{"cells": [{"cell_type": "markdown", "id": "f3d7f14b", "metadata": {"papermill": {"duration": 0.004096, "end_time": "2023-01-03T14:51:36.110093", "exception": false, "start_time": "2023-01-03T14:51:36.105997", "status": "completed"}, "tags": []}, "source": ["\n", "# Finetune Transformers Models with PyTorch Lightning\n", "\n", "* **Author:** PL team\n", "* **License:** CC BY-SA\n", "* **Generated:** 2023-01-03T15:49:54.952421\n", "\n", "This notebook will use HuggingFace's `datasets` library to get data, which will be wrapped in a `LightningDataModule`.\n", "Then, we write a class to perform text classification on any dataset from the [GLUE Benchmark](https://gluebenchmark.com/).\n", "(We just show CoLA and MRPC due to constraint on compute/disk)\n", "\n", "\n", "---\n", "Open in [{height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/lightning_examples/text-transformers.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/Lightning-AI/lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/stable/)\n", "| Join us [on Slack](https://www.pytorchlightning.ai/community)"]}, {"cell_type": "markdown", "id": "cbc9ba0d", "metadata": {"papermill": {"duration": 0.044767, "end_time": "2023-01-03T14:51:36.158310", "exception": false, "start_time": "2023-01-03T14:51:36.113543", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "23db96b9", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2023-01-03T14:51:36.223834Z", "iopub.status.busy": "2023-01-03T14:51:36.223367Z", "iopub.status.idle": "2023-01-03T14:51:40.609504Z", "shell.execute_reply": "2023-01-03T14:51:40.608567Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 4.450325, "end_time": "2023-01-03T14:51:40.611595", "exception": false, "start_time": "2023-01-03T14:51:36.161270", "status": "completed"}, "tags": []}, "outputs": [], "source": ["! pip install --quiet \"torchmetrics>=0.7\" \"torchtext>=0.9\" \"pytorch-lightning>=1.4\" \"setuptools==59.5.0\" \"torch>=1.8\" \"transformers\" \"datasets\" \"scikit-learn\" \"ipython[notebook]\" \"scipy\""]}, {"cell_type": "code", "execution_count": 2, "id": "eb65bd76", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:51:40.678626Z", "iopub.status.busy": "2023-01-03T14:51:40.677969Z", "iopub.status.idle": "2023-01-03T14:51:44.013086Z", "shell.execute_reply": "2023-01-03T14:51:44.012338Z"}, "papermill": {"duration": 3.353223, "end_time": "2023-01-03T14:51:44.015013", "exception": false, "start_time": "2023-01-03T14:51:40.661790", "status": "completed"}, "tags": []}, "outputs": [], "source": ["from datetime import datetime\n", "from typing import Optional\n", "\n", "import datasets\n", "import torch\n", "from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything\n", "from torch.utils.data import DataLoader\n", "from transformers import (\n", " AdamW,\n", " AutoConfig,\n", " AutoModelForSequenceClassification,\n", " AutoTokenizer,\n", " get_linear_schedule_with_warmup,\n", ")"]}, {"cell_type": "markdown", "id": "3a732255", "metadata": {"papermill": {"duration": 0.00344, "end_time": "2023-01-03T14:51:44.508894", "exception": false, "start_time": "2023-01-03T14:51:44.505454", "status": "completed"}, "tags": []}, "source": ["## Training BERT with Lightning"]}, {"cell_type": "markdown", "id": "b7cf63eb", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.047285, "end_time": "2023-01-03T14:51:44.564691", "exception": false, "start_time": "2023-01-03T14:51:44.517406", "status": "completed"}, "tags": []}, "source": ["### Lightning DataModule for GLUE"]}, {"cell_type": "code", "execution_count": 3, "id": "4cc12479", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:51:44.615349Z", "iopub.status.busy": "2023-01-03T14:51:44.614869Z", "iopub.status.idle": "2023-01-03T14:51:44.629913Z", "shell.execute_reply": "2023-01-03T14:51:44.629245Z"}, "papermill": {"duration": 0.063527, "end_time": "2023-01-03T14:51:44.631472", "exception": false, "start_time": "2023-01-03T14:51:44.567945", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class GLUEDataModule(LightningDataModule):\n", " task_text_field_map = {\n", " \"cola\": [\"sentence\"],\n", " \"sst2\": [\"sentence\"],\n", " \"mrpc\": [\"sentence1\", \"sentence2\"],\n", " \"qqp\": [\"question1\", \"question2\"],\n", " \"stsb\": [\"sentence1\", \"sentence2\"],\n", " \"mnli\": [\"premise\", \"hypothesis\"],\n", " \"qnli\": [\"question\", \"sentence\"],\n", " \"rte\": [\"sentence1\", \"sentence2\"],\n", " \"wnli\": [\"sentence1\", \"sentence2\"],\n", " \"ax\": [\"premise\", \"hypothesis\"],\n", " }\n", "\n", " glue_task_num_labels = {\n", " \"cola\": 2,\n", " \"sst2\": 2,\n", " \"mrpc\": 2,\n", " \"qqp\": 2,\n", " \"stsb\": 1,\n", " \"mnli\": 3,\n", " \"qnli\": 2,\n", " \"rte\": 2,\n", " \"wnli\": 2,\n", " \"ax\": 3,\n", " }\n", "\n", " loader_columns = [\n", " \"datasets_idx\",\n", " \"input_ids\",\n", " \"token_type_ids\",\n", " \"attention_mask\",\n", " \"start_positions\",\n", " \"end_positions\",\n", " \"labels\",\n", " ]\n", "\n", " def __init__(\n", " self,\n", " model_name_or_path: str,\n", " task_name: str = \"mrpc\",\n", " max_seq_length: int = 128,\n", " train_batch_size: int = 32,\n", " eval_batch_size: int = 32,\n", " **kwargs,\n", " ):\n", " super().__init__()\n", " self.model_name_or_path = model_name_or_path\n", " self.task_name = task_name\n", " self.max_seq_length = max_seq_length\n", " self.train_batch_size = train_batch_size\n", " self.eval_batch_size = eval_batch_size\n", "\n", " self.text_fields = self.task_text_field_map[task_name]\n", " self.num_labels = self.glue_task_num_labels[task_name]\n", " self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True)\n", "\n", " def setup(self, stage: str):\n", " self.dataset = datasets.load_dataset(\"glue\", self.task_name)\n", "\n", " for split in self.dataset.keys():\n", " self.dataset[split] = self.dataset[split].map(\n", " self.convert_to_features,\n", " batched=True,\n", " remove_columns=[\"label\"],\n", " )\n", " self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]\n", " self.dataset[split].set_format(type=\"torch\", columns=self.columns)\n", "\n", " self.eval_splits = [x for x in self.dataset.keys() if \"validation\" in x]\n", "\n", " def prepare_data(self):\n", " datasets.load_dataset(\"glue\", self.task_name)\n", " AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True)\n", "\n", " def train_dataloader(self):\n", " return DataLoader(self.dataset[\"train\"], batch_size=self.train_batch_size, shuffle=True)\n", "\n", " def val_dataloader(self):\n", " if len(self.eval_splits) == 1:\n", " return DataLoader(self.dataset[\"validation\"], batch_size=self.eval_batch_size)\n", " elif len(self.eval_splits) > 1:\n", " return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits]\n", "\n", " def test_dataloader(self):\n", " if len(self.eval_splits) == 1:\n", " return DataLoader(self.dataset[\"test\"], batch_size=self.eval_batch_size)\n", " elif len(self.eval_splits) > 1:\n", " return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits]\n", "\n", " def convert_to_features(self, example_batch, indices=None):\n", " # Either encode single sentence or sentence pairs\n", " if len(self.text_fields) > 1:\n", " texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]]))\n", " else:\n", " texts_or_text_pairs = example_batch[self.text_fields[0]]\n", "\n", " # Tokenize the text/text pairs\n", " features = self.tokenizer.batch_encode_plus(\n", " texts_or_text_pairs, max_length=self.max_seq_length, pad_to_max_length=True, truncation=True\n", " )\n", "\n", " # Rename label to labels to make it easier to pass to model forward\n", " features[\"labels\"] = example_batch[\"label\"]\n", "\n", " return features"]}, {"cell_type": "markdown", "id": "c06287a7", "metadata": {"papermill": {"duration": 0.002609, "end_time": "2023-01-03T14:51:44.662634", "exception": false, "start_time": "2023-01-03T14:51:44.660025", "status": "completed"}, "tags": []}, "source": ["**You could use this datamodule with standalone PyTorch if you wanted...**"]}, {"cell_type": "code", "execution_count": 4, "id": "b4256de2", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:51:44.712462Z", "iopub.status.busy": "2023-01-03T14:51:44.711837Z", "iopub.status.idle": "2023-01-03T14:51:55.189104Z", "shell.execute_reply": "2023-01-03T14:51:55.188429Z"}, "papermill": {"duration": 10.482616, "end_time": "2023-01-03T14:51:55.190708", "exception": false, "start_time": "2023-01-03T14:51:44.708092", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "d325b2fc8c95403d8d71990ed997a2a4", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading: 0%| | 0.00/28.0 [00:00 1:\n", " preds = torch.argmax(logits, axis=1)\n", " elif self.hparams.num_labels == 1:\n", " preds = logits.squeeze()\n", "\n", " labels = batch[\"labels\"]\n", "\n", " return {\"loss\": val_loss, \"preds\": preds, \"labels\": labels}\n", "\n", " def validation_epoch_end(self, outputs):\n", " if self.hparams.task_name == \"mnli\":\n", " for i, output in enumerate(outputs):\n", " # matched or mismatched\n", " split = self.hparams.eval_splits[i].split(\"_\")[-1]\n", " preds = torch.cat([x[\"preds\"] for x in output]).detach().cpu().numpy()\n", " labels = torch.cat([x[\"labels\"] for x in output]).detach().cpu().numpy()\n", " loss = torch.stack([x[\"loss\"] for x in output]).mean()\n", " self.log(f\"val_loss_{split}\", loss, prog_bar=True)\n", " split_metrics = {\n", " f\"{k}_{split}\": v for k, v in self.metric.compute(predictions=preds, references=labels).items()\n", " }\n", " self.log_dict(split_metrics, prog_bar=True)\n", " return loss\n", "\n", " preds = torch.cat([x[\"preds\"] for x in outputs]).detach().cpu().numpy()\n", " labels = torch.cat([x[\"labels\"] for x in outputs]).detach().cpu().numpy()\n", " loss = torch.stack([x[\"loss\"] for x in outputs]).mean()\n", " self.log(\"val_loss\", loss, prog_bar=True)\n", " self.log_dict(self.metric.compute(predictions=preds, references=labels), prog_bar=True)\n", "\n", " def configure_optimizers(self):\n", " \"\"\"Prepare optimizer and schedule (linear warmup and decay)\"\"\"\n", " model = self.model\n", " no_decay = [\"bias\", \"LayerNorm.weight\"]\n", " optimizer_grouped_parameters = [\n", " {\n", " \"params\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n", " \"weight_decay\": self.hparams.weight_decay,\n", " },\n", " {\n", " \"params\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],\n", " \"weight_decay\": 0.0,\n", " },\n", " ]\n", " optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)\n", "\n", " scheduler = get_linear_schedule_with_warmup(\n", " optimizer,\n", " num_warmup_steps=self.hparams.warmup_steps,\n", " num_training_steps=self.trainer.estimated_stepping_batches,\n", " )\n", " scheduler = {\"scheduler\": scheduler, \"interval\": \"step\", \"frequency\": 1}\n", " return [optimizer], [scheduler]"]}, {"cell_type": "markdown", "id": "e83da310", "metadata": {"papermill": {"duration": 0.048708, "end_time": "2023-01-03T14:51:55.469345", "exception": false, "start_time": "2023-01-03T14:51:55.420637", "status": "completed"}, "tags": []}, "source": ["## Training"]}, {"cell_type": "markdown", "id": "d53bb3f7", "metadata": {"papermill": {"duration": 0.047278, "end_time": "2023-01-03T14:51:55.564794", "exception": false, "start_time": "2023-01-03T14:51:55.517516", "status": "completed"}, "tags": []}, "source": ["### CoLA\n", "\n", "See an interactive view of the\n", "CoLA dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=cola)"]}, {"cell_type": "code", "execution_count": 6, "id": "8fcb7542", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:51:55.660556Z", "iopub.status.busy": "2023-01-03T14:51:55.660216Z", "iopub.status.idle": "2023-01-03T14:53:23.618469Z", "shell.execute_reply": "2023-01-03T14:53:23.617750Z"}, "papermill": {"duration": 88.008037, "end_time": "2023-01-03T14:53:23.620225", "exception": false, "start_time": "2023-01-03T14:51:55.612188", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 42\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "acdb7de94aee44a5b2ebe452e999f627", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading: 0%| | 0.00/684 [00:00\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n", "\u2503 Validate metric \u2503 DataLoader 0 \u2503 DataLoader 1 \u2503\n", "\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n", "\u2502 accuracy_matched \u2502 0.3181864619255066 \u2502 0.3181864619255066 \u2502\n", "\u2502 accuracy_mismatched \u2502 0.3182465434074402 \u2502 0.3182465434074402 \u2502\n", "\u2502 val_loss_matched \u2502 1.1081156730651855 \u2502 1.1081156730651855 \u2502\n", "\u2502 val_loss_mismatched \u2502 1.108543872833252 \u2502 1.108543872833252 \u2502\n", "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n", "\n"], "text/plain": ["\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n", "\u2503\u001b[1m \u001b[0m\u001b[1m Validate metric \u001b[0m\u001b[1m \u001b[0m\u2503\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m\u2503\u001b[1m \u001b[0m\u001b[1m DataLoader 1 \u001b[0m\u001b[1m \u001b[0m\u2503\n", "\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n", "\u2502\u001b[36m \u001b[0m\u001b[36m accuracy_matched \u001b[0m\u001b[36m \u001b[0m\u2502\u001b[35m \u001b[0m\u001b[35m 0.3181864619255066 \u001b[0m\u001b[35m \u001b[0m\u2502\u001b[35m \u001b[0m\u001b[35m 0.3181864619255066 \u001b[0m\u001b[35m \u001b[0m\u2502\n", "\u2502\u001b[36m \u001b[0m\u001b[36m accuracy_mismatched \u001b[0m\u001b[36m \u001b[0m\u2502\u001b[35m \u001b[0m\u001b[35m 0.3182465434074402 \u001b[0m\u001b[35m \u001b[0m\u2502\u001b[35m \u001b[0m\u001b[35m 0.3182465434074402 \u001b[0m\u001b[35m \u001b[0m\u2502\n", "\u2502\u001b[36m \u001b[0m\u001b[36m val_loss_matched \u001b[0m\u001b[36m \u001b[0m\u2502\u001b[35m \u001b[0m\u001b[35m 1.1081156730651855 \u001b[0m\u001b[35m \u001b[0m\u2502\u001b[35m \u001b[0m\u001b[35m 1.1081156730651855 \u001b[0m\u001b[35m \u001b[0m\u2502\n", "\u2502\u001b[36m \u001b[0m\u001b[36m val_loss_mismatched \u001b[0m\u001b[36m \u001b[0m\u2502\u001b[35m \u001b[0m\u001b[35m 1.108543872833252 \u001b[0m\u001b[35m \u001b[0m\u2502\u001b[35m \u001b[0m\u001b[35m 1.108543872833252 \u001b[0m\u001b[35m \u001b[0m\u2502\n", "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["[{'val_loss_matched': 1.1081156730651855,\n", " 'accuracy_matched': 0.3181864619255066,\n", " 'val_loss_mismatched': 1.108543872833252,\n", " 'accuracy_mismatched': 0.3182465434074402},\n", " {'val_loss_matched': 1.1081156730651855,\n", " 'accuracy_matched': 0.3181864619255066,\n", " 'val_loss_mismatched': 1.108543872833252,\n", " 'accuracy_mismatched': 0.3182465434074402}]"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}], "source": ["dm = GLUEDataModule(\n", " model_name_or_path=\"distilbert-base-cased\",\n", " task_name=\"mnli\",\n", ")\n", "dm.setup(\"fit\")\n", "model = GLUETransformer(\n", " model_name_or_path=\"distilbert-base-cased\",\n", " num_labels=dm.num_labels,\n", " eval_splits=dm.eval_splits,\n", " task_name=dm.task_name,\n", ")\n", "\n", "trainer = Trainer(\n", " max_epochs=3,\n", " accelerator=\"auto\",\n", " devices=1 if torch.cuda.is_available() else None, # limiting got iPython runs\n", ")\n", "trainer.validate(model, dm)"]}, {"cell_type": "markdown", "id": "2fdb84e0", "metadata": {"papermill": {"duration": 0.011608, "end_time": "2023-01-03T14:56:30.977196", "exception": false, "start_time": "2023-01-03T14:56:30.965588", "status": "completed"}, "tags": []}, "source": ["## Congratulations - Time to Join the Community!\n", "\n", "Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning\n", "movement, you can do so in the following ways!\n", "\n", "### Star [Lightning](https://github.com/Lightning-AI/lightning) on GitHub\n", "The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool\n", "tools we're building.\n", "\n", "### Join our [Slack](https://www.pytorchlightning.ai/community)!\n", "The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself\n", "and share your interests in `#general` channel\n", "\n", "\n", "### Contributions !\n", "The best way to contribute to our community is to become a code contributor! At any time you can go to\n", "[Lightning](https://github.com/Lightning-AI/lightning) or [Bolt](https://github.com/Lightning-AI/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/Lightning-AI/lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/Lightning-AI/lightning-bolts/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* You can also contribute your own notebooks with useful examples !\n", "\n", "### Great thanks from the entire Pytorch Lightning Team for your interest !\n", "\n", "[{height=\"60px\" width=\"240px\"}](https://pytorchlightning.ai)"]}, {"cell_type": "raw", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. customcarditem::\n", " :header: Finetune Transformers Models with PyTorch Lightning\n", " :card_description: This notebook will use HuggingFace's `datasets` library to get data, which will be wrapped in a `LightningDataModule`. Then, we write a class to perform text classification on...\n", " :tags: Text,GPU/TPU,Lightning-Examples"]}], "metadata": {"jupytext": {"cell_metadata_filter": "colab,colab_type,id,-all", "formats": "ipynb,py:percent", "main_language": "python"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16"}, "papermill": {"default_parameters": {}, "duration": 297.843688, "end_time": "2023-01-03T14:56:32.120376", "environment_variables": {}, "exception": null, "input_path": "lightning_examples/text-transformers/text-transformers.ipynb", "output_path": ".notebooks/lightning_examples/text-transformers.ipynb", "parameters": {}, "start_time": "2023-01-03T14:51:34.276688", "version": "2.4.0"}, "widgets": {"application/vnd.jupyter.widget-state+json": {"state": {"0018262054ce4e6d90532d369b636b36": {"model_module": 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