Fine-tuned pre-trained GPT2 for topic specific text generation. Such system can be used for Text Augmentation.
- git clone https://github.com/prakhar21/TextAugmentation-GPT2.git
- Move your data to data/ dir.
* Please refer to data/SMSSpamCollection to get the idea of file format.
- Assuming are done with Point 2 under Getting Started
2. Run python3 train.py --data_file <filename> --epoch <number_of_epochs> --warmup <warmup_steps> --model_name <model_name> --max_len <max_seq_length> --learning_rate <learning_rate> --batch <batch_size>
1. python3 generate.py --model_name <model_name> --sentences <number_of_sentences> --label <class_of_training_data>
* It is recommended that you tune the parameters for your task. Not doing so may result in choosing default parameters and eventually giving sub-optimal performace.
I had fine-tuned the model on SPAM/HAM dataset. You can download it from here and follow the steps mentioned under Generation Text section.
Sample Results
SPAM: You have 2 new messages. Please call 08719121161 now. £3.50. Limited time offer. Call 090516284580.<|endoftext|>
SPAM: Want to buy a car or just a drink? This week only 800p/text betta...<|endoftext|>
SPAM: FREE Call Todays top players, the No1 players and their opponents and get their opinions on www.todaysplay.co.uk Todays Top Club players are in the draw for a chance to be awarded the £1000 prize. TodaysClub.com<|endoftext|>
SPAM: you have been awarded a £2000 cash prize. call 090663644177 or call 090530663647<|endoftext|>
HAM: Do you remember me?<|endoftext|>
HAM: I don't think so. You got anything else?<|endoftext|>
HAM: Ugh I don't want to go to school.. Cuz I can't go to exam..<|endoftext|>
HAM: K.,k:)where is my laptop?<|endoftext|>
- Top-k and Top-p Sampling (Variant of Nucleus Sampling) has been used while decoding the sequence word-by-word. You can read more about it here
Note: First time you run, it will take considerable amount of time because of the following reasons -
- Downloads pre-trained gpt2-medium model (Depends on your Network Speed)
- Fine-tunes the gpt2 with your dataset (Depends on size of the data, Epochs, Hyperparameters, etc)
All the experiments were done on IntelDevCloud Machines