")\n",
|
|
"model.save_pretrained(peft_model_id)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "527eeaa4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"ckpt = f\"{peft_model_id}/adapter_model.safetensors\"\n",
|
|
"!du -h $ckpt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "b19f5a90",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from peft import PeftModel, PeftConfig\n",
|
|
"\n",
|
|
"peft_model_id = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\".replace(\n",
|
|
" \"/\", \"_\"\n",
|
|
")\n",
|
|
"\n",
|
|
"config = PeftConfig.from_pretrained(peft_model_id)\n",
|
|
"model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)\n",
|
|
"model = PeftModel.from_pretrained(model, peft_model_id)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "a11a3768",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"@greateranglia Ok thanks...\n",
|
|
"{'input_ids': tensor([[227985, 5484, 915, 2566, 14173, 2960, 29906, 387, 20706,\n",
|
|
" 49337, 1369, 77658, 915, 210]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
|
|
"tensor([[227985, 5484, 915, 2566, 14173, 2960, 29906, 387, 20706,\n",
|
|
" 49337, 1369, 77658, 915, 210, 1936, 106863, 3]],\n",
|
|
" device='cuda:0')\n",
|
|
"['Tweet text : @greateranglia Ok thanks... Label : no complaint']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.to(device)\n",
|
|
"model.eval()\n",
|
|
"i = 4\n",
|
|
"inputs = tokenizer(f'{text_column} : {dataset[\"test\"][i][\"Tweet text\"]} Label : ', return_tensors=\"pt\")\n",
|
|
"print(dataset[\"test\"][i][\"Tweet text\"])\n",
|
|
"print(inputs)\n",
|
|
"\n",
|
|
"with torch.no_grad():\n",
|
|
" inputs = {k: v.to(device) for k, v in inputs.items()}\n",
|
|
" outputs = model.generate(\n",
|
|
" input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n",
|
|
" )\n",
|
|
" print(outputs)\n",
|
|
" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "f890c951",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "463a41a2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "5c60c7a9",
|
|
"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
|
|
}
|