Update README.md

README.md

@@ -268,40 +268,56 @@ should probably proofread and complete it, then remove this comment. -->
 
 # FLAN-T5-NLP-Paper-to-Question-Generation
 
-This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.4504
+This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an [allenai/QASPER: a dataset for question answering on scientific research papers ](https://huggingface.co/datasets/allenai/qasper)-based [NLP-Paper-to-QA-Generation](https://huggingface.co/datasets/UNIST-Eunchan/NLP-Paper-to-QA-Generation) dataset.
 
-## Model description
 
-More information needed
 
-## Intended uses & limitations
+## How to Use ( Code Snippets )
 
-More information needed
 
-## Training and evaluation data
+### # Load model directly 
+```(python)
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-More information needed
+tokenizer = AutoTokenizer.from_pretrained("UNIST-Eunchan/FLAN-T5-NLP-Paper-to-Question-Generation")
+model = AutoModelForSeq2SeqLM.from_pretrained("UNIST-Eunchan/FLAN-T5-NLP-Paper-to-Question-Generation")
+```
 
-## Training procedure
+### Prompting Input
+```(python)
+txt = r""" 
+Generate Question, Answer pair correspond to the following research paper. 
+[Abstract] + {text['abstract']} + [Introduction] + {text['introduction']}
+Question, Answer:
+""".replace("\n", "")
 
-### Training hyperparameters
+inputs = tokenizer(txt, max_length = 1024, truncation=True, padding="max_length", return_tensors="pt")
+```
+
+### For Multiple Question Generation (👍)
+```(python)
+summaries = model.generate(input_ids =inputs["input_ids"], max_new_tokens=100, do_sample = True, top_p = 0.95, num_return_sequences = 4)
+```
+### For Single Question Generation 
+```(python)
+summaries = model.generate(input_ids =inputs["input_ids"], max_new_tokens=100, do_sample = True, top_p = 0.95)
+```
+
+
+
+```
+decoded_summaries = [tokenizer.decode(s, skip_special_tokens=False, clean_up_tokenization_spaces=True) for s in summaries]
+decoded_summaries = [d.replace("<n>", " ").replace(tokenizer.pad_token, "").replace(tokenizer.eos_token, "") for d in decoded_summaries]
+
+```
 
-The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 1
-- eval_batch_size: 1
-- seed: 42
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 16
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 184
-- num_epochs: 10
 
 ### Training results
 
+
+It achieves the following results on the evaluation set:
+- Loss: 0.4504
+
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
 | No log | 0.99 | 46 | 34.6109 |
@@ -315,10 +331,31 @@ The following hyperparameters were used during training:
 | 0.4811 | 8.94 | 414 | 0.4505 |
 | 0.4721 | 9.93 | 460 | 0.4504 |
 
+## Model description
+
+- FLAN-T5-Large (770M) 
+
+## Intended uses & limitations
+
+- NLP Paper's Abstract + Introduction --> {Question} [SEP] {Answer}
+
+
+## Training and evaluation data
+- Used Dataset: [UNIST-Eunchan/NLP-Paper-to-QA-Generation](https://huggingface.co/datasets/UNIST-Eunchan/NLP-Paper-to-QA-Generation) dataset.
+- Train: dataset['train'] + dataset['test']
+- Evaluation: dataset['validation'] 
+ 
+### Training hyperparameters
 
-### Framework versions
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 184
+- num_epochs: 10
 
-- Transformers 4.35.2
-- Pytorch 2.1.0+cu118
-- Datasets 2.15.0
-- Tokenizers 0.15.0