# ^^ safe to call this function even if cuda is not availableĪs mentioned earlier, we'll be using the BERT model. Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if Next, let's make a function to set seed so we'll have the same results in different runs: def set_seed(seed: int): Open up a new notebook/Python file and import the necessary modules: import torchįrom transformers.file_utils import is_tf_available, is_torch_available, is_torch_tpu_availableįrom transformers import BertTokenizerFast, BertForSequenceClassificationįrom transformers import Trainer, TrainingArgumentsįrom sklearn.datasets import fetch_20newsgroupsįrom sklearn.model_selection import train_test_split
#Finetune bert install#
To get started, let's install Huggingface transformers library along with others: pip3 install transformers numpy torch sklearn For example, I've implemented this tutorial on fake news detection, and it works great. If you have a custom dataset for classification, you can follow along as well, as you should make very few changes. We'll be using 20 newsgroups dataset as a demo for this tutorial it is a dataset that has about 18,000 news posts on 20 different topics. If you want to train BERT from scratch, that's called pre-training this tutorial will definitely help you. Please note that this tutorial is about fine-tuning the BERT model on a downstream task (such as text classification). In this tutorial, we will take you through an example of fine-tuning BERT (and other transformer models) for text classification using the Huggingface Transformers library on the dataset of your choice. One of the most significant milestones in the evolution of NLP is the release of Google's BERT model in late 2018, which is known as the beginning of a new era in NLP. The power of transfer learning combined with large-scale transformer language models has become a standard in state-of-the-art NLP. Transformer models have been showing incredible results in most of the tasks in the natural language processing field.
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