You can pass a run_name parameter to finetune and load_gpt2 if you want to store/load multiple models in a checkpoint folder. This guide will show you how to: Finetune DistilBERT on the SQuAD dataset for extractive question answering. Abstractive: generate an answer from the context that correctly answers the question. generate ( sess, return_as_list = True ) print ( single_text ) There are two common types of question answering tasks: Extractive: extract the answer from the given context. an API or a bot) by using the return_as_list parameter. GPT2 is well known for its capabilities to generate text. generate ( sess )Īs with textgenrnn, you can generate and save text for later use (e.g. If you want to load a model from that folder and generate text from it: import gpt_2_simple as gpt2 sess = gpt2. The generated model checkpoints are by default in /checkpoint/run1. Graphcore partner, Pienso, delivers a machine learning platform, based on IPU-powered NLP models, to help enterprises understand text data better than ever before. finetune ( sess, 'shakespeare.txt', steps = 1000 ) # steps is max number of training steps gpt2. Intelligent Text Analytics for accurate insights. download_gpt2 () # model is saved into current directory under /models/124M/ sess = gpt2. txt file which is then used to finetune the GPT-2 model. Warning: the pretrained model, and thus any finetuned model, is 500 MB! import gpt_2_simple as gpt2 gpt2. Pocket-sized hallucination on demand You can now run a GPT-3-level AI model on your laptop, phone, and Raspberry Pi Thanks to Meta LLaMA, AI text models may have their 'Stable Diffusion moment. Since the dataset is massive, we use Ray to override the python GIL and leverage all CPU cores to expedite the process. UsageĪn example for downloading the model to the local system, fineturning it on a dataset. Additionally, this package allows easier generation of text, generating to a file for easy curation, allowing for prefixes to force the text to start with a given phrase. The generated text is then classified using a discriminative model like BERT or T5, which is trained on labeled data to predict the relevance of the generated text to the specific task. A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI GPT-2 text generation model (specifically the "small", 124M hyperparameter version).
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