Update README.md

README.md

@@ -4,43 +4,53 @@ tags:
 metrics:
 - rouge
 model-index:
-- name: t5-v1-base-s-q-c-multi-task-qgen-v2
+- name: question-answering-generative-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. -->
-
-# t5-v1-base-s-q-c-multi-task-qgen-v2
-
-This model is a fine-tuned version of [anshoomehra/t5-v1-base-s-q-c-multi-task-qgen](https://huggingface.co/anshoomehra/t5-v1-base-s-q-c-multi-task-qgen) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.6751
-- Rouge1: 0.8028
-- Rouge2: 0.5168
-- Rougel: 0.8022
-- Rougelsum: 0.8022
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+# Question Answering Generative
+The model is intended to be used for Q&A task, given the question & context, the model would attempt to infer the answer text.<br>
+Model is generative (t5-v1-base), fine-tuned from [question-generation-auto-hints-t5-v1-base-s-q-c](https://huggingface.co/anshoomehra/question-generation-auto-hints-t5-v1-base-s-q-c) with - **Loss:** 0.6751 & **Rougel:** 0.8022 performance scores.
+
+Please follow this link for [Encoder based Question Answering](https://huggingface.co/anshoomehra/question-answering-roberta-base-s/blob/main/README.md)
+
+Example code:
+
+```
+from transformers import (
+    AutoModelForSeq2SeqLM,
+    AutoTokenizer
+)
+
+def _generate(query, context, model, device):
+    
+    FT_MODEL = AutoModelForSeq2SeqLM.from_pretrained(model).to(device)
+    FT_MODEL_TOKENIZER = AutoTokenizer.from_pretrained(model)
+    input_text = "question: " + query + "</s> question_context: " + context
+    
+    input_tokenized = FT_MODEL_TOKENIZER.encode(input_text, return_tensors='pt', truncation=True, padding='max_length', max_length=1024).to(device)
+    _tok_count_assessment = FT_MODEL_TOKENIZER.encode(input_text, return_tensors='pt', truncation=True).to(device)
+
+    summary_ids = FT_MODEL.generate(input_tokenized, 
+                                       max_length=30, 
+                                       min_length=5, 
+                                       num_beams=2,
+                                       early_stopping=True,
+                                   )
+    output = [FT_MODEL_TOKENIZER.decode(id, clean_up_tokenization_spaces=True, skip_special_tokens=True) for id in summary_ids] 
+    
+    return str(output[0])
+
+device = [0 if torch.cuda.is_available() else 'cpu'][0]
+_generate(query, context, model="anshoomehra/t5-v1-base-s-q-c-multi-task-qgen-v2", device=device)   
+```
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0003
-- train_batch_size: 6
-- eval_batch_size: 6
+- train_batch_size: 3
+- eval_batch_size: 3
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear