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"model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "code", "source": [ "! pip install datasets transformers torch scikit-learn evaluate" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "u29i-U30zRjY", "outputId": "5153ea5d-33b5-4c01-f9d9-775d107aa527" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "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", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta 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"colab": { "base_uri": "https://localhost:8080/" }, "id": "V_HmRNcmzhsw", "outputId": "310ed497-e8ba-429e-9a54-00124cf0cd25" }, "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", "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 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=,\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=,\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> 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> 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", " 96% 1001/1045 [01:17<00:36, 1.20it/s]\u001b[A\n", " 96% 1002/1045 [01:17<00:26, 1.59it/s]\u001b[A\n", " 96% 1003/1045 [01:17<00:20, 2.07it/s]\u001b[A\n", " 96% 1004/1045 [01:17<00:15, 2.62it/s]\u001b[A\n", " 96% 1005/1045 [01:17<00:12, 3.21it/s]\u001b[A\n", " 96% 1006/1045 [01:17<00:10, 3.81it/s]\u001b[A\n", " 96% 1007/1045 [01:18<00:08, 4.39it/s]\u001b[A\n", " 96% 1008/1045 [01:18<00:07, 4.90it/s]\u001b[A\n", " 97% 1009/1045 [01:18<00:06, 5.36it/s]\u001b[A\n", " 97% 1010/1045 [01:18<00:06, 5.64it/s]\u001b[A\n", " 97% 1011/1045 [01:18<00:05, 5.93it/s]\u001b[A\n", " 97% 1012/1045 [01:18<00:05, 6.17it/s]\u001b[A\n", " 97% 1013/1045 [01:18<00:05, 6.35it/s]\u001b[A\n", " 97% 1014/1045 [01:19<00:04, 6.49it/s]\u001b[A\n", " 97% 1015/1045 [01:19<00:04, 6.57it/s]\u001b[A\n", " 97% 1016/1045 [01:19<00:04, 6.64it/s]\u001b[A\n", " 97% 1017/1045 [01:19<00:04, 6.70it/s]\u001b[A\n", " 97% 1018/1045 [01:19<00:04, 6.74it/s]\u001b[A\n", " 98% 1019/1045 [01:19<00:03, 6.77it/s]\u001b[A\n", " 98% 1020/1045 [01:19<00:03, 6.79it/s]\u001b[A\n", " 98% 1021/1045 [01:20<00:03, 6.79it/s]\u001b[A\n", " 98% 1022/1045 [01:20<00:03, 6.81it/s]\u001b[A\n", " 98% 1023/1045 [01:20<00:03, 6.81it/s]\u001b[A\n", " 98% 1024/1045 [01:20<00:03, 6.80it/s]\u001b[A\n", " 98% 1025/1045 [01:20<00:02, 6.81it/s]\u001b[A\n", " 98% 1026/1045 [01:20<00:02, 6.81it/s]\u001b[A\n", " 98% 1027/1045 [01:20<00:02, 6.81it/s]\u001b[A\n", " 98% 1028/1045 [01:21<00:02, 6.81it/s]\u001b[A\n", " 98% 1029/1045 [01:21<00:02, 6.81it/s]\u001b[A\n", " 99% 1030/1045 [01:21<00:02, 6.80it/s]\u001b[A\n", " 99% 1031/1045 [01:21<00:02, 6.81it/s]\u001b[A\n", " 99% 1032/1045 [01:21<00:01, 6.81it/s]\u001b[A\n", " 99% 1033/1045 [01:21<00:01, 6.81it/s]\u001b[A\n", " 99% 1034/1045 [01:22<00:01, 6.81it/s]\u001b[A\n", " 99% 1035/1045 [01:22<00:01, 6.81it/s]\u001b[A\n", " 99% 1036/1045 [01:22<00:01, 6.70it/s]\u001b[A\n", " 99% 1037/1045 [01:22<00:01, 6.73it/s]\u001b[A\n", " 99% 1038/1045 [01:22<00:01, 6.75it/s]\u001b[A\n", " 99% 1039/1045 [01:22<00:00, 6.77it/s]\u001b[A\n", "100% 1040/1045 [01:22<00:00, 6.78it/s]\u001b[A\n", "100% 1041/1045 [01:23<00:00, 6.79it/s]\u001b[A\n", "100% 1042/1045 [01:23<00:00, 6.80it/s]\u001b[A\n", "100% 1043/1045 [01:23<00:00, 6.80it/s]\u001b[A\n", "100% 1044/1045 [01:23<00:00, 6.81it/s]\u001b[A[INFO|trainer.py:1901] 2023-02-10 21:24:32,729 >> \n", "\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", 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"0d2449918088475b8f5d9a565df8313e", "218e5c36adcc4c30acc2e6258b064d45", "d44f09b99aaa48078639294de8545760", "937a618928e64ff3b83f513307f7739a", "ccd95f01c0f948bc974d854a7f271f5a", "4e1371d4c889450e8931119d5b20e755" ] }, "id": "Wvhv6_uyymcs", "outputId": "a8317ae9-d601-48c0-ffe6-27018ba4cdbf" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)olve/main/vocab.json: 0%| | 0.00/1.04M [00:00\u001b[0m in \u001b[0;36m\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 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_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 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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)}\")" ] } ] }