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"colab": { "base_uri": "https://localhost:8080/" }, "id": "u29i-U30zRjY", "outputId": "2ecd3ab8-fcf8-436f-90bc-73b804194095" }, "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[31m19.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hCollecting transformers\n", " Downloading transformers-4.26.1-py3-none-any.whl (6.3 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m33.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n", "Requirement already satisfied: scikit-learn in 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(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": 667, "referenced_widgets": [ "fbc74a6e57c94d66bfb493c150bbbf47", "3de5f4aaad314b04a6d326ed75473deb", "48669319a40449a6ac8bfc850e3888a0", "a1f2f849dec54b8298749baf353fa084", "b114219520ba4477b8c14e0ab6f77d4b", "08ae246277b64dfda696edc1ba2c0b81", "6ead2c9114054c449c074d21b4f50431", "db84fa270ba64af7b0e4f49e9fd84b41", "4b3e7ced2e024d8c8afd1a1cd070034a", "1550a85ee3874857b2b77febfb9e2525", "f12e174f9c5a4057a295c2b0f4c894ba", "0d14dee025724d3097b80fe729ae3532", "7589959458ae451bb0fc46d7e4193bcf", "015cae145720498cba1c65a4ac877052", "9e8fc689767b45b79771c6a5efca10b3", "4cf50a0ecdaf4191a36e8dddfeaa8b36", "30db2acbf995414fb3e47c9d12ee8697", "b3aebbd1bcf241c68ef4d01f244e38c0", "8b2c386292954944bf004dab5a6ef8d8", "1dd09c89af8448ed9673238c4a3ebb8e", "fc2503b36be542e5a3512b8196c30e2b", "8eb569059fa74218a9f47c78e3db8436", "24abb274aee648eaa308802754b96b7a", "aab5cad3e7d94ae981eac90756bf8d41", "fc353140430841929ff5a94e09a4143e", "f23d67fd143844a393b1a08ee3d48969", "f7be3e08cb514922b0f67913216005dc", "32a2ce7715a941b0bec06e5676252446", "c9faf32b655f4fa992d641768a558220", "17b604cbc8da43898eca3f1527e3b125", "d30de94f30ce4da1a516c087074aee96", "adcced2b10f046d0bb31d20e4ec46708", "bb8ce706f3644b13a5a6982b052fec84", "2a8fa16116094071970fac4c8de210e7", "2b766b35ab0944bca286e1a7dea163a4", "f1395abbe0c14c2b925caef7b3630a94", "866570d7610f4843b3ff943809239197", "b5d63d011ffb484d969b3a1db52837a0", "d0e5d5dedb7f48c4b8467f75a374b1ee", "133b2906561441ceb62aaf52ef73e237", "c1f9e1738aad45a3918eb0ee7fe7e1b9", "b0897d82b3d1409880d4739ebc924ec1", "fd502663b2bf4538845cc4ba89605dce", "8d3c98af85474a68b5a11836909446f2", "e58e692e115b44469ec23491d22001f9", "70662fe04f374bc1a4f977526bc7b91e", "f77bc5ec0a1a43028497d888b399e8ef", "5db3f48937274eb9bc8823956bde1f0b", "829fcb51001c4c92b9dfc7d037125075", "fddfc33676b64b9d90429a5339bbeedb", "c569f575d48e49c68e02c148bede9f70", "9fb790cca3334d3b8e8b9c7c02a35cf6", "7edfe8f0b73641a1bfa576aae0703728", "86cfd09655f14aba8efcb98b5933865a", "55969ecc0f2e4a599a5da24b97d856b2", "35b7eb5a427046358c978089ecb9d550", "a0032c1faac44445bec67c334390980e", "ec01cb7f32f5448ca866647e3ebedbfb", "f1ee9050cee845f88e9baf513535785b", "5f4b7a51deb94d4dbae86148225c19b5", "81081eff27b242e8b96f8b17348f5fb2", "a970d1c066994b95835ff14d47d201cc", "5659feb2b0924d9f9e9324cd4c05777d", "eb21821900034762885653d6b54f5461", "7455c4b1c5ac4977b678845a5ba2ea78", "5f6799d3ddbc4c369e1aae2b95b3904c", "11286368590748a295186146162297ce", "8aab56f7ede942eda07d132777e5c1a6", "74e2c6e41731406ba201594f9e3de0be", "d48c6048e8af487aaf92327e8ed4fb7f", "17cb59373eef4e27b6fd58a29303ecc6", "fef878d871614253836af7f166e40010", "6f2162b33b2b4af4af632a9b1f20e291", "fc729efb3eed4f80900f2d110e1e6d4e", "23038bfafd9b4be9ae46bbfabbc5b624", "59d9f15287c1497b82bf7ee540118ac6", "d58b6979f60e42c2a5a79b789e790a03", "e7b1d27f3aa64d4f8d09127e36037731", "374c74aa844f4b13b089bb70d377d698", "82caada33e2640d69f520b524a4456f6", "5efeb3a9060d4448b874a8872f3f165c", "6ae20e8136884830825b5c49c37f867a", "700d432dafd3460288bc63ac9d20a598", "803645353525428ea3ca1cd17197350b", "d6041f8ba8b346838088d2582dbbb95c", "df3f52353df04a088de30577074117f9", "0ef75435cae04344a2eb8a52565e74c8", "201049abbcfa4c03a3ce4a66042c6f09", "9422ac8de56949968f6dd0aac6c82e90", "a4477dd1876f4a9d99408ea568ebdf0c", "62132fd350844e9487633e66692c0c51", "4b3af99603cd4614a32a7dd0f0cf69a7", "d20552706e624d81be477cf53c70de48", "1ace1068c2a74419822f05b01382b66c", "92862060d6b3404395b9d26b762673e0", "2d7de6a858c54f2792e4d38e1b2a57fc", "f4fac1abd5d54adf8e25bca6791daf7f", "6198eafef9d8467d9a63a5329cd6feac", "00164598fdbe4062910ad66f0201d9c3", "766d69ba63734c8e9dea95623c060c18", "20fbbcc1d6c7459fafd988d41438eb80", "e503fe4702d440d58db6c8ebcef5ca8d", "1bd87756be65458da1130d62b4d61c5a", "9776edf5da834fa887b501e385c8977a", "1b73671e115f4d5f93e0cf3ace65edf0", "b36db9ac6eba433a985087fb6ac7f88f", "f2f98c2d73f24d7f973a578f90255a76", "49277a7a3089460fa77ce74f54f263a3", "0f0d8cccdf3f4ec68caad47d432c2e6b", "e849e2fbaa054d4394bd0d081fa919c5", "fdf218b36eda42aa9ed2dd633d240f9d", "d7f5fafaa9b8473996724860957e053c", "d91686aad27f4c39b9dc2952bb175074", "4cb2ab536a6a48869357dcdb85aa07e3", "7d5196e467db44409a244cf73bc61fdc", "9c4cc76511744dd0b2502135aa9b26ad", "2d5274420c074ca1bcbcc1c1414421be", "653f135e75d64a4886fc8aaf9efe2e8e", "5f48da1818b54f148e9c6e9b61dda7f4", "a3a139b1683745958cfd65f4d5855071", "072dbd1ad830460fb1609deb6a4a3daf", "f78c464090be4742be047cadf84cd5d7", "bae635ed7cd54c64aeb618619a5b4207", "bf0c6a864ed946738528c16e1ce7ca3e", "0effabe282f7485f9a41ec518499b376", "cab3e18cde1e40898df29f80af4c049f", "3371bf2f419447c6977db12f3bc8a0ac", "55fcdf435e754088b572ac4cf61ae2e0", "bd74fe36a9c54c5dac5bcda51afee7cf", "d723a2df495a442dabebd2e59797435a", "bac0681cfc8143daa8276eeea99e13ab", "fe5c4bc05b2548e2bd5efac843351c69" ] }, "id": "bcR4tWQl0rqt", "outputId": "a314ee1d-e9b4-4056-dd23-6466e5ccce5d" }, "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/3.97k [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": "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/" }, "id": "rFa6ijdx2L28", "outputId": "2dcdecf5-6240-40cb-c157-8548f17b920a" }, "execution_count": 5, "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": 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 }, "id": "XkkeRPG_z3Jc", "outputId": "b0886744-a6a5-4472-dbc2-94a3928e6cdf" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "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=,\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=,\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", "INFO:__main__:Freezing layer 1\n", "INFO:__main__:Freezing layer 2\n", "INFO:__main__:Freezing layer 3\n", "INFO:__main__:Freezing layer 4\n", "INFO:__main__:Freezing layer 5\n", "INFO:__main__:Freezing layer 6\n", "INFO:__main__:Freezing layer 7\n", "INFO:__main__:Freezing layer 8\n", "INFO:__main__:Freezing layer 9\n", "INFO:__main__:Freezing layer 10\n", "INFO:__main__:Freezing layer 11\n", "INFO:__main__:Freezing layer 12\n", "INFO:__main__:Freezing layer 13\n", "INFO:__main__:Freezing layer 14\n", "INFO:__main__:Freezing layer 15\n", "INFO:__main__:Freezing layer 16\n", "INFO:__main__:Freezing layer 17\n", "INFO:__main__:Freezing layer 18\n", "INFO:__main__:Freezing layer 19\n", "INFO:__main__:Freezing 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"INFO:__main__:Ignoring layer 154\n", "INFO:__main__:Ignoring layer 155\n", "[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> 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" ] } ] }, { "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 }, "id": "Wvhv6_uyymcs", "outputId": "4c5cb0b8-e8fc-49bb-c1db-cfd3e21fd7ee" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "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" ] }, { "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\u001b[0m in \u001b[0;36m\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 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"tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n", "tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n", "\n", "model = GPT2ForSequenceClassification(config).from_pretrained('gpt2', num_labels=6)\n", "model.cuda()\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)}\")" ] } ] }