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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
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"Collecting urllib3<1.27,>=1.21.1\n",
|
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" Downloading urllib3-1.26.14-py2.py3-none-any.whl (140 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
|
||
"Installing collected packages: tokenizers, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets, evaluate\n",
|
||
" Attempting uninstall: urllib3\n",
|
||
" Found existing installation: urllib3 1.24.3\n",
|
||
" Uninstalling urllib3-1.24.3:\n",
|
||
" Successfully uninstalled urllib3-1.24.3\n",
|
||
"Successfully installed datasets-2.9.0 evaluate-0.4.0 huggingface-hub-0.12.0 multiprocess-0.70.14 responses-0.18.0 tokenizers-0.13.2 transformers-4.26.1 urllib3-1.26.14 xxhash-3.2.0\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
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{
|
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"cell_type": "markdown",
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"source": [],
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"metadata": {
|
||
"id": "a_f-yno_zity"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"!wget 'https://raw.githubusercontent.com/huggingface/transformers/v4.23.1/examples/pytorch/text-classification/run_glue.py' -O 'run_glue.py'"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "V_HmRNcmzhsw",
|
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"outputId": "310ed497-e8ba-429e-9a54-00124cf0cd25"
|
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},
|
||
"execution_count": 2,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"--2023-02-10 21:16:19-- https://raw.githubusercontent.com/huggingface/transformers/v4.23.1/examples/pytorch/text-classification/run_glue.py\n",
|
||
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
|
||
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
|
||
"HTTP request sent, awaiting response... 200 OK\n",
|
||
"Length: 27259 (27K) [text/plain]\n",
|
||
"Saving to: ‘run_glue.py’\n",
|
||
"\n",
|
||
"\rrun_glue.py 0%[ ] 0 --.-KB/s \rrun_glue.py 100%[===================>] 26.62K --.-KB/s in 0.003s \n",
|
||
"\n",
|
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"2023-02-10 21:16:19 (9.37 MB/s) - ‘run_glue.py’ saved [27259/27259]\n",
|
||
"\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import json\n",
|
||
"from pathlib import Path\n",
|
||
"from typing import Dict, List\n",
|
||
"from datasets import load_dataset\n",
|
||
"\n",
|
||
"loaded_data = load_dataset('emotion')\n",
|
||
"\n",
|
||
"!mkdir -v -p data\n",
|
||
"\n",
|
||
"train_path = Path('data/train.json')\n",
|
||
"valid_path = Path('data/valid.json')\n",
|
||
"test_path = Path('data/test.json')\n",
|
||
"data_train, data_valid, data_test = [], [], []\n",
|
||
"\n",
|
||
"for source_data, dataset, max_size in [\n",
|
||
" (loaded_data['train'], data_train, None),\n",
|
||
" (loaded_data['test'], data_valid, None),\n",
|
||
"]:\n",
|
||
" for i, data in enumerate(source_data):\n",
|
||
" if max_size is not None and i >= max_size:\n",
|
||
" break\n",
|
||
" data_line = {\n",
|
||
" 'label': int(data['label']),\n",
|
||
" 'text': data['text'],\n",
|
||
" }\n",
|
||
" dataset.append(data_line)\n",
|
||
"\n",
|
||
"print(f'Train: {len(data_train):6d}')\n",
|
||
"print(f'Valid: {len(data_valid):6d}')\n",
|
||
"\n",
|
||
"data_class_1, data_class_2 = [], []\n",
|
||
"\n",
|
||
"for data in data_valid:\n",
|
||
" label = data['label']\n",
|
||
" if label == 0:\n",
|
||
" data_class_1.append(data)\n",
|
||
" elif label == 1:\n",
|
||
" data_class_2.append(data)\n",
|
||
"\n",
|
||
"print(f'Label 1: {len(data_class_1):6d}')\n",
|
||
"print(f'Label 2: {len(data_class_2):6d}')\n",
|
||
"\n",
|
||
"size_half_class_1 = int(len(data_class_1) / 2)\n",
|
||
"size_half_class_2 = int(len(data_class_2) / 2)\n",
|
||
"\n",
|
||
"data_valid = data_class_1[:size_half_class_1] + data_class_2[:size_half_class_2]\n",
|
||
"data_test = data_class_1[size_half_class_1:] + data_class_2[size_half_class_2:]\n",
|
||
"\n",
|
||
"print(f'Valid: {len(data_valid):6d}')\n",
|
||
"print(f'Test : {len(data_test):6d}')\n",
|
||
"\n",
|
||
"MAP_LABEL_TRANSLATION = {\n",
|
||
" 0: 'sadness',\n",
|
||
" 1: 'joy',\n",
|
||
" 2: 'love',\n",
|
||
" 3: 'anger',\n",
|
||
" 4: 'fear',\n",
|
||
" 5: 'surprise',\n",
|
||
"}\n",
|
||
"\n",
|
||
"def save_as_translations(original_save_path: Path, data_to_save: List[Dict]) -> None:\n",
|
||
" file_name = 's2s-' + original_save_path.name\n",
|
||
" file_path = original_save_path.parent / file_name\n",
|
||
"\n",
|
||
" print(f'Saving into: {file_path}')\n",
|
||
" with open(file_path, 'wt') as f_write:\n",
|
||
" for data_line in data_to_save:\n",
|
||
" label = data_line['label']\n",
|
||
" new_label = MAP_LABEL_TRANSLATION[label]\n",
|
||
" data_line['label'] = new_label\n",
|
||
" data_line_str = json.dumps(data_line)\n",
|
||
" f_write.write(f'{data_line_str}\\n')\n",
|
||
"\n",
|
||
"for file_path, data_to_save in [(train_path, data_train), (valid_path, data_valid), (test_path, data_test)]:\n",
|
||
" print(f'Saving into: {file_path}')\n",
|
||
" with open(file_path, 'wt') as f_write:\n",
|
||
" for data_line in data_to_save:\n",
|
||
" data_line_str = json.dumps(data_line)\n",
|
||
" f_write.write(f'{data_line_str}\\n')\n",
|
||
" \n",
|
||
" save_as_translations(file_path, data_to_save)\n",
|
||
"\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 312,
|
||
"referenced_widgets": [
|
||
"0041bbf83bb64d50be7413e5aee17227",
|
||
"d9461132fa834a3c9851755ed80da514",
|
||
"0cd3573235784aa89624908bfd8a5389",
|
||
"b51cc0642ba240b6a59326587a052144",
|
||
"882e36c3c50843d9be49e4ae9068da61",
|
||
"41d1df991000411492ebeef71f504c58",
|
||
"f9522459b19842afabadf047c4ce2132",
|
||
"0ad23110c02e4339896d123df139c20d",
|
||
"d76aadfbf8c94f42bfc14ba1100823c1",
|
||
"82d1610d24fb4e1ea1408640219a7f29",
|
||
"b1ac08f1e9c44fe189ad777a20a4a055"
|
||
]
|
||
},
|
||
"id": "bcR4tWQl0rqt",
|
||
"outputId": "ae0c4861-b14b-47b8-fc12-b88423e2fd57"
|
||
},
|
||
"execution_count": 4,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"WARNING:datasets.builder:No config specified, defaulting to: emotion/split\n",
|
||
"WARNING:datasets.builder:Found cached dataset emotion (/root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd)\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
" 0%| | 0/3 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "0041bbf83bb64d50be7413e5aee17227"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Train: 16000\n",
|
||
"Valid: 2000\n",
|
||
"Label 1: 581\n",
|
||
"Label 2: 695\n",
|
||
"Valid: 637\n",
|
||
"Test : 639\n",
|
||
"Saving into: data/train.json\n",
|
||
"Saving into: data/s2s-train.json\n",
|
||
"Saving into: data/valid.json\n",
|
||
"Saving into: data/s2s-valid.json\n",
|
||
"Saving into: data/test.json\n",
|
||
"Saving into: data/s2s-test.json\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"\n",
|
||
"!head -n 2500 data/train.json > data/train-5k.json\n",
|
||
"!tail -n 2500 data/train.json >> data/train-5k.json\n",
|
||
"!wc -l data/train-5k.json"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "pRmHIvyB0fZe",
|
||
"outputId": "19360ab1-38d2-4e80-b18b-f1acc4c032bc"
|
||
},
|
||
"execution_count": 5,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"5000 data/train-5k.json\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from pathlib import Path\n",
|
||
"\n",
|
||
"for file_name in [\"train\", \"valid\", \"test\", \"s2s-train\", \"s2s-valid\", \"s2s-test\"]:\n",
|
||
" print(f\"=== {file_name} ===\")\n",
|
||
" all_text = Path(f\"data/{file_name}.json\").read_text().split('\\n')\n",
|
||
" text = all_text[:2500] + all_text[-2500:]\n",
|
||
" Path(f\"data/{file_name}-5k.json\").write_text(\"\\n\".join(text))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "rFa6ijdx2L28",
|
||
"outputId": "43106fd5-be6f-4924-d439-49c530c80caa"
|
||
},
|
||
"execution_count": 6,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== train ===\n",
|
||
"=== valid ===\n",
|
||
"=== test ===\n",
|
||
"=== s2s-train ===\n",
|
||
"=== s2s-valid ===\n",
|
||
"=== s2s-test ===\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import os\n",
|
||
"\n",
|
||
"os.environ['TOKENIZERS_PARALLELISM'] = 'true'"
|
||
],
|
||
"metadata": {
|
||
"id": "8opbDvBv3ZlK"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"!python run_glue.py \\\n",
|
||
" --cache_dir .cache_training \\\n",
|
||
" --model_name_or_path gpt2 \\\n",
|
||
" --train_file data/train-5k.json \\\n",
|
||
" --validation_file data/valid-5k.json \\\n",
|
||
" --test_file data/test-5k.json \\\n",
|
||
" --per_device_train_batch_size 24 \\\n",
|
||
" --per_device_eval_batch_size 24 \\\n",
|
||
" --do_train \\\n",
|
||
" --do_eval \\\n",
|
||
" --do_predict \\\n",
|
||
" --max_seq_length 128 \\\n",
|
||
" --learning_rate 2e-5 \\\n",
|
||
" --num_train_epochs 5 \\\n",
|
||
" --output_dir out/imdb-5k/gpt2"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "XkkeRPG_z3Jc",
|
||
"outputId": "a19a270d-5c3b-4285-d582-4510708a33d7"
|
||
},
|
||
"execution_count": 8,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"2023-02-10 21:22:54.785242: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n",
|
||
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
|
||
"2023-02-10 21:22:54.925689: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
|
||
"2023-02-10 21:22:55.662472: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
|
||
"2023-02-10 21:22:55.662568: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
|
||
"2023-02-10 21:22:55.662585: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
|
||
"WARNING:__main__:Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: False\n",
|
||
"INFO:__main__:Training/evaluation parameters TrainingArguments(\n",
|
||
"_n_gpu=1,\n",
|
||
"adafactor=False,\n",
|
||
"adam_beta1=0.9,\n",
|
||
"adam_beta2=0.999,\n",
|
||
"adam_epsilon=1e-08,\n",
|
||
"auto_find_batch_size=False,\n",
|
||
"bf16=False,\n",
|
||
"bf16_full_eval=False,\n",
|
||
"data_seed=None,\n",
|
||
"dataloader_drop_last=False,\n",
|
||
"dataloader_num_workers=0,\n",
|
||
"dataloader_pin_memory=True,\n",
|
||
"ddp_bucket_cap_mb=None,\n",
|
||
"ddp_find_unused_parameters=None,\n",
|
||
"ddp_timeout=1800,\n",
|
||
"debug=[],\n",
|
||
"deepspeed=None,\n",
|
||
"disable_tqdm=False,\n",
|
||
"do_eval=True,\n",
|
||
"do_predict=True,\n",
|
||
"do_train=True,\n",
|
||
"eval_accumulation_steps=None,\n",
|
||
"eval_delay=0,\n",
|
||
"eval_steps=None,\n",
|
||
"evaluation_strategy=no,\n",
|
||
"fp16=False,\n",
|
||
"fp16_backend=auto,\n",
|
||
"fp16_full_eval=False,\n",
|
||
"fp16_opt_level=O1,\n",
|
||
"fsdp=[],\n",
|
||
"fsdp_min_num_params=0,\n",
|
||
"fsdp_transformer_layer_cls_to_wrap=None,\n",
|
||
"full_determinism=False,\n",
|
||
"gradient_accumulation_steps=1,\n",
|
||
"gradient_checkpointing=False,\n",
|
||
"greater_is_better=None,\n",
|
||
"group_by_length=False,\n",
|
||
"half_precision_backend=auto,\n",
|
||
"hub_model_id=None,\n",
|
||
"hub_private_repo=False,\n",
|
||
"hub_strategy=every_save,\n",
|
||
"hub_token=<HUB_TOKEN>,\n",
|
||
"ignore_data_skip=False,\n",
|
||
"include_inputs_for_metrics=False,\n",
|
||
"jit_mode_eval=False,\n",
|
||
"label_names=None,\n",
|
||
"label_smoothing_factor=0.0,\n",
|
||
"learning_rate=2e-05,\n",
|
||
"length_column_name=length,\n",
|
||
"load_best_model_at_end=False,\n",
|
||
"local_rank=-1,\n",
|
||
"log_level=passive,\n",
|
||
"log_level_replica=passive,\n",
|
||
"log_on_each_node=True,\n",
|
||
"logging_dir=out/imdb-5k/gpt2/runs/Feb10_21-22-58_4bf02db3dc1f,\n",
|
||
"logging_first_step=False,\n",
|
||
"logging_nan_inf_filter=True,\n",
|
||
"logging_steps=500,\n",
|
||
"logging_strategy=steps,\n",
|
||
"lr_scheduler_type=linear,\n",
|
||
"max_grad_norm=1.0,\n",
|
||
"max_steps=-1,\n",
|
||
"metric_for_best_model=None,\n",
|
||
"mp_parameters=,\n",
|
||
"no_cuda=False,\n",
|
||
"num_train_epochs=5.0,\n",
|
||
"optim=adamw_hf,\n",
|
||
"optim_args=None,\n",
|
||
"output_dir=out/imdb-5k/gpt2,\n",
|
||
"overwrite_output_dir=False,\n",
|
||
"past_index=-1,\n",
|
||
"per_device_eval_batch_size=24,\n",
|
||
"per_device_train_batch_size=24,\n",
|
||
"prediction_loss_only=False,\n",
|
||
"push_to_hub=False,\n",
|
||
"push_to_hub_model_id=None,\n",
|
||
"push_to_hub_organization=None,\n",
|
||
"push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
|
||
"ray_scope=last,\n",
|
||
"remove_unused_columns=True,\n",
|
||
"report_to=['tensorboard'],\n",
|
||
"resume_from_checkpoint=None,\n",
|
||
"run_name=out/imdb-5k/gpt2,\n",
|
||
"save_on_each_node=False,\n",
|
||
"save_steps=500,\n",
|
||
"save_strategy=steps,\n",
|
||
"save_total_limit=None,\n",
|
||
"seed=42,\n",
|
||
"sharded_ddp=[],\n",
|
||
"skip_memory_metrics=True,\n",
|
||
"tf32=None,\n",
|
||
"torch_compile=False,\n",
|
||
"torch_compile_backend=None,\n",
|
||
"torch_compile_mode=None,\n",
|
||
"torchdynamo=None,\n",
|
||
"tpu_metrics_debug=False,\n",
|
||
"tpu_num_cores=None,\n",
|
||
"use_ipex=False,\n",
|
||
"use_legacy_prediction_loop=False,\n",
|
||
"use_mps_device=False,\n",
|
||
"warmup_ratio=0.0,\n",
|
||
"warmup_steps=0,\n",
|
||
"weight_decay=0.0,\n",
|
||
"xpu_backend=None,\n",
|
||
")\n",
|
||
"INFO:__main__:Checkpoint detected, resuming training at out/imdb-5k/gpt2/checkpoint-500. To avoid this behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\n",
|
||
"INFO:__main__:load a local file for train: data/train-5k.json\n",
|
||
"INFO:__main__:load a local file for validation: data/valid-5k.json\n",
|
||
"INFO:__main__:load a local file for test: data/test-5k.json\n",
|
||
"WARNING:datasets.builder:Using custom data configuration default-58ab9a923ac72046\n",
|
||
"INFO:datasets.info:Loading Dataset Infos from /usr/local/lib/python3.8/dist-packages/datasets/packaged_modules/json\n",
|
||
"INFO:datasets.builder:Overwrite dataset info from restored data version.\n",
|
||
"INFO:datasets.info:Loading Dataset info from .cache_training/json/default-58ab9a923ac72046/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51\n",
|
||
"WARNING:datasets.builder:Found cached dataset json (/content/.cache_training/json/default-58ab9a923ac72046/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)\n",
|
||
"INFO:datasets.info:Loading Dataset info from /content/.cache_training/json/default-58ab9a923ac72046/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51\n",
|
||
"100% 3/3 [00:00<00:00, 880.54it/s]\n",
|
||
"[INFO|configuration_utils.py:660] 2023-02-10 21:22:59,860 >> loading configuration file config.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/config.json\n",
|
||
"[INFO|configuration_utils.py:712] 2023-02-10 21:22:59,863 >> Model config GPT2Config {\n",
|
||
" \"_name_or_path\": \"gpt2\",\n",
|
||
" \"activation_function\": \"gelu_new\",\n",
|
||
" \"architectures\": [\n",
|
||
" \"GPT2LMHeadModel\"\n",
|
||
" ],\n",
|
||
" \"attn_pdrop\": 0.1,\n",
|
||
" \"bos_token_id\": 50256,\n",
|
||
" \"embd_pdrop\": 0.1,\n",
|
||
" \"eos_token_id\": 50256,\n",
|
||
" \"id2label\": {\n",
|
||
" \"0\": \"LABEL_0\",\n",
|
||
" \"1\": \"LABEL_1\",\n",
|
||
" \"2\": \"LABEL_2\",\n",
|
||
" \"3\": \"LABEL_3\",\n",
|
||
" \"4\": \"LABEL_4\",\n",
|
||
" \"5\": \"LABEL_5\"\n",
|
||
" },\n",
|
||
" \"initializer_range\": 0.02,\n",
|
||
" \"label2id\": {\n",
|
||
" \"LABEL_0\": 0,\n",
|
||
" \"LABEL_1\": 1,\n",
|
||
" \"LABEL_2\": 2,\n",
|
||
" \"LABEL_3\": 3,\n",
|
||
" \"LABEL_4\": 4,\n",
|
||
" \"LABEL_5\": 5\n",
|
||
" },\n",
|
||
" \"layer_norm_epsilon\": 1e-05,\n",
|
||
" \"model_type\": \"gpt2\",\n",
|
||
" \"n_ctx\": 1024,\n",
|
||
" \"n_embd\": 768,\n",
|
||
" \"n_head\": 12,\n",
|
||
" \"n_inner\": null,\n",
|
||
" \"n_layer\": 12,\n",
|
||
" \"n_positions\": 1024,\n",
|
||
" \"reorder_and_upcast_attn\": false,\n",
|
||
" \"resid_pdrop\": 0.1,\n",
|
||
" \"scale_attn_by_inverse_layer_idx\": false,\n",
|
||
" \"scale_attn_weights\": true,\n",
|
||
" \"summary_activation\": null,\n",
|
||
" \"summary_first_dropout\": 0.1,\n",
|
||
" \"summary_proj_to_labels\": true,\n",
|
||
" \"summary_type\": \"cls_index\",\n",
|
||
" \"summary_use_proj\": true,\n",
|
||
" \"task_specific_params\": {\n",
|
||
" \"text-generation\": {\n",
|
||
" \"do_sample\": true,\n",
|
||
" \"max_length\": 50\n",
|
||
" }\n",
|
||
" },\n",
|
||
" \"transformers_version\": \"4.26.1\",\n",
|
||
" \"use_cache\": true,\n",
|
||
" \"vocab_size\": 50257\n",
|
||
"}\n",
|
||
"\n",
|
||
"[INFO|tokenization_auto.py:458] 2023-02-10 21:22:59,992 >> Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
|
||
"[INFO|configuration_utils.py:660] 2023-02-10 21:23:00,119 >> loading configuration file config.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/config.json\n",
|
||
"[INFO|configuration_utils.py:712] 2023-02-10 21:23:00,120 >> Model config GPT2Config {\n",
|
||
" \"_name_or_path\": \"gpt2\",\n",
|
||
" \"activation_function\": \"gelu_new\",\n",
|
||
" \"architectures\": [\n",
|
||
" \"GPT2LMHeadModel\"\n",
|
||
" ],\n",
|
||
" \"attn_pdrop\": 0.1,\n",
|
||
" \"bos_token_id\": 50256,\n",
|
||
" \"embd_pdrop\": 0.1,\n",
|
||
" \"eos_token_id\": 50256,\n",
|
||
" \"initializer_range\": 0.02,\n",
|
||
" \"layer_norm_epsilon\": 1e-05,\n",
|
||
" \"model_type\": \"gpt2\",\n",
|
||
" \"n_ctx\": 1024,\n",
|
||
" \"n_embd\": 768,\n",
|
||
" \"n_head\": 12,\n",
|
||
" \"n_inner\": null,\n",
|
||
" \"n_layer\": 12,\n",
|
||
" \"n_positions\": 1024,\n",
|
||
" \"reorder_and_upcast_attn\": false,\n",
|
||
" \"resid_pdrop\": 0.1,\n",
|
||
" \"scale_attn_by_inverse_layer_idx\": false,\n",
|
||
" \"scale_attn_weights\": true,\n",
|
||
" \"summary_activation\": null,\n",
|
||
" \"summary_first_dropout\": 0.1,\n",
|
||
" \"summary_proj_to_labels\": true,\n",
|
||
" \"summary_type\": \"cls_index\",\n",
|
||
" \"summary_use_proj\": true,\n",
|
||
" \"task_specific_params\": {\n",
|
||
" \"text-generation\": {\n",
|
||
" \"do_sample\": true,\n",
|
||
" \"max_length\": 50\n",
|
||
" }\n",
|
||
" },\n",
|
||
" \"transformers_version\": \"4.26.1\",\n",
|
||
" \"use_cache\": true,\n",
|
||
" \"vocab_size\": 50257\n",
|
||
"}\n",
|
||
"\n",
|
||
"[INFO|tokenization_utils_base.py:1802] 2023-02-10 21:23:00,397 >> loading file vocab.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/vocab.json\n",
|
||
"[INFO|tokenization_utils_base.py:1802] 2023-02-10 21:23:00,397 >> loading file merges.txt from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/merges.txt\n",
|
||
"[INFO|tokenization_utils_base.py:1802] 2023-02-10 21:23:00,397 >> loading file tokenizer.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/tokenizer.json\n",
|
||
"[INFO|tokenization_utils_base.py:1802] 2023-02-10 21:23:00,397 >> loading file added_tokens.json from cache at None\n",
|
||
"[INFO|tokenization_utils_base.py:1802] 2023-02-10 21:23:00,397 >> loading file special_tokens_map.json from cache at None\n",
|
||
"[INFO|tokenization_utils_base.py:1802] 2023-02-10 21:23:00,397 >> loading file tokenizer_config.json from cache at None\n",
|
||
"[INFO|configuration_utils.py:660] 2023-02-10 21:23:00,397 >> loading configuration file config.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/config.json\n",
|
||
"[INFO|configuration_utils.py:712] 2023-02-10 21:23:00,398 >> Model config GPT2Config {\n",
|
||
" \"_name_or_path\": \"gpt2\",\n",
|
||
" \"activation_function\": \"gelu_new\",\n",
|
||
" \"architectures\": [\n",
|
||
" \"GPT2LMHeadModel\"\n",
|
||
" ],\n",
|
||
" \"attn_pdrop\": 0.1,\n",
|
||
" \"bos_token_id\": 50256,\n",
|
||
" \"embd_pdrop\": 0.1,\n",
|
||
" \"eos_token_id\": 50256,\n",
|
||
" \"initializer_range\": 0.02,\n",
|
||
" \"layer_norm_epsilon\": 1e-05,\n",
|
||
" \"model_type\": \"gpt2\",\n",
|
||
" \"n_ctx\": 1024,\n",
|
||
" \"n_embd\": 768,\n",
|
||
" \"n_head\": 12,\n",
|
||
" \"n_inner\": null,\n",
|
||
" \"n_layer\": 12,\n",
|
||
" \"n_positions\": 1024,\n",
|
||
" \"reorder_and_upcast_attn\": false,\n",
|
||
" \"resid_pdrop\": 0.1,\n",
|
||
" \"scale_attn_by_inverse_layer_idx\": false,\n",
|
||
" \"scale_attn_weights\": true,\n",
|
||
" \"summary_activation\": null,\n",
|
||
" \"summary_first_dropout\": 0.1,\n",
|
||
" \"summary_proj_to_labels\": true,\n",
|
||
" \"summary_type\": \"cls_index\",\n",
|
||
" \"summary_use_proj\": true,\n",
|
||
" \"task_specific_params\": {\n",
|
||
" \"text-generation\": {\n",
|
||
" \"do_sample\": true,\n",
|
||
" \"max_length\": 50\n",
|
||
" }\n",
|
||
" },\n",
|
||
" \"transformers_version\": \"4.26.1\",\n",
|
||
" \"use_cache\": true,\n",
|
||
" \"vocab_size\": 50257\n",
|
||
"}\n",
|
||
"\n",
|
||
"[INFO|modeling_utils.py:2275] 2023-02-10 21:23:00,491 >> loading weights file pytorch_model.bin from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/pytorch_model.bin\n",
|
||
"[INFO|modeling_utils.py:2857] 2023-02-10 21:23:01,899 >> All model checkpoint weights were used when initializing GPT2ForSequenceClassification.\n",
|
||
"\n",
|
||
"[WARNING|modeling_utils.py:2859] 2023-02-10 21:23:01,899 >> Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at gpt2 and are newly initialized: ['score.weight']\n",
|
||
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
|
||
"WARNING:datasets.arrow_dataset:Loading cached processed dataset at /content/.cache_training/json/default-58ab9a923ac72046/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-d1ffff8de8defc1a.arrow\n",
|
||
"Running tokenizer on dataset: 0% 0/2 [00:00<?, ?ba/s][ERROR|tokenization_utils_base.py:1042] 2023-02-10 21:23:04,511 >> Using pad_token, but it is not set yet.\n",
|
||
"INFO:__main__:Set PAD token to EOS: <|endoftext|>\n",
|
||
"INFO:datasets.arrow_dataset:Caching processed dataset at /content/.cache_training/json/default-58ab9a923ac72046/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-c4ceea5bc782c3e6.arrow\n",
|
||
"Running tokenizer on dataset: 100% 2/2 [00:00<00:00, 26.57ba/s]\n",
|
||
"WARNING:datasets.arrow_dataset:Loading cached processed dataset at /content/.cache_training/json/default-58ab9a923ac72046/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-4279d22576228078.arrow\n",
|
||
"INFO:__main__:Sample 912 of the training set: {'label': 2, 'text': 'i feel we need a little romantic boost in the relationship', 'input_ids': [72, 1254, 356, 761, 257, 1310, 14348, 5750, 287, 262, 2776, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.\n",
|
||
"INFO:__main__:Sample 204 of the training set: {'label': 1, 'text': 'i feel pretty mellow so far about whatever healing wounding process may be getting underway', 'input_ids': [72, 1254, 2495, 33748, 322, 523, 1290, 546, 4232, 11516, 40942, 1429, 743, 307, 1972, 17715, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.\n",
|
||
"INFO:__main__:Sample 2253 of the training set: {'label': 1, 'text': 'i feel ive answered those questions for her and shes pretty trusting for the most part', 'input_ids': [72, 1254, 220, 425, 9373, 883, 2683, 329, 607, 290, 673, 82, 2495, 33914, 329, 262, 749, 636, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.\n",
|
||
"[INFO|trainer.py:1972] 2023-02-10 21:23:07,962 >> Loading model from out/imdb-5k/gpt2/checkpoint-500.\n",
|
||
"[INFO|trainer.py:710] 2023-02-10 21:23:08,382 >> The following columns in the training set don't have a corresponding argument in `GPT2ForSequenceClassification.forward` and have been ignored: text. If text are not expected by `GPT2ForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
|
||
" warnings.warn(\n",
|
||
"[INFO|trainer.py:1650] 2023-02-10 21:23:09,160 >> ***** Running training *****\n",
|
||
"[INFO|trainer.py:1651] 2023-02-10 21:23:09,160 >> Num examples = 4999\n",
|
||
"[INFO|trainer.py:1652] 2023-02-10 21:23:09,160 >> Num Epochs = 5\n",
|
||
"[INFO|trainer.py:1653] 2023-02-10 21:23:09,160 >> Instantaneous batch size per device = 24\n",
|
||
"[INFO|trainer.py:1654] 2023-02-10 21:23:09,160 >> Total train batch size (w. parallel, distributed & accumulation) = 24\n",
|
||
"[INFO|trainer.py:1655] 2023-02-10 21:23:09,160 >> Gradient Accumulation steps = 1\n",
|
||
"[INFO|trainer.py:1656] 2023-02-10 21:23:09,160 >> Total optimization steps = 1045\n",
|
||
"[INFO|trainer.py:1657] 2023-02-10 21:23:09,161 >> Number of trainable parameters = 124444416\n",
|
||
"[INFO|trainer.py:1679] 2023-02-10 21:23:09,161 >> Continuing training from checkpoint, will skip to saved global_step\n",
|
||
"[INFO|trainer.py:1680] 2023-02-10 21:23:09,161 >> Continuing training from epoch 2\n",
|
||
"[INFO|trainer.py:1681] 2023-02-10 21:23:09,161 >> Continuing training from global step 500\n",
|
||
"[INFO|trainer.py:1683] 2023-02-10 21:23:09,161 >> Will skip the first 2 epochs then the first 82 batches in the first epoch. If this takes a lot of time, you can add the `--ignore_data_skip` flag to your launch command, but you will resume the training on data already seen by your model.\n",
|
||
"Skipping the first batches: 0% 0/82 [00:00<?, ?it/s]\n",
|
||
"Skipping the first batches: 100% 82/82 [00:00<00:00, 209.80it/s]\n",
|
||
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" 71% 738/1045 [00:36<00:45, 6.81it/s]\u001b[A\n",
|
||
" 71% 739/1045 [00:36<00:45, 6.80it/s]\u001b[A\n",
|
||
" 71% 740/1045 [00:36<00:44, 6.80it/s]\u001b[A\n",
|
||
" 71% 741/1045 [00:36<00:44, 6.81it/s]\u001b[A\n",
|
||
" 71% 742/1045 [00:36<00:44, 6.81it/s]\u001b[A\n",
|
||
" 71% 743/1045 [00:36<00:44, 6.82it/s]\u001b[A\n",
|
||
" 71% 744/1045 [00:37<00:44, 6.81it/s]\u001b[A\n",
|
||
" 71% 745/1045 [00:37<00:44, 6.81it/s]\u001b[A\n",
|
||
" 71% 746/1045 [00:37<00:43, 6.82it/s]\u001b[A\n",
|
||
" 71% 747/1045 [00:37<00:43, 6.81it/s]\u001b[A\n",
|
||
" 72% 748/1045 [00:37<00:43, 6.82it/s]\u001b[A\n",
|
||
" 72% 749/1045 [00:37<00:43, 6.83it/s]\u001b[A\n",
|
||
" 72% 750/1045 [00:38<00:43, 6.82it/s]\u001b[A\n",
|
||
" 72% 751/1045 [00:38<00:43, 6.82it/s]\u001b[A\n",
|
||
" 72% 752/1045 [00:38<00:42, 6.81it/s]\u001b[A\n",
|
||
" 72% 753/1045 [00:38<00:42, 6.81it/s]\u001b[A\n",
|
||
" 72% 754/1045 [00:38<00:42, 6.81it/s]\u001b[A\n",
|
||
" 72% 755/1045 [00:38<00:42, 6.81it/s]\u001b[A\n",
|
||
" 72% 756/1045 [00:38<00:42, 6.81it/s]\u001b[A\n",
|
||
" 72% 757/1045 [00:39<00:42, 6.80it/s]\u001b[A\n",
|
||
" 73% 758/1045 [00:39<00:42, 6.80it/s]\u001b[A\n",
|
||
" 73% 759/1045 [00:39<00:42, 6.81it/s]\u001b[A\n",
|
||
" 73% 760/1045 [00:39<00:41, 6.81it/s]\u001b[A\n",
|
||
" 73% 761/1045 [00:39<00:41, 6.78it/s]\u001b[A\n",
|
||
" 73% 762/1045 [00:39<00:41, 6.79it/s]\u001b[A\n",
|
||
" 73% 763/1045 [00:39<00:41, 6.80it/s]\u001b[A\n",
|
||
" 73% 764/1045 [00:40<00:41, 6.81it/s]\u001b[A\n",
|
||
" 73% 765/1045 [00:40<00:41, 6.80it/s]\u001b[A\n",
|
||
" 73% 766/1045 [00:40<00:40, 6.81it/s]\u001b[A\n",
|
||
" 73% 767/1045 [00:40<00:40, 6.81it/s]\u001b[A\n",
|
||
" 73% 768/1045 [00:40<00:40, 6.82it/s]\u001b[A\n",
|
||
" 74% 769/1045 [00:40<00:41, 6.70it/s]\u001b[A\n",
|
||
" 74% 770/1045 [00:40<00:40, 6.72it/s]\u001b[A\n",
|
||
" 74% 771/1045 [00:41<00:40, 6.75it/s]\u001b[A\n",
|
||
" 74% 772/1045 [00:41<00:40, 6.75it/s]\u001b[A\n",
|
||
" 74% 773/1045 [00:41<00:40, 6.76it/s]\u001b[A\n",
|
||
" 74% 774/1045 [00:41<00:40, 6.77it/s]\u001b[A\n",
|
||
" 74% 775/1045 [00:41<00:39, 6.78it/s]\u001b[A\n",
|
||
" 74% 776/1045 [00:41<00:39, 6.77it/s]\u001b[A\n",
|
||
" 74% 777/1045 [00:42<00:39, 6.77it/s]\u001b[A\n",
|
||
" 74% 778/1045 [00:42<00:39, 6.78it/s]\u001b[A\n",
|
||
" 75% 779/1045 [00:42<00:39, 6.78it/s]\u001b[A\n",
|
||
" 75% 780/1045 [00:42<00:39, 6.78it/s]\u001b[A\n",
|
||
" 75% 781/1045 [00:42<00:38, 6.78it/s]\u001b[A\n",
|
||
" 75% 782/1045 [00:42<00:38, 6.77it/s]\u001b[A\n",
|
||
" 75% 783/1045 [00:42<00:38, 6.77it/s]\u001b[A\n",
|
||
" 75% 784/1045 [00:43<00:38, 6.76it/s]\u001b[A\n",
|
||
" 75% 785/1045 [00:43<00:38, 6.77it/s]\u001b[A\n",
|
||
" 75% 786/1045 [00:43<00:38, 6.77it/s]\u001b[A\n",
|
||
" 75% 787/1045 [00:43<00:38, 6.77it/s]\u001b[A\n",
|
||
" 75% 788/1045 [00:43<00:37, 6.78it/s]\u001b[A\n",
|
||
" 76% 789/1045 [00:43<00:37, 6.77it/s]\u001b[A\n",
|
||
" 76% 790/1045 [00:43<00:37, 6.78it/s]\u001b[A\n",
|
||
" 76% 791/1045 [00:44<00:37, 6.78it/s]\u001b[A\n",
|
||
" 76% 792/1045 [00:44<00:37, 6.79it/s]\u001b[A\n",
|
||
" 76% 793/1045 [00:44<00:37, 6.80it/s]\u001b[A\n",
|
||
" 76% 794/1045 [00:44<00:36, 6.81it/s]\u001b[A\n",
|
||
" 76% 795/1045 [00:44<00:36, 6.81it/s]\u001b[A\n",
|
||
" 76% 796/1045 [00:44<00:36, 6.82it/s]\u001b[A\n",
|
||
" 76% 797/1045 [00:44<00:36, 6.81it/s]\u001b[A\n",
|
||
" 76% 798/1045 [00:45<00:36, 6.81it/s]\u001b[A\n",
|
||
" 76% 799/1045 [00:45<00:36, 6.80it/s]\u001b[A\n",
|
||
" 77% 800/1045 [00:45<00:35, 6.81it/s]\u001b[A\n",
|
||
" 77% 801/1045 [00:45<00:35, 6.79it/s]\u001b[A\n",
|
||
" 77% 802/1045 [00:45<00:35, 6.79it/s]\u001b[A\n",
|
||
" 77% 803/1045 [00:45<00:35, 6.80it/s]\u001b[A\n",
|
||
" 77% 804/1045 [00:45<00:35, 6.79it/s]\u001b[A\n",
|
||
" 77% 805/1045 [00:46<00:35, 6.80it/s]\u001b[A\n",
|
||
" 77% 806/1045 [00:46<00:35, 6.81it/s]\u001b[A\n",
|
||
" 77% 807/1045 [00:46<00:34, 6.80it/s]\u001b[A\n",
|
||
" 77% 808/1045 [00:46<00:34, 6.80it/s]\u001b[A\n",
|
||
" 77% 809/1045 [00:46<00:34, 6.80it/s]\u001b[A\n",
|
||
" 78% 810/1045 [00:46<00:34, 6.79it/s]\u001b[A\n",
|
||
" 78% 811/1045 [00:47<00:34, 6.79it/s]\u001b[A\n",
|
||
" 78% 812/1045 [00:47<00:34, 6.78it/s]\u001b[A\n",
|
||
" 78% 813/1045 [00:47<00:34, 6.79it/s]\u001b[A\n",
|
||
" 78% 814/1045 [00:47<00:34, 6.73it/s]\u001b[A\n",
|
||
" 78% 815/1045 [00:47<00:34, 6.75it/s]\u001b[A\n",
|
||
" 78% 816/1045 [00:47<00:33, 6.76it/s]\u001b[A\n",
|
||
" 78% 817/1045 [00:47<00:33, 6.76it/s]\u001b[A\n",
|
||
" 78% 818/1045 [00:48<00:33, 6.78it/s]\u001b[A\n",
|
||
" 78% 819/1045 [00:48<00:33, 6.78it/s]\u001b[A\n",
|
||
" 78% 820/1045 [00:48<00:33, 6.79it/s]\u001b[A\n",
|
||
" 79% 821/1045 [00:48<00:32, 6.80it/s]\u001b[A\n",
|
||
" 79% 822/1045 [00:48<00:32, 6.80it/s]\u001b[A\n",
|
||
" 79% 823/1045 [00:48<00:32, 6.81it/s]\u001b[A\n",
|
||
" 79% 824/1045 [00:48<00:32, 6.81it/s]\u001b[A\n",
|
||
" 79% 825/1045 [00:49<00:32, 6.82it/s]\u001b[A\n",
|
||
" 79% 826/1045 [00:49<00:32, 6.81it/s]\u001b[A\n",
|
||
" 79% 827/1045 [00:49<00:31, 6.82it/s]\u001b[A\n",
|
||
" 79% 828/1045 [00:49<00:31, 6.81it/s]\u001b[A\n",
|
||
" 79% 829/1045 [00:49<00:31, 6.79it/s]\u001b[A\n",
|
||
" 79% 830/1045 [00:49<00:31, 6.80it/s]\u001b[A\n",
|
||
" 80% 831/1045 [00:49<00:31, 6.77it/s]\u001b[A\n",
|
||
" 80% 832/1045 [00:50<00:31, 6.77it/s]\u001b[A\n",
|
||
" 80% 833/1045 [00:50<00:31, 6.79it/s]\u001b[A\n",
|
||
" 80% 834/1045 [00:50<00:31, 6.79it/s]\u001b[A\n",
|
||
" 80% 835/1045 [00:50<00:30, 6.80it/s]\u001b[A\n",
|
||
" 80% 837/1045 [00:50<00:26, 7.79it/s]\u001b[A\n",
|
||
" 80% 838/1045 [00:50<00:27, 7.52it/s]\u001b[A\n",
|
||
" 80% 839/1045 [00:51<00:28, 7.32it/s]\u001b[A\n",
|
||
" 80% 840/1045 [00:51<00:28, 7.17it/s]\u001b[A\n",
|
||
" 80% 841/1045 [00:51<00:28, 7.07it/s]\u001b[A\n",
|
||
" 81% 842/1045 [00:51<00:29, 6.98it/s]\u001b[A\n",
|
||
" 81% 843/1045 [00:51<00:29, 6.94it/s]\u001b[A\n",
|
||
" 81% 844/1045 [00:51<00:29, 6.91it/s]\u001b[A\n",
|
||
" 81% 845/1045 [00:51<00:29, 6.89it/s]\u001b[A\n",
|
||
" 81% 846/1045 [00:52<00:28, 6.86it/s]\u001b[A\n",
|
||
" 81% 847/1045 [00:52<00:28, 6.86it/s]\u001b[A\n",
|
||
" 81% 848/1045 [00:52<00:28, 6.85it/s]\u001b[A\n",
|
||
" 81% 849/1045 [00:52<00:28, 6.85it/s]\u001b[A\n",
|
||
" 81% 850/1045 [00:52<00:28, 6.83it/s]\u001b[A\n",
|
||
" 81% 851/1045 [00:52<00:28, 6.81it/s]\u001b[A\n",
|
||
" 82% 852/1045 [00:52<00:28, 6.80it/s]\u001b[A\n",
|
||
" 82% 853/1045 [00:53<00:28, 6.80it/s]\u001b[A\n",
|
||
" 82% 854/1045 [00:53<00:28, 6.80it/s]\u001b[A\n",
|
||
" 82% 855/1045 [00:53<00:27, 6.80it/s]\u001b[A\n",
|
||
" 82% 856/1045 [00:53<00:27, 6.81it/s]\u001b[A\n",
|
||
" 82% 857/1045 [00:53<00:27, 6.80it/s]\u001b[A\n",
|
||
" 82% 858/1045 [00:53<00:27, 6.81it/s]\u001b[A\n",
|
||
" 82% 859/1045 [00:53<00:27, 6.79it/s]\u001b[A\n",
|
||
" 82% 860/1045 [00:54<00:27, 6.80it/s]\u001b[A\n",
|
||
" 82% 861/1045 [00:54<00:27, 6.80it/s]\u001b[A\n",
|
||
" 82% 862/1045 [00:54<00:26, 6.79it/s]\u001b[A\n",
|
||
" 83% 863/1045 [00:54<00:26, 6.79it/s]\u001b[A\n",
|
||
" 83% 864/1045 [00:54<00:26, 6.78it/s]\u001b[A\n",
|
||
" 83% 865/1045 [00:54<00:26, 6.80it/s]\u001b[A\n",
|
||
" 83% 866/1045 [00:55<00:26, 6.80it/s]\u001b[A\n",
|
||
" 83% 867/1045 [00:55<00:26, 6.80it/s]\u001b[A\n",
|
||
" 83% 868/1045 [00:55<00:25, 6.81it/s]\u001b[A\n",
|
||
" 83% 869/1045 [00:55<00:25, 6.82it/s]\u001b[A\n",
|
||
" 83% 870/1045 [00:55<00:25, 6.79it/s]\u001b[A\n",
|
||
" 83% 871/1045 [00:55<00:25, 6.80it/s]\u001b[A\n",
|
||
" 83% 872/1045 [00:55<00:25, 6.79it/s]\u001b[A\n",
|
||
" 84% 873/1045 [00:56<00:25, 6.80it/s]\u001b[A\n",
|
||
" 84% 874/1045 [00:56<00:25, 6.79it/s]\u001b[A\n",
|
||
" 84% 875/1045 [00:56<00:25, 6.80it/s]\u001b[A\n",
|
||
" 84% 876/1045 [00:56<00:24, 6.80it/s]\u001b[A\n",
|
||
" 84% 877/1045 [00:56<00:24, 6.80it/s]\u001b[A\n",
|
||
" 84% 878/1045 [00:56<00:24, 6.79it/s]\u001b[A\n",
|
||
" 84% 879/1045 [00:56<00:24, 6.79it/s]\u001b[A\n",
|
||
" 84% 880/1045 [00:57<00:24, 6.80it/s]\u001b[A\n",
|
||
" 84% 881/1045 [00:57<00:24, 6.80it/s]\u001b[A\n",
|
||
" 84% 882/1045 [00:57<00:23, 6.82it/s]\u001b[A\n",
|
||
" 84% 883/1045 [00:57<00:23, 6.82it/s]\u001b[A\n",
|
||
" 85% 884/1045 [00:57<00:23, 6.82it/s]\u001b[A\n",
|
||
" 85% 885/1045 [00:57<00:23, 6.80it/s]\u001b[A\n",
|
||
" 85% 886/1045 [00:57<00:23, 6.81it/s]\u001b[A\n",
|
||
" 85% 887/1045 [00:58<00:23, 6.79it/s]\u001b[A\n",
|
||
" 85% 888/1045 [00:58<00:23, 6.80it/s]\u001b[A\n",
|
||
" 85% 889/1045 [00:58<00:23, 6.75it/s]\u001b[A\n",
|
||
" 85% 890/1045 [00:58<00:22, 6.75it/s]\u001b[A\n",
|
||
" 85% 891/1045 [00:58<00:22, 6.75it/s]\u001b[A\n",
|
||
" 85% 892/1045 [00:58<00:22, 6.77it/s]\u001b[A\n",
|
||
" 85% 893/1045 [00:58<00:22, 6.78it/s]\u001b[A\n",
|
||
" 86% 894/1045 [00:59<00:22, 6.74it/s]\u001b[A\n",
|
||
" 86% 895/1045 [00:59<00:22, 6.76it/s]\u001b[A\n",
|
||
" 86% 896/1045 [00:59<00:21, 6.78it/s]\u001b[A\n",
|
||
" 86% 897/1045 [00:59<00:21, 6.80it/s]\u001b[A\n",
|
||
" 86% 898/1045 [00:59<00:21, 6.79it/s]\u001b[A\n",
|
||
" 86% 899/1045 [00:59<00:21, 6.80it/s]\u001b[A\n",
|
||
" 86% 900/1045 [01:00<00:21, 6.80it/s]\u001b[A\n",
|
||
" 86% 901/1045 [01:00<00:21, 6.80it/s]\u001b[A\n",
|
||
" 86% 902/1045 [01:00<00:21, 6.80it/s]\u001b[A\n",
|
||
" 86% 903/1045 [01:00<00:20, 6.81it/s]\u001b[A\n",
|
||
" 87% 904/1045 [01:00<00:20, 6.80it/s]\u001b[A\n",
|
||
" 87% 905/1045 [01:00<00:20, 6.78it/s]\u001b[A\n",
|
||
" 87% 906/1045 [01:00<00:20, 6.79it/s]\u001b[A\n",
|
||
" 87% 907/1045 [01:01<00:20, 6.80it/s]\u001b[A\n",
|
||
" 87% 908/1045 [01:01<00:20, 6.80it/s]\u001b[A\n",
|
||
" 87% 909/1045 [01:01<00:20, 6.76it/s]\u001b[A\n",
|
||
" 87% 910/1045 [01:01<00:19, 6.78it/s]\u001b[A\n",
|
||
" 87% 911/1045 [01:01<00:19, 6.79it/s]\u001b[A\n",
|
||
" 87% 912/1045 [01:01<00:19, 6.80it/s]\u001b[A\n",
|
||
" 87% 913/1045 [01:01<00:19, 6.80it/s]\u001b[A\n",
|
||
" 87% 914/1045 [01:02<00:19, 6.81it/s]\u001b[A\n",
|
||
" 88% 915/1045 [01:02<00:19, 6.81it/s]\u001b[A\n",
|
||
" 88% 916/1045 [01:02<00:18, 6.81it/s]\u001b[A\n",
|
||
" 88% 917/1045 [01:02<00:18, 6.81it/s]\u001b[A\n",
|
||
" 88% 918/1045 [01:02<00:18, 6.79it/s]\u001b[A\n",
|
||
" 88% 919/1045 [01:02<00:18, 6.80it/s]\u001b[A\n",
|
||
" 88% 920/1045 [01:02<00:18, 6.81it/s]\u001b[A\n",
|
||
" 88% 921/1045 [01:03<00:18, 6.81it/s]\u001b[A\n",
|
||
" 88% 922/1045 [01:03<00:18, 6.81it/s]\u001b[A\n",
|
||
" 88% 923/1045 [01:03<00:17, 6.82it/s]\u001b[A\n",
|
||
" 88% 924/1045 [01:03<00:17, 6.82it/s]\u001b[A\n",
|
||
" 89% 925/1045 [01:03<00:17, 6.82it/s]\u001b[A\n",
|
||
" 89% 926/1045 [01:03<00:17, 6.81it/s]\u001b[A\n",
|
||
" 89% 927/1045 [01:03<00:17, 6.82it/s]\u001b[A\n",
|
||
" 89% 928/1045 [01:04<00:17, 6.82it/s]\u001b[A\n",
|
||
" 89% 929/1045 [01:04<00:17, 6.80it/s]\u001b[A\n",
|
||
" 89% 930/1045 [01:04<00:16, 6.81it/s]\u001b[A\n",
|
||
" 89% 931/1045 [01:04<00:16, 6.82it/s]\u001b[A\n",
|
||
" 89% 932/1045 [01:04<00:16, 6.82it/s]\u001b[A\n",
|
||
" 89% 933/1045 [01:04<00:16, 6.71it/s]\u001b[A\n",
|
||
" 89% 934/1045 [01:05<00:16, 6.75it/s]\u001b[A\n",
|
||
" 89% 935/1045 [01:05<00:16, 6.77it/s]\u001b[A\n",
|
||
" 90% 936/1045 [01:05<00:16, 6.79it/s]\u001b[A\n",
|
||
" 90% 937/1045 [01:05<00:15, 6.78it/s]\u001b[A\n",
|
||
" 90% 938/1045 [01:05<00:15, 6.77it/s]\u001b[A\n",
|
||
" 90% 939/1045 [01:05<00:15, 6.79it/s]\u001b[A\n",
|
||
" 90% 940/1045 [01:05<00:15, 6.80it/s]\u001b[A\n",
|
||
" 90% 941/1045 [01:06<00:15, 6.80it/s]\u001b[A\n",
|
||
" 90% 942/1045 [01:06<00:15, 6.81it/s]\u001b[A\n",
|
||
" 90% 943/1045 [01:06<00:15, 6.71it/s]\u001b[A\n",
|
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||
"\u001b[A{'loss': 0.2421, 'learning_rate': 8.612440191387561e-07, 'epoch': 4.78}\n",
|
||
"\n",
|
||
" 96% 1000/1045 [01:14<00:06, 6.83it/s]\u001b[A[INFO|trainer.py:2709] 2023-02-10 21:24:23,900 >> Saving model checkpoint to out/imdb-5k/gpt2/checkpoint-1000\n",
|
||
"[INFO|configuration_utils.py:453] 2023-02-10 21:24:23,901 >> Configuration saved in out/imdb-5k/gpt2/checkpoint-1000/config.json\n",
|
||
"[INFO|modeling_utils.py:1704] 2023-02-10 21:24:24,615 >> Model weights saved in out/imdb-5k/gpt2/checkpoint-1000/pytorch_model.bin\n",
|
||
"[INFO|tokenization_utils_base.py:2160] 2023-02-10 21:24:24,616 >> tokenizer config file saved in out/imdb-5k/gpt2/checkpoint-1000/tokenizer_config.json\n",
|
||
"[INFO|tokenization_utils_base.py:2167] 2023-02-10 21:24:24,616 >> Special tokens file saved in out/imdb-5k/gpt2/checkpoint-1000/special_tokens_map.json\n",
|
||
"\n",
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||
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|
||
"\n",
|
||
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\u001b[A{'train_runtime': 83.5689, 'train_samples_per_second': 299.094, 'train_steps_per_second': 12.505, 'train_loss': 0.1263527454942037, 'epoch': 5.0}\n",
|
||
"\n",
|
||
"100% 1045/1045 [01:23<00:00, 12.51it/s]\n",
|
||
"[INFO|trainer.py:2709] 2023-02-10 21:24:32,732 >> Saving model checkpoint to out/imdb-5k/gpt2\n",
|
||
"[INFO|configuration_utils.py:453] 2023-02-10 21:24:32,733 >> Configuration saved in out/imdb-5k/gpt2/config.json\n",
|
||
"[INFO|modeling_utils.py:1704] 2023-02-10 21:24:33,797 >> Model weights saved in out/imdb-5k/gpt2/pytorch_model.bin\n",
|
||
"[INFO|tokenization_utils_base.py:2160] 2023-02-10 21:24:33,797 >> tokenizer config file saved in out/imdb-5k/gpt2/tokenizer_config.json\n",
|
||
"[INFO|tokenization_utils_base.py:2167] 2023-02-10 21:24:33,798 >> Special tokens file saved in out/imdb-5k/gpt2/special_tokens_map.json\n",
|
||
"***** train metrics *****\n",
|
||
" epoch = 5.0\n",
|
||
" train_loss = 0.1264\n",
|
||
" train_runtime = 0:01:23.56\n",
|
||
" train_samples = 4999\n",
|
||
" train_samples_per_second = 299.094\n",
|
||
" train_steps_per_second = 12.505\n",
|
||
"INFO:__main__:*** Evaluate ***\n",
|
||
"[INFO|trainer.py:710] 2023-02-10 21:24:33,908 >> The following columns in the evaluation set don't have a corresponding argument in `GPT2ForSequenceClassification.forward` and have been ignored: text. If text are not expected by `GPT2ForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"[INFO|trainer.py:2964] 2023-02-10 21:24:33,910 >> ***** Running Evaluation *****\n",
|
||
"[INFO|trainer.py:2966] 2023-02-10 21:24:33,910 >> Num examples = 1274\n",
|
||
"[INFO|trainer.py:2969] 2023-02-10 21:24:33,910 >> Batch size = 24\n",
|
||
"100% 54/54 [00:02<00:00, 21.70it/s]\n",
|
||
"***** eval metrics *****\n",
|
||
" epoch = 5.0\n",
|
||
" eval_accuracy = 0.9278\n",
|
||
" eval_loss = 0.1801\n",
|
||
" eval_runtime = 0:00:02.53\n",
|
||
" eval_samples = 1274\n",
|
||
" eval_samples_per_second = 502.583\n",
|
||
" eval_steps_per_second = 21.303\n",
|
||
"INFO:__main__:*** Predict ***\n",
|
||
"[INFO|trainer.py:710] 2023-02-10 21:24:36,448 >> The following columns in the test set don't have a corresponding argument in `GPT2ForSequenceClassification.forward` and have been ignored: text. If text are not expected by `GPT2ForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"[INFO|trainer.py:2964] 2023-02-10 21:24:36,449 >> ***** Running Prediction *****\n",
|
||
"[INFO|trainer.py:2966] 2023-02-10 21:24:36,450 >> Num examples = 1278\n",
|
||
"[INFO|trainer.py:2969] 2023-02-10 21:24:36,450 >> Batch size = 24\n",
|
||
"100% 54/54 [00:02<00:00, 22.06it/s]\n",
|
||
"INFO:__main__:***** Predict results None *****\n",
|
||
"[INFO|modelcard.py:449] 2023-02-10 21:24:39,131 >> Dropping the following result as it does not have all the necessary fields:\n",
|
||
"{'task': {'name': 'Text Classification', 'type': 'text-classification'}, 'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.9277864694595337}]}\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000,
|
||
"referenced_widgets": [
|
||
"12a3b4013c4741f194e8f143839f59d2",
|
||
"b815be0dbca3430c94707ba7330dcfe9",
|
||
"9de0834718944d45bff2d357cd47e725",
|
||
"44647a9ae99e40058538cbca96bcbf5c",
|
||
"f8134c2d5a18474695a19f3bb8e9bb6d",
|
||
"d20c11c53061466990453a1a71a9a480",
|
||
"0631843dd59d4eb5a93d91ae4156dd47",
|
||
"1c82d38ce7324413a3145a39257e3147",
|
||
"eecc3314f4d8426f94f3f4875c4b5add",
|
||
"e68de1b49d7b47aebd009ded3c38c05c",
|
||
"ff7eb857645f463e8d7f0d95dfd970b2",
|
||
"5e0e76acace04ba58c272928ad50f2e5",
|
||
"8e5e234d92564011a694845143e67f89",
|
||
"f5261966a4c840f296891c717f5a4d4c",
|
||
"a0b29f1871ae43e0836c2f18194a5378",
|
||
"d33712e2db6347b8a3588d00cbb0d439",
|
||
"122531057b694b77a3cdcb0ce785d671",
|
||
"b2d2116bcf224757a6b5ca8cc1937ab8",
|
||
"fc01afd864ad4b07a0fd4070af881352",
|
||
"dd9c156436f74072b1685a605a8f6c64",
|
||
"5a80f201f3f043dc906aff76ff52aec8",
|
||
"59a0c3a926744e2a9546ff2d2ae6ef38",
|
||
"80906117603f4dc2a0b470ce05540a5c",
|
||
"7f20bca4963841a0a6067e3c21daf7a6",
|
||
"f3925714cbf84692b78ed9f37d9b5df1",
|
||
"87a267e672aa4b90a7a668e568c2ffa0",
|
||
"fc60868bdb324f2ebe1d5eed30f16cd7",
|
||
"3b95c0faa19241d7b1fbba60ab965c8f",
|
||
"3a8212d0ba2442cc9823b4a42804c6d2",
|
||
"154b47706d124d0e81ba1df333458182",
|
||
"a25fd7a31239465ca4f20f28c9c4bb37",
|
||
"1588a76df2bb44ccabc155b53dbe35dd",
|
||
"c6cf46d05dd24c4ca669c265de967207",
|
||
"095510cdbd9544a1af9bc2e34115b847",
|
||
"462b7882331b4872bb5396824383fe25",
|
||
"a947489a34734e458cd6e37b63901291",
|
||
"d20b1cfd811747aba8c6d88c54e64433",
|
||
"fd8a71b28b5f41b7b0e04bd61093af47",
|
||
"1ca769a9bf004fc881811f5aee9a0a9b",
|
||
"334fcdcfbc124bdcbb6692845847c438",
|
||
"e3a06ccb471a45798db9896ee48f2b73",
|
||
"e46145a44b6f4e7bba5d47db588da21e",
|
||
"752eb2a35c564df9a4e73d86ea79d75f",
|
||
"40285cae29b4441788d1c3f1461a6c1a",
|
||
"cca3a213ff5a4623900cf9aa2c604216",
|
||
"dab69946f654460dbb49f98ac4c37edf",
|
||
"5851233b98924145ac191f52e4c2eb30",
|
||
"820b5b66baef4fffb78b73a2a9c9c9df",
|
||
"a34a21e2f3894beba3b3cb19b808944b",
|
||
"40d9079ec91044d9affb8d4e1231e493",
|
||
"5424529bd84c4530ab040375a450a5b2",
|
||
"270eda40069a4f5fa2b4ac9267ef0fc4",
|
||
"7b78620460ec45b09f29354e7f5da6b9",
|
||
"b783163b6c3f41c19ccf8d2d40470602",
|
||
"294d2b8ee5f841f8b966a91222f9a0e6",
|
||
"f3b75447c8d74249ae0aae431ed729be",
|
||
"36d3ad0a8cba41c8bd0d57cd60868550",
|
||
"ebb345f933524ec6910277d846c07032",
|
||
"be80e311422c4d48940a3a7343be1fcc",
|
||
"04591a88c1074919b787ad4ee6654fce",
|
||
"529c3d48f9964ea693842b9403c566bf",
|
||
"0b42880fee8b42e097266243b3bbead2",
|
||
"512cf46964d648b99fe02ff7305b847d",
|
||
"e7a979d652a84c0584b2b1134a04a80d",
|
||
"e5e4202270f848b9bfcae05782192e23",
|
||
"4f083705a43c49f29b51153c20795142",
|
||
"fb7ceb4de21647a2a652763041d36dfd",
|
||
"b41c9a9b3cd74797a4e5b49a853275a4",
|
||
"abbc127e208444fa9cf3b75dcda3b548",
|
||
"679256244c5e4e7998a6b2cf5c9e900d",
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||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
" 0%| | 0/1 [00:00<?, ?ba/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "4146f4034fdc41a08b1bfe6304f3f2a6"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Using cuda_amp half precision backend\n",
|
||
"The following columns in the training set don't have a corresponding argument in `GPT2ForSequenceClassification.forward` and have been ignored: text. If text are not expected by `GPT2ForSequenceClassification.forward`, you can safely ignore this message.\n",
|
||
"/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
|
||
" warnings.warn(\n",
|
||
"***** Running training *****\n",
|
||
" Num examples = 16000\n",
|
||
" Num Epochs = 3\n",
|
||
" Instantaneous batch size per device = 8\n",
|
||
" Total train batch size (w. parallel, distributed & accumulation) = 128\n",
|
||
" Gradient Accumulation steps = 16\n",
|
||
" Total optimization steps = 375\n",
|
||
" Number of trainable parameters = 124441344\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "error",
|
||
"ename": "RuntimeError",
|
||
"evalue": "ignored",
|
||
"traceback": [
|
||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
|
||
"\u001b[0;32m<ipython-input-2-c792703afcef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 66\u001b[0m )\n\u001b[1;32m 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 69\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0msum_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1541\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inner_training_loop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_find_batch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1542\u001b[0m )\n\u001b[0;32m-> 1543\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 1544\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1545\u001b[0m \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 1789\u001b[0m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1790\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1791\u001b[0;31m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1792\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1793\u001b[0m if (\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m 2537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2538\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2539\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2541\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_gpu\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m 2569\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2570\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2571\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2572\u001b[0m \u001b[0;31m# Save past state if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2573\u001b[0m \u001b[0;31m# TODO: this needs to be fixed and made cleaner later.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1192\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1193\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1195\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1196\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1368\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_return_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1370\u001b[0;31m transformer_outputs = self.transformer(\n\u001b[0m\u001b[1;32m 1371\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1372\u001b[0m \u001b[0mpast_key_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpast_key_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1192\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1193\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1195\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1196\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 885\u001b[0m )\n\u001b[1;32m 886\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 887\u001b[0;31m outputs = block(\n\u001b[0m\u001b[1;32m 888\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 889\u001b[0m \u001b[0mlayer_past\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayer_past\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1192\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1193\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1195\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1196\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0mresidual\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mln_1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m attn_outputs = self.attn(\n\u001b[0m\u001b[1;32m 389\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0mlayer_past\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayer_past\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1192\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1193\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1195\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1196\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0mattention_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder_attention_mask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 310\u001b[0;31m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_attn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 311\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split_heads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_heads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1192\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1193\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1195\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1196\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/pytorch_utils.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0msize_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;31mRuntimeError\u001b[0m: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling `cublasCreate(handle)`"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from transformers import GPT2Config, GPT2Tokenizer, GPT2Model, Trainer, TrainingArguments, GPT2ForSequenceClassification\n",
|
||
"from datasets import load_dataset\n",
|
||
"import torch\n",
|
||
"from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
|
||
"\n",
|
||
"config=GPT2Config(vocab_size=2048, return_token_type_ids=False)\n",
|
||
"tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
|
||
"tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
|
||
"\n",
|
||
"model = GPT2ForSequenceClassification.from_pretrained('gpt2')\n",
|
||
"\n",
|
||
"def tokenization(batched_text):\n",
|
||
" return tokenizer(batched_text['text'], return_tensors='pt', padding=True)\n",
|
||
"\n",
|
||
"def compute_metrics(pred):\n",
|
||
" labels = pred.label_ids\n",
|
||
" preds = pred.predictions.argmax(-1)\n",
|
||
" precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='micro')\n",
|
||
" acc = accuracy_score(labels, preds)\n",
|
||
" return {\n",
|
||
" 'accuracy': acc,\n",
|
||
" 'f1': f1,\n",
|
||
" 'precision': precision,\n",
|
||
" 'recall': recall\n",
|
||
" }\n",
|
||
"\n",
|
||
"\n",
|
||
"dataset = load_dataset(\"emotion\")\n",
|
||
"\n",
|
||
"train_data= dataset[\"train\"]\n",
|
||
"test_data = dataset[\"test\"]\n",
|
||
"eval_data = dataset[\"validation\"]\n",
|
||
"\n",
|
||
"train_data = train_data.map(tokenization, batched=True, batch_size=len(train_data))\n",
|
||
"eval_data = eval_data.map(tokenization, batched=True, batch_size=len(eval_data))\n",
|
||
"\n",
|
||
"train_data.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])\n",
|
||
"eval_data.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])\n",
|
||
"\n",
|
||
"\n",
|
||
"training_args = TrainingArguments(\n",
|
||
" output_dir=\"./output\",\n",
|
||
" num_train_epochs=3,\n",
|
||
" per_device_train_batch_size = 8,\n",
|
||
" gradient_accumulation_steps = 16, \n",
|
||
" per_device_eval_batch_size= 8,\n",
|
||
" evaluation_strategy = \"epoch\",\n",
|
||
" save_strategy = \"epoch\",\n",
|
||
" disable_tqdm = False, \n",
|
||
" load_best_model_at_end=True,\n",
|
||
" warmup_steps=10,\n",
|
||
" weight_decay=0.01,\n",
|
||
" logging_steps = 4,\n",
|
||
" fp16 = True,\n",
|
||
" dataloader_num_workers = 2,\n",
|
||
" run_name = 'gpt-2-classification'\n",
|
||
")\n",
|
||
"\n",
|
||
"trainer = Trainer(\n",
|
||
" model=model,\n",
|
||
" args=training_args,\n",
|
||
" compute_metrics=compute_metrics,\n",
|
||
" train_dataset=train_data,\n",
|
||
" eval_dataset=eval_data,\n",
|
||
"\n",
|
||
")\n",
|
||
"\n",
|
||
"trainer.train()\n",
|
||
"i = 0\n",
|
||
"sum_preds = 0\n",
|
||
"model = model.to('cpu')\n",
|
||
"for line in test_data:\n",
|
||
"\n",
|
||
" inputs = tokenizer(line.get('text'), return_tensors=\"pt\")\n",
|
||
" labels = torch.tensor([1]).unsqueeze(0) # Batch size 1\n",
|
||
" outputs = model(**inputs, labels=labels)\n",
|
||
" _, predictions = torch.max(outputs[1], 1)\n",
|
||
" a = int(predictions.int())\n",
|
||
" b = line.get('label')\n",
|
||
" print(i)\n",
|
||
" i += 1\n",
|
||
" sum_preds += int(a == b)\n",
|
||
"\n",
|
||
"print(f\"ACCURACY: {(sum_preds/i * 100)}\")"
|
||
]
|
||
}
|
||
]
|
||
} |