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A library to synthesize text datasets using Large Language Models (LLM). Mutate reads through the examples in the dataset andgenerates similar examples using auto generated few shot prompts.
pip install mutate-nlpor
pip install git+https://github.com/infinitylogesh/mutate
frommutateimportpipelinepipe=pipeline("text-classification-synthesis",model="EleutherAI/gpt-neo-2.7B",device=1)task_desc="Each item in the following contains movie reviews and corresponding sentiments. Possible sentimets are neg and pos"# returns a python generatortext_synth_gen=pipe("csv",data_files=["local/path/sentiment_classfication.csv"],task_desc=task_desc,text_column="text",label_column="label",text_column_alias="Comment",label_column_alias="sentiment",shot_count=5,class_names=["pos","neg"])#Loop through the generator to synthesize examples by classforsynthesized_examplesintext_synth_gen:print(synthesized_examples)
Show Output
{"text": ["The story was very dull and was a waste of my time. This was not a film I would ever watch. The acting was bad. I was bored. There were no surprises. They showed one dinosaur,","I did not like this film. It was a slow and boring film, it didn't seem to have any plot, there was nothing to it. The only good part was the ending, I just felt that the film should have ended more abruptly."]"label":["neg","neg"]}{"text":["The Bell witch is one of the most interesting, yet disturbing films of recent years. It’s an odd and unique look at a very real, but very dark issue. With its mixture of horror, fantasy and fantasy adventure, this film is as much a horror film as a fantasy film. And it‘s worth your time. While the movie has its flaws, it is worth watching and if you are a fan of a good fantasy or horror story, you will not be disappointed."],"label":["pos"]}# and so on .....Under the hood Mutate uses the wonderful 🤗 datasets library for dataset processing, So it supports 🤗 datasets out of the box.
frommutateimportpipelinepipe=pipeline("text-classification-synthesis",model="EleutherAI/gpt-neo-2.7B",device=1)task_desc="Each item in the following contains customer service queries expressing the mentioned intent"synthesizerGen=pipe("banking77",task_desc=task_desc,text_column="text",label_column="label",# if the `text_column` doesn't have a meaningful valuetext_column_alias="Queries",label_column_alias="Intent",# if the `label_column` doesn't have a meaningful valueshot_count=5,dataset_args=["en"])forexpinsynthesizerGen:print(exp)
Show Output
{"text":["How can i know if my account has been activated? (This is the one that I am confused about)","Thanks! My card activated"],"label":["activate_my_card","activate_my_card"]}{"text": ["How do i activate this new one? Is it possible?","what is the activation process for this card?"],"label":["activate_my_card","activate_my_card"]}# and so on .....Caution: Infinetly looping through the dataset has a higher chance of duplicate examples to be generated.
frommutateimportpipelinepipe=pipeline("text-classification-synthesis",model="EleutherAI/gpt-neo-2.7B",device=1)task_desc="Each item in the following contains movie reviews and corresponding sentiments. Possible sentimets are neg and pos"# returns a python generatortext_synth_gen=pipe("csv",data_files=["local/path/sentiment_classfication.csv"],task_desc=task_desc,text_column="text",label_column="label",text_column_alias="Comment",label_column_alias="sentiment",class_names=["pos","neg"],# Flag to generate indefinite examplesinfinite_loop=True)#Infinite loopforexpinsynthesizerGen:print(exp)
- Text classification dataset synthesis : Few Shot text data synsthesize for text classification datasets using Causal LLMs ( GPT like )
- Other types of text Dataset synthesis - NER , sentence pairs etc
- Finetuning support for better quality generation
- Pseudo labelling
- EleutherAI for democratizing Large LMs.
- This library uses 🤗Datasets and 🤗Transformers for processing datasets and models.
The Idea of generating examples from Large Language Model is inspired by the works below,
- A Few More Examples May Be Worth Billions of Parameters by Yuval Kirstain, Patrick Lewis, Sebastian Riedel, Omer Levy
- GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation by Kang Min Yoo, Dongju Park, Jaewook Kang, Sang-Woo Lee, Woomyeong Park
- Data Augmentation using Pre-trained Transformer Models by Varun Kumar, Ashutosh Choudhary, Eunah Cho