Added model

README.md

@@ -1,3 +1,50 @@
 ---
+datasets:
+- squad
+tags:
+- question-answer-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'
+```

added_tokens.json

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+{"<sep>": 32100, "<hl>": 32101}

config.json

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+{
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 32,
+      "num_beams": 4,
+      "prefix": ""
+    }
+  },
+  "vocab_size": 32102
+}

pytorch_model.bin

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+size 891612585

special_tokens_map.json

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+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

spiece.model

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+size 791656

tokenizer_config.json

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+{"model_max_length": 512, "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

training_args.bin

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+size 1087