Update README.md

README.md

@@ -1,39 +1,63 @@
 ---
 tags:
-- generated_from_trainer
+- Question(s) Generation
 metrics:
 - rouge
 model-index:
-- name: t5-v1-base-s-q-c-multi-task-qgen
+- name: anshoomehra/question-generation-auto-hints-t5-v1-base-s-q-c
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Auto Question Generation  
+The model is intended to be used for Auto And/Or Hint enabled Question Generation tasks. The model is expected to produce one or possibly more than one question from the provided context.
+ 
+[Live Demo: Question Generation](https://huggingface.co/spaces/anshoomehra/question_generation)
 
-# t5-v1-base-s-q-c-multi-task-qgen
+Including this there are five models trained with different training sets, demo provide comparison to all in one go. However, you can reach individual projects at below links:
 
-This model is a fine-tuned version of [anshoomehra/t5-v1-base-s-q-multi-task-qgen](https://huggingface.co/anshoomehra/t5-v1-base-s-q-multi-task-qgen) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.5165
-- Rouge1: 0.5819
-- Rouge2: 0.4231
-- Rougel: 0.5487
-- Rougelsum: 0.5491
+[Auto Question Generation v1](https://huggingface.co/anshoomehra/question-generation-auto-t5-v1-base-s)
 
-## Model description
+[Auto Question Generation v2](https://huggingface.co/anshoomehra/question-generation-auto-t5-v1-base-s-q)
 
-More information needed
+[Auto Question Generation v3](https://huggingface.co/anshoomehra/question-generation-auto-t5-v1-base-s-q-c)
 
-## Intended uses & limitations
+[Auto/Hints based Question Generation v1](https://huggingface.co/anshoomehra/question-generation-auto-hints-t5-v1-base-s-q)
 
-More information needed
+This model can be used as below:
 
-## Training and evaluation data
+```
+from transformers import (
+    AutoModelForSeq2SeqLM,
+    AutoTokenizer
+)
+
+model_checkpoint = "anshoomehra/question-generation-auto-hints-t5-v1-base-s-q-c"
+
+model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+
+## Input with prompt
+context="question_context: <context>"
+encodings = tokenizer.encode(context, return_tensors='pt', truncation=True, padding='max_length').to(device)
 
-More information needed
+## You can play with many hyperparams to condition the output, look at demo
+output = model.generate(encodings, 
+                        #max_length=300, 
+                        #min_length=20, 
+                        #length_penalty=2.0, 
+                        num_beams=4,
+                        #early_stopping=True,
+                        #do_sample=True,
+                        #temperature=1.1
+                       )
+
+## Multiple questions are expected to be delimited by '?' You can write a small wrapper to elegantly format. Look at the demo.
+questions = [tokenizer.decode(id, clean_up_tokenization_spaces=False, skip_special_tokens=False) for id in output]
+```
+
+## Training and evaluation data
 
-## Training procedure
+Custom data.
 
 ### Training hyperparameters