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:
```
print('Tokenizing...')
inputs = tokenizer(graphs, return_tensors="pt", padding=True, truncation=True)
print('Predicting...')
y_hats = model.generate(inputs.input_ids)
print('Decoding...')
preds = tokenizer.batch_decode(y_hats, skip_special_tokens=True, clean_up_tokenization_spaces=True)
```

Good luck!

[Associated research report](https://dspace.uba.uva.nl/server/api/core/bitstreams/fee95174-b7d4-4cd8-8545-f7ec8ab29e2d/content)
{"language": ["en"], "license": "openrail", "library_name": "transformers", "tags": ["knowledge_graphs", "question_generation"], "datasets": ["web_questions"], "metrics": ["bleu", "bertscore"]}
transformers pytorch safetensors t5 text2text-generation knowledge_graphs question_generation en dataset:web_questions license:openrail autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #knowledge_graphs #question_generation #en #dataset-web_questions #license-openrail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

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:



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


Good luck!

Associated research report
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #knowledge_graphs #question_generation #en #dataset-web_questions #license-openrail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

passage: TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #knowledge_graphs #question_generation #en #dataset-web_questions #license-openrail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
