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We set per_device_train_batch_size=2 and per_device_eval_batch_size=2 because of the GPU constraints.
#Finetune gpt2 code
In transformers/example/language-modeling/run-language-modelling.py, we should append the following code for the model before training: special_tokens_dict = num_added_toks = tokenizer.add_special_tokens(special_tokens_dict) model.resize_token_embeddings(len(tokenizer))Īfter running this code, the special tokens will be added to the tokenizer and the model will resize its embedding to fit with the modified tokenizer.įor training, we define some parameters first and then run the language modeling script: cd transformers/example/language-modeling N=gpu_num OUTPUT_DIR=/path/to/model TRAIN_FILE=/path/to/dataset/train.txt VALID_FILE=/path/to/dataset/valid.txt CUDA_VISIBLE_DEVICES=$N python run_language_modeling.py \ -output_dir=$OUTPUT_DIR \ -model_type=gpt2 \ -model_name_or_path=gpt2 \ -do_train \ -train_data_file=$TRAIN_FILE \ -do_eval \ -eval_data_file=$VALID_FILE \ -per_device_train_batch_size=2 \ -per_device_eval_batch_size=2 \ -line_by_line \ -evaluate_during_training \ -learning_rate 5e-5 \ -num_train_epochs=5 We should also set the pad token because we will be using LineByLineDataset, which will essentially treat each line in the dataset as distinct examples.
#Finetune gpt2 install
If you want to see visualizations of your model and hyperparameters during training, you can also choose to install tensorboard or wandb: pip install tensorboard pip install wandb wandb login Step 3: Fine-tune GPT2īefore training, we should set the bos token and eos token as defined earlier in our datasets. Install Huggingface library: pip install transformersĬlone Huggingface repo: git clone /huggingface/transformers To build and train GPT2, we need to install the Huggingface library, as well as its repository. You can get the preprocessing notebook here. txt files, getting train.txt, valid.txt, test.txt. We add a bos token to the start of each summary and eos token to the end of each summary for later training purposes. Here is what the dataset looks like:įor data preprocessing, we first split the entire dataset into the train, validation, and test datasets with the train-valid-test ratio: 70–20–10.
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We are using The CMU Books Summary Dataset, which contains 16,559 books extracted from Wikipedia along with the metadata including title, author, publication date, genres, and plot summary.
#Finetune gpt2 download
Step 1: Prepare Datasetīefore building the model, we need to download and preprocess the dataset first. The entire codebase for this article can be viewed here. We will be using the Huggingface repository for building our model and generating the texts.
#Finetune gpt2 how to
Here in today’s article, we will dive deeply into how to implement another popular transformer, GPT2, to write interesting and creative stories! Specifically, we will test the ability of GPT2 to write creative book summaries using the CMU Books Summary Dataset. GPT2 is really useful for language generation tasks as it is an autoregressive language model. It results in competitive performance on multiple language tasks using only the pre-trained knowledge without explicitly training on them.
![finetune gpt2 finetune gpt2](https://img-blog.csdnimg.cn/img_convert/e8e0e249d943d21cdc62c51769e99d99.png)
Developed by OpenAI, GPT2 is a large-scale transformer-based language model that is pre-trained on a large corpus of text: 8 million high-quality webpages. If you haven’t read my previous article on BERT for text classification, go ahead and take a look! Another popular transformer that we will talk about today is GPT2. This is mainly due to one of the most important breakthroughs of NLP in the modern decade - Transformers. The past few years have been especially booming in the world of NLP.