pytorch t5 seq2seq dataset:squad transformers question-generation distilt5 distilt5-qg license:mit text2text-generation
.gitattributes README.md added_tokens.json config.json pytorch_model.bin special_tokens_map.json spiece.model tokenizer_config.json
---
datasets:
- squad
tags:
- question-generation
- distilt5
- distilt5-qg
widget:
- text: "<hl> 42 <hl> is the answer to life, the universe and everything. </s>"
- text: "Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>"
- text: "Although <hl> practicality <hl> beats purity </s>"
license: "MIT"
---

## DistilT5 for question-generation
This is distilled version of [t5-small-qa-qg-hl](https://huggingface.co/valhalla/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](https://github.com/huggingface/transformers/tree/master/examples/seq2seq#distilbart).

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](https://huggingface.co/valhalla/distilt5-qg-hl-6-4)        | 18.4141 | 24.8417 | 40.3435 | -      | -      |
| [distilt5-qa-qg-hl-6-4](https://huggingface.co/valhalla/distilt5-qa-qg-hl-6-4)  | 18.6493 | 24.9685 | 40.5605 | 76.13  | 84.659 |
| [distilt5-qg-hl-12-6](https://huggingface.co/valhalla/distilt5-qg-hl-12-6)      | 20.5275 | 26.5010 | 43.2676 | -      | -      |
| [distilt5-qa-qg-hl-12-6](https://huggingface.co/valhalla/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](https://github.com/patil-suraj/question_generation) repo.

### Model in action 🚀

You'll need to clone the [repo](https://github.com/patil-suraj/question_generation).

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/question_generation/blob/master/question_generation.ipynb)

```python3
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?'}]
```