DistilT5 for question-generation
This is distilled version of t5-small-qa-qg-hl model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
The model is distilled using the No Teacher Distillation method proposed by Huggingface, here.
We just copy alternating layers from t5-small-qa-qg-hl and finetune more on the same data. Following table lists other distilled models and their metrics.
| Name | BLEU-4 | METEOR | ROUGE-L | QA-EM | QA-F1 |
|---|---|---|---|---|---|
| distilt5-qg-hl-6-4 | 18.4141 | 24.8417 | 40.3435 | - | - |
| distilt5-qa-qg-hl-6-4 | 18.6493 | 24.9685 | 40.5605 | 76.13 | 84.659 |
| distilt5-qg-hl-12-6 | 20.5275 | 26.5010 | 43.2676 | - | - |
| distilt5-qa-qg-hl-12-6 | 20.6109 | 26.4533 | 43.0895 | 81.61 | 89.831 |
You can play with the model using the inference API, just highlight the answer spans with <hl> tokens. For example
<hl> 42 <hl> is the answer to life, the universe and everything.
For more deatils see this repo.
Model in action 🚀
You'll need to clone the repo.
from pipelines import pipeline
nlp = pipeline("question-generation", model="valhalla/distilt5-qg-hl-6-4")
nlp("42 is the answer to life, universe and everything.")
=> [{'answer': '42', 'question': 'What is the answer to life?'}]