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{
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"source": [
"! pip install datasets transformers torch scikit-learn evaluate"
],
"metadata": {
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},
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{
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"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting datasets\n",
" Downloading datasets-2.9.0-py3-none-any.whl (462 kB)\n",
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"\u001b[?25hCollecting transformers\n",
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"\u001b[?25hRequirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n",
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"Installing collected packages: tokenizers, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets, evaluate\n",
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"source": [],
"metadata": {
"id": "a_f-yno_zity"
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{
"cell_type": "code",
"source": [
"!wget 'https://git.wmi.amu.edu.pl/s444465/projekt-glebokie/raw/branch/master/run_glue.py' -O 'run_glue.py'\n",
"!wget 'https://git.wmi.amu.edu.pl/s444465/projekt-glebokie/raw/branch/master/roberta.py' -O 'roberta.py'\n",
"!wget 'https://git.wmi.amu.edu.pl/s444465/projekt-glebokie/raw/branch/master/gpt2.py' -O 'gpt2.py'"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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},
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"id": "V_HmRNcmzhsw",
"outputId": "4e1f2362-305f-4af3-f125-32ccadb484a2"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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},
{
"cell_type": "code",
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"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"
],
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
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"height": 667,
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},
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"execution_count": 3,
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{
"output_type": "display_data",
"data": {
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"output_type": "stream",
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"text": [
"WARNING:datasets.builder:No config specified, defaulting to: emotion/split\n"
]
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},
{
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"text": [
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"Downloading and preparing dataset emotion/split to /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd...\n"
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{
"output_type": "display_data",
"data": {
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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"model_id": "11286368590748a295186146162297ce"
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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"model_id": "e7b1d27f3aa64d4f8d09127e36037731"
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
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"model_id": "9422ac8de56949968f6dd0aac6c82e90"
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
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"model_id": "766d69ba63734c8e9dea95623c060c18"
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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"model_id": "fdf218b36eda42aa9ed2dd633d240f9d"
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}
},
"metadata": {}
},
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{
"output_type": "stream",
"name": "stdout",
"text": [
"Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd. Subsequent calls will reuse this data.\n"
]
},
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{
"output_type": "display_data",
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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"model_id": "f78c464090be4742be047cadf84cd5d7"
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}
},
"metadata": {}
},
{
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"output_type": "stream",
"name": "stdout",
"text": [
"mkdir: created directory 'data'\n",
"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": "b3291697-b066-4acb-e9f8-d1aef3599dac"
},
"execution_count": 4,
"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/"
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},
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"id": "rFa6ijdx2L28",
"outputId": "2dcdecf5-6240-40cb-c157-8548f17b920a"
},
"execution_count": 5,
"outputs": [
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{
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"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": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "pxuxjHt8P57X"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!python run_glue.py \\\n",
"--cache_dir .cache_training \\\n",
"--model_name_or_path gpt2 \\\n",
"--custom_model gpt2_hidden \\\n",
"--freeze_weights \\\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/",
"height": 1000
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},
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"id": "XkkeRPG_z3Jc",
"outputId": "b0886744-a6a5-4472-dbc2-94a3928e6cdf"
},
"execution_count": 7,
"outputs": [
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{
"output_type": "stream",
"name": "stdout",
"text": [
2023-02-12 20:03:40 +01:00
"2023-02-12 18:48:04.170632: 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 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2023-02-12 18:48:06.020374: 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",
"2023-02-12 18:48:06.020480: 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",
"2023-02-12 18:48:06.020498: 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/Feb12_18-48-11_0a1839978b23,\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__: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-eb684d402fe5af19\n",
"INFO:datasets.info:Loading Dataset Infos from /usr/local/lib/python3.8/dist-packages/datasets/packaged_modules/json\n",
"INFO:datasets.builder:Generating dataset json (/content/.cache_training/json/default-eb684d402fe5af19/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)\n",
"Downloading and preparing dataset json/default to /content/.cache_training/json/default-eb684d402fe5af19/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\n",
"Downloading data files: 100% 3/3 [00:00<00:00, 8519.24it/s]\n",
"INFO:datasets.download.download_manager:Downloading took 0.0 min\n",
"INFO:datasets.download.download_manager:Checksum Computation took 0.0 min\n",
"Extracting data files: 100% 3/3 [00:00<00:00, 1477.73it/s]\n",
"INFO:datasets.utils.info_utils:Unable to verify checksums.\n",
"INFO:datasets.builder:Generating train split\n",
"INFO:datasets.builder:Generating validation split\n",
"INFO:datasets.builder:Generating test split\n",
"INFO:datasets.utils.info_utils:Unable to verify splits sizes.\n",
"Dataset json downloaded and prepared to /content/.cache_training/json/default-eb684d402fe5af19/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n",
"100% 3/3 [00:00<00:00, 730.84it/s]\n",
"Downloading (…)lve/main/config.json: 100% 665/665 [00:00<00:00, 91.3kB/s]\n",
"[INFO|configuration_utils.py:660] 2023-02-12 18:48:12,394 >> loading configuration file config.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/config.json\n",
"[INFO|configuration_utils.py:712] 2023-02-12 18:48:12,395 >> 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] 2023-02-12 18:48:12,427 >> Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
"[INFO|configuration_utils.py:660] 2023-02-12 18:48:12,460 >> loading configuration file config.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/config.json\n",
"[INFO|configuration_utils.py:712] 2023-02-12 18:48:12,461 >> 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",
"Downloading (…)olve/main/vocab.json: 100% 1.04M/1.04M [00:00<00:00, 68.6MB/s]\n",
"Downloading (…)olve/main/merges.txt: 100% 456k/456k [00:00<00:00, 36.6MB/s]\n",
"Downloading (…)/main/tokenizer.json: 100% 1.36M/1.36M [00:00<00:00, 61.1MB/s]\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 18:48:12,933 >> loading file vocab.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/vocab.json\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 18:48:12,933 >> loading file merges.txt from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/merges.txt\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 18:48:12,933 >> loading file tokenizer.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/tokenizer.json\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 18:48:12,933 >> loading file added_tokens.json from cache at None\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 18:48:12,933 >> loading file special_tokens_map.json from cache at None\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 18:48:12,933 >> loading file tokenizer_config.json from cache at None\n",
"[INFO|configuration_utils.py:660] 2023-02-12 18:48:12,934 >> loading configuration file config.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/config.json\n",
"[INFO|configuration_utils.py:712] 2023-02-12 18:48:12,935 >> 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:__main__:Using hidden states in model: True\n",
"INFO:__main__:Using implementation from class: GPT2ForSequenceClassificationCustom\n",
"Downloading (…)\"pytorch_model.bin\";: 100% 548M/548M [00:02<00:00, 259MB/s]\n",
"[INFO|modeling_utils.py:2275] 2023-02-12 18:48:15,229 >> loading weights file pytorch_model.bin from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/pytorch_model.bin\n",
"[INFO|modeling_utils.py:2857] 2023-02-12 18:48:20,237 >> All model checkpoint weights were used when initializing GPT2ForSequenceClassificationCustom.\n",
"\n",
"[WARNING|modeling_utils.py:2859] 2023-02-12 18:48:20,237 >> Some weights of GPT2ForSequenceClassificationCustom were not initialized from the model checkpoint at gpt2 and are newly initialized: ['score.dense_2.bias', 'score.out_proj.weight', 'score.dense_2.weight', 'score.dense_1_hidden.bias', 'score.dense_1_input.weight', 'score.dense_1_input.bias', 'score.dense_1_hidden.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
"INFO:__main__:Freezing encoder weights\n",
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"[ERROR|tokenization_utils_base.py:1042] 2023-02-12 18:48:20,265 >> Using pad_token, but it is not set yet.\n",
"INFO:__main__:Set PAD token to EOS: <|endoftext|>\n",
"Running tokenizer on dataset: 0% 0/5 [00:00<?, ?ba/s]INFO:datasets.arrow_dataset:Caching processed dataset at /content/.cache_training/json/default-eb684d402fe5af19/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-226821bed74c0e17.arrow\n",
"Running tokenizer on dataset: 100% 5/5 [00:01<00:00, 3.34ba/s]\n",
"Running tokenizer on dataset: 0% 0/2 [00:00<?, ?ba/s]INFO:datasets.arrow_dataset:Caching processed dataset at /content/.cache_training/json/default-eb684d402fe5af19/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-b1f24dce00ae0c47.arrow\n",
"Running tokenizer on dataset: 100% 2/2 [00:00<00:00, 2.35ba/s]\n",
"Running tokenizer on dataset: 0% 0/2 [00:00<?, ?ba/s]INFO:datasets.arrow_dataset:Caching processed dataset at /content/.cache_training/json/default-eb684d402fe5af19/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-13134a48c79051f9.arrow\n",
"Running tokenizer on dataset: 100% 2/2 [00:00<00:00, 4.98ba/s]\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",
"Downloading builder script: 100% 4.20k/4.20k [00:00<00:00, 3.58MB/s]\n",
"[INFO|trainer.py:710] 2023-02-12 18:48:30,921 >> The following columns in the training set don't have a corresponding argument in `GPT2ForSequenceClassificationCustom.forward` and have been ignored: text. If text are not expected by `GPT2ForSequenceClassificationCustom.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] 2023-02-12 18:48:30,927 >> ***** Running training *****\n",
"[INFO|trainer.py:1651] 2023-02-12 18:48:30,927 >> Num examples = 4999\n",
"[INFO|trainer.py:1652] 2023-02-12 18:48:30,927 >> Num Epochs = 5\n",
"[INFO|trainer.py:1653] 2023-02-12 18:48:30,927 >> Instantaneous batch size per device = 24\n",
"[INFO|trainer.py:1654] 2023-02-12 18:48:30,927 >> Total train batch size (w. parallel, distributed & accumulation) = 24\n",
"[INFO|trainer.py:1655] 2023-02-12 18:48:30,927 >> Gradient Accumulation steps = 1\n",
"[INFO|trainer.py:1656] 2023-02-12 18:48:30,927 >> Total optimization steps = 1045\n",
"[INFO|trainer.py:1657] 2023-02-12 18:48:30,928 >> Number of trainable parameters = 68517888\n",
"{'loss': 1.0247, 'learning_rate': 1.0430622009569378e-05, 'epoch': 2.39}\n",
" 48% 500/1045 [03:49<04:14, 2.14it/s][INFO|trainer.py:2709] 2023-02-12 18:52:20,075 >> Saving model checkpoint to out/imdb-5k/gpt2/checkpoint-500\n",
"[INFO|configuration_utils.py:453] 2023-02-12 18:52:20,076 >> Configuration saved in out/imdb-5k/gpt2/checkpoint-500/config.json\n",
"[INFO|modeling_utils.py:1704] 2023-02-12 18:52:21,822 >> Model weights saved in out/imdb-5k/gpt2/checkpoint-500/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2160] 2023-02-12 18:52:21,823 >> tokenizer config file saved in out/imdb-5k/gpt2/checkpoint-500/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2167] 2023-02-12 18:52:21,823 >> Special tokens file saved in out/imdb-5k/gpt2/checkpoint-500/special_tokens_map.json\n",
"{'loss': 0.3843, 'learning_rate': 8.612440191387561e-07, 'epoch': 4.78}\n",
" 96% 1000/1045 [07:46<00:20, 2.15it/s][INFO|trainer.py:2709] 2023-02-12 18:56:17,122 >> Saving model checkpoint to out/imdb-5k/gpt2/checkpoint-1000\n",
"[INFO|configuration_utils.py:453] 2023-02-12 18:56:17,123 >> Configuration saved in out/imdb-5k/gpt2/checkpoint-1000/config.json\n",
"[INFO|modeling_utils.py:1704] 2023-02-12 18:56:18,817 >> Model weights saved in out/imdb-5k/gpt2/checkpoint-1000/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2160] 2023-02-12 18:56:18,817 >> tokenizer config file saved in out/imdb-5k/gpt2/checkpoint-1000/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2167] 2023-02-12 18:56:18,818 >> Special tokens file saved in out/imdb-5k/gpt2/checkpoint-1000/special_tokens_map.json\n",
"100% 1045/1045 [08:10<00:00, 2.65it/s][INFO|trainer.py:1901] 2023-02-12 18:56:41,796 >> \n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n",
"{'train_runtime': 490.8844, 'train_samples_per_second': 50.918, 'train_steps_per_second': 2.129, 'train_loss': 0.689463275015069, 'epoch': 5.0}\n",
"100% 1045/1045 [08:10<00:00, 2.13it/s]\n",
"[INFO|trainer.py:2709] 2023-02-12 18:56:41,814 >> Saving model checkpoint to out/imdb-5k/gpt2\n",
"[INFO|configuration_utils.py:453] 2023-02-12 18:56:41,815 >> Configuration saved in out/imdb-5k/gpt2/config.json\n",
"[INFO|modeling_utils.py:1704] 2023-02-12 18:56:43,512 >> Model weights saved in out/imdb-5k/gpt2/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2160] 2023-02-12 18:56:43,513 >> tokenizer config file saved in out/imdb-5k/gpt2/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2167] 2023-02-12 18:56:43,513 >> Special tokens file saved in out/imdb-5k/gpt2/special_tokens_map.json\n",
"***** train metrics *****\n",
" epoch = 5.0\n",
" train_loss = 0.6895\n",
" train_runtime = 0:08:10.88\n",
" train_samples = 4999\n",
" train_samples_per_second = 50.918\n",
" train_steps_per_second = 2.129\n",
"INFO:__main__:*** Evaluate ***\n",
"[INFO|trainer.py:710] 2023-02-12 18:56:43,641 >> The following columns in the evaluation set don't have a corresponding argument in `GPT2ForSequenceClassificationCustom.forward` and have been ignored: text. If text are not expected by `GPT2ForSequenceClassificationCustom.forward`, you can safely ignore this message.\n",
"[INFO|trainer.py:2964] 2023-02-12 18:56:43,642 >> ***** Running Evaluation *****\n",
"[INFO|trainer.py:2966] 2023-02-12 18:56:43,642 >> Num examples = 1274\n",
"[INFO|trainer.py:2969] 2023-02-12 18:56:43,642 >> Batch size = 24\n",
"100% 54/54 [00:09<00:00, 5.50it/s]\n",
"***** eval metrics *****\n",
" epoch = 5.0\n",
" eval_accuracy = 0.9231\n",
" eval_loss = 0.2178\n",
" eval_runtime = 0:00:10.05\n",
" eval_samples = 1274\n",
" eval_samples_per_second = 126.717\n",
" eval_steps_per_second = 5.371\n",
"INFO:__main__:*** Predict ***\n",
"[INFO|trainer.py:710] 2023-02-12 18:56:53,699 >> The following columns in the test set don't have a corresponding argument in `GPT2ForSequenceClassificationCustom.forward` and have been ignored: text. If text are not expected by `GPT2ForSequenceClassificationCustom.forward`, you can safely ignore this message.\n",
"[INFO|trainer.py:2964] 2023-02-12 18:56:53,701 >> ***** Running Prediction *****\n",
"[INFO|trainer.py:2966] 2023-02-12 18:56:53,701 >> Num examples = 1278\n",
"[INFO|trainer.py:2969] 2023-02-12 18:56:53,701 >> Batch size = 24\n",
"100% 54/54 [00:09<00:00, 5.49it/s]\n",
"INFO:__main__:***** Predict results None *****\n",
"[INFO|modelcard.py:449] 2023-02-12 18:57:03,752 >> 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': 0.9230769276618958}]}\n"
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]
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}
]
},
{
"cell_type": "code",
"source": [
"CUDA_LAUNCH_BLOCKING=1"
],
"metadata": {
"id": "vTup03PZl1IO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
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},
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"id": "Wvhv6_uyymcs",
"outputId": "4c5cb0b8-e8fc-49bb-c1db-cfd3e21fd7ee"
},
"outputs": [
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{
"output_type": "stream",
"name": "stderr",
"text": [
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"loading file vocab.json from cache at /root/.cache/huggingface/hub/models--microsoft--DialogRPT-updown/snapshots/afe1247fd7e1b3abea28a52ea72db4ce1c8d2186/vocab.json\n",
"loading file merges.txt from cache at /root/.cache/huggingface/hub/models--microsoft--DialogRPT-updown/snapshots/afe1247fd7e1b3abea28a52ea72db4ce1c8d2186/merges.txt\n",
"loading file added_tokens.json from cache at None\n",
"loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--microsoft--DialogRPT-updown/snapshots/afe1247fd7e1b3abea28a52ea72db4ce1c8d2186/special_tokens_map.json\n",
"loading file tokenizer_config.json from cache at None\n",
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--microsoft--DialogRPT-updown/snapshots/afe1247fd7e1b3abea28a52ea72db4ce1c8d2186/config.json\n",
"Model config GPT2Config {\n",
" \"_name_or_path\": \"microsoft/DialogRPT-updown\",\n",
" \"activation_function\": \"gelu_new\",\n",
" \"architectures\": [\n",
" \"GPT2ForSequenceClassification\"\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",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"label2id\": {\n",
" \"LABEL_0\": 0\n",
" },\n",
" \"layer_norm_epsilon\": 1e-05,\n",
" \"model_type\": \"gpt2\",\n",
" \"n_ctx\": 1024,\n",
" \"n_embd\": 1024,\n",
" \"n_head\": 16,\n",
" \"n_inner\": null,\n",
" \"n_layer\": 24,\n",
" \"n_positions\": 1024,\n",
" \"n_special\": 0,\n",
" \"pad_token_id\": 50256,\n",
" \"predict_special_tokens\": true,\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",
"Assigning [PAD] to the pad_token key of the tokenizer\n",
"Adding [PAD] to the vocabulary\n",
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--microsoft--DialogRPT-updown/snapshots/afe1247fd7e1b3abea28a52ea72db4ce1c8d2186/config.json\n",
"Model config GPT2Config {\n",
" \"activation_function\": \"gelu_new\",\n",
" \"architectures\": [\n",
" \"GPT2ForSequenceClassification\"\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\": 1024,\n",
" \"n_head\": 16,\n",
" \"n_inner\": null,\n",
" \"n_layer\": 24,\n",
" \"n_positions\": 1024,\n",
" \"n_special\": 0,\n",
" \"pad_token_id\": 50256,\n",
" \"predict_special_tokens\": true,\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",
"loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--microsoft--DialogRPT-updown/snapshots/afe1247fd7e1b3abea28a52ea72db4ce1c8d2186/pytorch_model.bin\n",
"All model checkpoint weights were used when initializing GPT2ForSequenceClassification.\n",
"\n",
"Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at microsoft/DialogRPT-updown and are newly initialized because the shapes did not match:\n",
"- score.weight: found shape torch.Size([1, 1024]) in the checkpoint and torch.Size([6, 1024]) in the model instantiated\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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]
},
{
"output_type": "error",
"ename": "RuntimeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-4-1d5e45df897c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGPT2ForSequenceClassification\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"microsoft/DialogRPT-updown\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_mismatched_sizes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\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 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtokenization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatched_text\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mcuda\u001b[0;34m(self, device)\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[0mModule\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 748\u001b[0m \"\"\"\n\u001b[0;32m--> 749\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\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 750\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 751\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mipu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT\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_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 639\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\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 640\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\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--> 641\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\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 642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 643\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 639\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\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 640\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\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--> 641\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\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 642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 643\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 662\u001b[0m \u001b[0;31m# `with torch.no_grad():`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 663\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\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--> 664\u001b[0;31m \u001b[0mparam_applied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\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 665\u001b[0m \u001b[0mshould_use_set_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 666\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mshould_use_set_data\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<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[0mModule\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 748\u001b[0m \"\"\"\n\u001b[0;32m--> 749\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\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 750\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 751\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mipu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1."
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]
}
],
"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",
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"config=GPT2Config(vocab_size=2048, num_labels=6)\n",
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"tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
"tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
"\n",
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"model = GPT2ForSequenceClassification(config).from_pretrained('gpt2', num_labels=6)\n",
"model.cuda()\n",
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"\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)}\")"
]
}
]
}
