create model cards for qg models (#5610)

model_cards/valhalla/t5-base-e2e-qg/README.md

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+---
+datasets:
+- squad
+tags:
+- question-generation
+widget:
+- text: "Python is a programming language. It is developed by Guido Van Rossum and released in 1991. </s>"
+license: "MIT"
+---
+
+## T5 for question-generation
+This is [t5-base](https://arxiv.org/abs/1910.10683) model trained for end-to-end question generation task. Simply input the text and the model will generate multile questions. 
+
+You can play with the model using the inference API, just put the text and see the results!
+
+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
+
+text = "Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum \
+and first released in 1991, Python's design philosophy emphasizes code \
+readability with its notable use of significant whitespace."
+
+nlp = pipeline("e2e-qg", model="valhalla/t5-base-e2e-qg")
+nlp(text)
+=> [
+ 'Who created Python?',
+ 'When was Python first released?',
+ "What is Python's design philosophy?"
+]
+```

model_cards/valhalla/t5-base-qa-qg-hl/README.md

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+---
+datasets:
+- squad
+tags:
+- question-generation
+widget:
+- text: "generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>"
+- text: "question: What is 42 context: 42 is the answer to life, the universe and everything. </s>"
+license: "MIT"
+---
+
+## T5 for multi-task QA and QG
+This is multi-task [t5-base](https://arxiv.org/abs/1910.10683) model trained for question answering and answer aware question generation tasks. 
+
+For question generation the answer spans are highlighted within the text with special highlight tokens (`<hl>`) and prefixed with 'generate question: '. For QA the input is processed like this `question: question_text context: context_text </s>` 
+
+You can play with the model using the inference API. Here's how you can use it
+
+For QG
+
+`generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>`
+
+For QA
+
+`question: What is 42 context: 42 is the answer to life, the universe and everything. </s>`
+
+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("multitask-qa-qg", model="valhalla/t5-base-qa-qg-hl")
+
+# to generate questions simply pass the text
+nlp("42 is the answer to life, the universe and everything.")
+=> [{'answer': '42', 'question': 'What is the answer to life, the universe and everything?'}]
+
+# for qa pass a dict with "question" and "context"
+nlp({
+    "question": "What is 42 ?",
+    "context": "42 is the answer to life, the universe and everything."
+})
+=> 'the answer to life, the universe and everything'
+```

model_cards/valhalla/t5-base-qg-hl/README.md

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+---
+datasets:
+- squad
+tags:
+- question-generation
+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"
+---
+
+## T5 for question-generation
+This is [t5-base](https://arxiv.org/abs/1910.10683) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. 
+
+You can play with the model using the inference API, just highlight the answer spans with `<hl>` tokens and end the text with `</s>`. For example
+
+`<hl> 42 <hl> is the answer to life, the universe and everything. </s>`
+
+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/t5-base-qg-hl")
+nlp("42 is the answer to life, universe and everything.")
+=> [{'answer': '42', 'question': 'What is the answer to life, universe and everything?'}]
+```

model_cards/valhalla/t5-samll-qg-prepend/README.md

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+---
+datasets:
+- squad
+tags:
+- question-generation
+widget:
+- text: "answer: 42  context: 42 is the answer to life, the universe and everything. </s>"
+- text: "answer: Guido Van Rossum context: Python is a programming language. It is developed by Guido Van Rossum. </s>"
+- text: "answer: Explicit context: Explicit is better than implicit </s>"
+license: "MIT"
+---
+
+## T5 for question-generation
+This is [t5-small](https://arxiv.org/abs/1910.10683) model trained for answer aware question generation task. The answer text is prepended before the context text. 
+
+You can play with the model using the inference API, just get the input text in this format and see the results!
+`answer: answer_text context: context_text </s>`
+
+For example
+
+`answer: 42  context: 42 is the answer to life, the universe and everything. </s>`
+
+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", qg_format="prepend")
+nlp("42 is the answer to life, universe and everything.")
+=> [{'answer': '42', 'question': 'What is the answer to life, universe and everything?'}]
+```

model_cards/valhalla/t5-small-e2e-qg/README.md

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+---
+datasets:
+- squad
+tags:
+- question-generation
+widget:
+- text: "Python is developed by Guido Van Rossum and released in 1991. </s>"
+license: "MIT"
+---
+
+## T5 for question-generation
+This is [t5-small](https://arxiv.org/abs/1910.10683) model trained for end-to-end question generation task. Simply input the text and the model will generate multile questions. 
+
+You can play with the model using the inference API, just put the text and see the results!
+
+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
+
+text = "Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum \
+and first released in 1991, Python's design philosophy emphasizes code \
+readability with its notable use of significant whitespace."
+
+nlp = pipeline("e2e-qg")
+nlp(text)
+=> [
+ 'Who created Python?',
+ 'When was Python first released?',
+ "What is Python's design philosophy?"
+]
+```

model_cards/valhalla/t5-small-qa-qg-hl/README.md

@@ -0,0 +1,49 @@
+---
+datasets:
+- squad
+tags:
+- question-generation
+widget:
+- text: "generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>"
+- text: "question: What is 42 context: 42 is the answer to life, the universe and everything. </s>"
+license: "MIT"
+---
+
+## T5 for multi-task QA and QG
+This is multi-task [t5-small](https://arxiv.org/abs/1910.10683) model trained for question answering and answer aware question generation tasks. 
+
+For question generation the answer spans are highlighted within the text with special highlight tokens (`<hl>`) and prefixed with 'generate question: '. For QA the input is processed like this `question: question_text context: context_text </s>` 
+
+You can play with the model using the inference API. Here's how you can use it
+
+For QG
+
+`generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>`
+
+For QA
+
+`question: What is 42 context: 42 is the answer to life, the universe and everything. </s>`
+
+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("multitask-qa-qg")
+
+# to generate questions simply pass the text
+nlp("42 is the answer to life, the universe and everything.")
+=> [{'answer': '42', 'question': 'What is the answer to life, the universe and everything?'}]
+
+# for qa pass a dict with "question" and "context"
+nlp({
+    "question": "What is 42 ?",
+    "context": "42 is the answer to life, the universe and everything."
+})
+=> 'the answer to life, the universe and everything'
+```

model_cards/valhalla/t5-small-qg-hl/README.md

@@ -0,0 +1,33 @@
+---
+datasets:
+- squad
+tags:
+- question-generation
+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: "Simple is better than <hl> complex <hl>. </s>"
+license: "MIT"
+---
+
+## T5 for question-generation
+This is [t5-small](https://arxiv.org/abs/1910.10683) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. 
+
+You can play with the model using the inference API, just highlight the answer spans with `<hl>` tokens and end the text with `</s>`. For example
+
+`<hl> 42 <hl> is the answer to life, the universe and everything. </s>`
+
+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")
+nlp("42 is the answer to life, universe and everything.")
+=> [{'answer': '42', 'question': 'What is the answer to life, universe and everything?'}]
+```