{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AJJ9B2v6-Xna", "outputId": "49913840-3c1e-4647-95c3-b75192aacd25" }, "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[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hCollecting transformers\n", " Downloading transformers-4.26.0-py3-none-any.whl (6.3 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m69.7 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 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"cell_type": "code", "source": [ "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "device" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "-hCpa7A9crhz", "outputId": "6738a397-d1d0-4d65-fd71-f69dc7b16498" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'cuda'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "7ad9610223044081816a7258e097d878", "888be178fed04a278b18c181b48fa0bb", "b5693089a53042beb1e845045564345a", "037b5adbc2fd4d52b23f0df0ad7cd309", "1621026aa15a45329502ef96bece682a", "dda446073f3e48e794aa5aa5716502f3", "886b97e782d846e383d2b9fcabddd148", "ce9f634e3874405fa3bc6f19716054c5", "977c82cfb8ce4a84b5eb5d8883b261ea", "f593896178284fbeb83d823ef8619386", "efa911ab769a4ee395af9a3214e57edc", "5696afbc323244f69598ca5e3ee479af", "e3039157007e4eea8c56edf10b74a477", "e6d50dda045448a49247ba37d3305e9f", "b829af9c223040949e85c6ec24bacf8e", "d05ad8e433fc49449336ee4dd87acc88", "1fe92af3886e4986913d943a7af0ac56", "1945c96df95845ca842baeb88fb83d3a", "c4c6cdf84eee4ca8834e421bb30ec25c", "e564ac0e22d94561a76e42afe2f890ae", "bc7789038345498796fb78b7b3559cbe", "e56a6d4505344d1ab9ea7b4e817c1d22", "eb7c33d7ee8d4a729591d3b2266d41ab", "ad313b0b123948b9be90964bf039f6de", "ad8024ecd7d549c292bb0f1f09d79079", "e87ced487035447c997a6fd53aaf9320", "a43a4d2693a54d43986951e3b38f800b", "75a3b022acd34154bf5eb19795e6305b", "9eba6ed51e904840b7eb75e5120b9057", "6f73c843f7a641d7acb0b70de272532d", "ffacaf2f9da140c797304236f07ad8e3", "8660ccbbb5f347118b2ef9e1dc19e8c3", "66a7724c40f74b4a9ea468ee46e44ba5", "b11bcb52db6a417faa9ebec26b3b90fd", "512fc85481db43d5b37e2bd8fad1da32", "1b4798d14b2a430da7f6515905b2d1bc", 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"1ed9761d847543efb2c3a7e2a8a37b3b", "755e86df1e7c49b8a92a43e7ba09efb0", "403df49447664ce09e6c84cf7df0c872", "2c08c03e6ab046b5ba11eb51a35c67be", "c015f27a7f6244b7ab9c92b9e24c4f24", "76975e0ebcf04d9a8367cc177d6accd7", "feb20344101e4e49b683216cc29bd357", "ff1acc41dd5b4f99b8cfb1ab3e56a43c", "e59113f81046477786a6cd8a60f2bb40", "6b9f4e0da4164c37a41ce85746b68664", "6824e56d0d9642e38dd659ca51e2f5b6", "a16b269ce71c4b64894fd898de779b12", "07328ccc4d79452f8b046431a50566a9", "cf6c41b676f344fb9e727adcc61ba5b5", "8c0e231b31bd4cf78d9ba8e9ad7cc8cb", "66210d69385b48ee91d84cbbcfd2c6e3", "333b4a61c8e149ec8a0eab14e6cabf22", "059b72ace86541ada6f7a353bf8ce132", "c164c26cd16045a9839218e6db4e385a", "97e41ffa9e874a20913abff5437df135", "8d043afa8e7c4773abc4986455872634", "63381803d15a4eac8b3de40662811d37", "c421884ebce2450c82d9f90de0615288", "744cc66a00044e0db32745b15900be66", "edb24966372a40dd903c7f6aa7e30fb3", "1551b6ebb0084afcab16187d40214f35", "565bfe8f65ab439d99e93cc1833b2705", "e82907b7eac04f43b70a82d2e51302fa", "8d2a42d69fc641ebb8e89bcca569b22a", "0875f6c7f22c48b09f62c76bbc8d95b9", "1cfa1f3167ba42b4ad83be27620979f3", "2830283e56bc4275821361582db4854e", "fabb0e9eba3a45a082779596dcdda0b4", "eb48bb36dee14eaebf8dd81942022294", "2ce2fb8864ba4202a75448e00c4ef007", "903e647eb6f1408ba0852bd7e07a2212", "79d59e7f8abf417d82058e230c6e84ba", "3220f3b75c37460fb1fabc80da742358", "88df2ea3a4784543a0a3f4fb232ad3a1", "323ae6bb9c6d46f49ac9883113e3ab7d", "fd9f31fda6a64b638cfd2ba49c643344", "06760b850dd84d19b35e77cea3f98c9c", "bd0104bbd6eb4588b643c1d0fb736217", "3b7fc76c25ae4f119f357d3c06f864af", "db09425df69e436e9ddef0b3e8d858af", "f922351e55dd47f89f691510685ea2ba", "2a9671ce289047e58662c7d4058d5f74", "39e53ccc92904d06951943d7eef4b780", "a0275a5e740f42b2bb8c981d5d5f6e20", "d6e95e99b2d94f719b05f995a8d149ae", "8a0ed919945f4cd0ada3d4f86aa86669", "ff328e171ce44bc09645fc5ed8beba7a", "e502b8b35db44e5ab0dd8a5c91bb4f06", "e3ff4bda54784d87a503781d6cd68d85", "3291eb08713a4626837525b551cc22d9", "6768c139bd6f49e3bb6d00d60b358b69", "4e6eceab001e4c44a31e8d4698fb1631", "b5958a54700e4b99b1804d3934a3d97f", "6fce6922761443adadbddc8d0d1131fd", "9be77e7437a9418b81f3182679b4197f", "09f6af8460f84d78a2c8303199f3270c", "c762cd1ea1ac4886b1abffa86039aab2", "aeea1be0482b48268e73c549463c579d", "b84f0f59b11e41379893621aeb289a1e", "d22cf96999aa4b3f95f7659db894c36b", "b223d24fa2c045a791627baa724804e8", "5331d5e572de423fada0546cc618d91d", "a245c8d16a344b5bb167400848b2c4d3", "47772015e6c449da8aff53dbfa0cffc3", "b989e3b708df4dbe850cf747bb9ec4c1", "8309f0819c444eb892368fdf2e83fd4a", "3a0b417a35b142a9af8678195f46a52c", "23ae05a35b524ddeaa53230bef0ea96b", "94b30dd4df0447b5b86e750c02ab00ee", "62626b9d17b04f6487f940e7bccabdbc", "e7b35092ab984f3783c2d5fd388dcf57", "dd29a21d218c40329da13051965353ae", "ed903053fd9e47dab58b1525f47dfb7a", "3e5bc82598144aceb8dce56127e7667a", "afa313b4156c4311b262e72ec7ca8a2d", "5345cb10f07e453b839c057e3ea46515", "8d3d57fde6454029be9e7ca090295859", "d6165d23dd254ebfbc998111c13659f8", "8d4316ef2beb42fd921abdc9a30f7458", "5fe0178d997e4895b7276000dc66cb1a", "d89e9fead08441119b2ef302048b942e", "5fa2f44757bb4b4f964d5226a45623c5", "ee535c7bb8d84b15a0dbc1dc9c2ee9dc", "36c15405afb44621aae008919af32427", "c4d2e0eb4b9046888dd6ed8ea4e62e50", "b9bbdd9e11d94e3bbba2dad2965016a1", "da0cd1552e4f437f847e8b4e531abb1a", "9e5488d1fd564163ac99350130b0bece", "7a5bbb426aed4c64b3eea80abb290397", "dd5ccee7ab104ecb81c2836cb4016b5f", "86b19ac631cc49cbbfe25ba45066f6fd", "8cbde9775a044a56a66773f3767db715", "8514f3c6d6b14bb686b231c217d634bc", "e38116c6ad964a8bb37647330189b161", "b920c2432cbb42afb39e22b241ba6139", "9f7303a3628e4087be23295c10e1499b", "442e925806de4d4186d5c101134d9c62", "92eaffca64b7445e812564c41ca8f3e7", "5f5ea46b5ae14a3f8ca2c48695e1596e", "0af380bad8924ece854b06bf79485ca3", "60ced01ddb934935b772d04b3af8e200", "2c12a7af342d47c8b3d87e15e923d58d", "45baa80ab77e489d8c702dce1c9da241", "80151ba1080444928b84c030d903039c", "000a63769e43456cb0ee18807cc0f9b3", "c4ab88c73ae3414ca72e77a4672d8fc3", "363c3eb2874746aa8d1d4294f2acea2d", "7e3d22287f6244d69490cb5bb1c95a74", "c3d8987cd6944ef8bf0f47b982b852f4", "9e3f231d54464f408ff8e29be684b89d", "67eabe64d5d145da8fd39973a9b40bcd", "f7ed1bab834d4b878de373e5e914b280", "63f100f3c386401ca3c4d8d7271db24b", "36b50f0c07af45f09fcd733a4e864638", "b30663644ffa48bca54fb74309b6226d", "acad7a3022d3471a8feea8f005b32172", "bd8ac0728e7746ed812a6555494a4ea9", "c5c1eba10d0c4aa98abf6cfa97c4e833", "70310effe97f4d1085f8d29c9ed21d1d", "1560ebd3d61b4490b31c2ed9aed091b0", "a5be8278a5c04cd5a99c81d366d97589", "53a292f572a744398d20a9126046ae3f", "d1fdff486383453fa0d5e2acffc362cf", "6cedd92e303b4d648f696299d2c50f07", "c26d4ac54db64d7aad1b9d4402df5e2b", "07238ae15b5041679ba9191cc727c74a", "7e75c8164d8941a696e5ca510097e314", "1b4fd5c0dac24bad8983b08430a567ae", "2b2373d5b7834efb9648eb1ea6b1ab71", "96dcea02cc004dddb959733a33c78a14" ] }, "id": "lXyDkA_2-KTl", 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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 2000\n", " Batch size = 32\n", "Saving model checkpoint to ./output/checkpoint-31\n", "Configuration saved in ./output/checkpoint-31/config.json\n", "Model weights saved in ./output/checkpoint-31/pytorch_model.bin\n", "The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 2000\n", " Batch size = 32\n", "Saving model checkpoint to ./output/checkpoint-62\n", "Configuration saved in ./output/checkpoint-62/config.json\n", "Model weights saved in ./output/checkpoint-62/pytorch_model.bin\n", "The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 2000\n", " Batch size = 32\n", "Saving model checkpoint to ./output/checkpoint-93\n", "Configuration saved in ./output/checkpoint-93/config.json\n", "Model weights saved in ./output/checkpoint-93/pytorch_model.bin\n", "The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 2000\n", " Batch size = 32\n", "Saving model checkpoint to ./output/checkpoint-124\n", "Configuration saved in ./output/checkpoint-124/config.json\n", "Model weights saved in ./output/checkpoint-124/pytorch_model.bin\n", "The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 2000\n", " Batch size = 32\n", "Saving model checkpoint to ./output/checkpoint-155\n", "Configuration saved in ./output/checkpoint-155/config.json\n", "Model weights saved in ./output/checkpoint-155/pytorch_model.bin\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n", "Loading best model from ./output/checkpoint-155 (score: 0.1608307659626007).\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=155, training_loss=0.495477742725803, metrics={'train_runtime': 393.8758, 'train_samples_per_second': 203.11, 'train_steps_per_second': 0.394, 'total_flos': 3612118290333696.0, 'train_loss': 0.495477742725803, 'epoch': 4.99})" ] }, "metadata": {}, "execution_count": 2 } ], "source": [ "from datasets import load_dataset\n", "from transformers import RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer, Trainer, TrainingArguments\n", "import torch\n", "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n", "\n", "\n", "# Initializing a RoBERTa configuration\n", "configuration = RobertaConfig(vocab_size=2048)\n", "model = RobertaForSequenceClassification(configuration).from_pretrained(\"roberta-base\", num_labels=6)\n", "\n", "model.cuda()\n", "tokenizer = RobertaTokenizer.from_pretrained(\"roberta-base\")\n", "\n", "\n", "def tokenization(batched_text):\n", " return tokenizer(batched_text['text'], padding = True, truncation=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", "# Accessing the model configuration\n", "configuration = model.config\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=5,\n", " per_device_train_batch_size = 32,\n", " gradient_accumulation_steps = 16, \n", " per_device_eval_batch_size= 32,\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 = 'roberta-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", "\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", "# print(predictions)\n" ] }, { "cell_type": "code", "source": [ "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", " i += 1\n", " sum_preds += int(a == b)\n", "\n", "print(f\"ACCURACY: {(sum_preds/i * 100)}\")\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Yo80KbX5gxVi", "outputId": "96ed92b0-bee3-4139-eeff-9f2c0b9c33fb" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": 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