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T5-base model fine-tuned for question generation from knowledge graphs. Can be used to generate questions from linearized knowledge graphs, meaning graphs in the form of its all its triples listed in the following format:

<A> answer node(s) <H> head <R> relation <T> tail <H> head <R> relation <T> tail ... etc ..., where answer node(s) refers to the node(s) which should contain the answer to the generated question.

To load the model:

from transformers import T5ForConditionalGeneration, T5TokenizerFast
model = T5ForConditionalGeneration.from_pretrained('stanlochten/t5-KGQgen')
tokenizer = T5TokenizerFast.from_pretrained('t5-base',  extra_ids=0, 
            additional_special_tokens = ['<A>', '<H>', '<R>', '<T>'])

To generate questions from your graphs, where graphs is a list of strings for each graph:

inputs = tokenizer(graphs, return_tensors="pt", padding=True, truncation=True)
y_hats = model.generate(inputs.input_ids)
preds = tokenizer.batch_decode(y_hats, skip_special_tokens=True, clean_up_tokenization_spaces=True)

Good luck!

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Model size
223M params
Tensor type
Hosted inference API
This model can be loaded on the Inference API on-demand.

Dataset used to train stanlochten/t5-KGQgen