---
license: apache-2.0
base_model: facebook/bart-base
datasets:
- squad_v2
- drop
language:
- en
library_name: peft
tags:
- General purpose
- Text2text Generation
metrics:
- bertscore
- accuracy
- rouge
---
# Model Card 

  
  Base Model: facebook/bart-base
  
  Fine-tuned : using PEFT-LoRa 
  
  Datasets : squad_v2, drop
  
  Task: Generating questions from context and answers 
  
  Language: English 


# Loading the model 


  ```python
    from peft import PeftModel, PeftConfig
    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
    HUGGING_FACE_USER_NAME = "mou3az"
    model_name = "Question-Generation"
    peft_model_id = f"{HUGGING_FACE_USER_NAME}/{model_name}"
    config = PeftConfig.from_pretrained(peft_model_id)
    model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, device_map='auto')
    QG_tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
    QG_model = PeftModel.from_pretrained(model, peft_model_id)
  ```


# At inference time


  ```python
    def get_question(context, answer):
        device = next(QG_model.parameters()).device
        input_text = f"Given the context '{context}' and the answer '{answer}', what question can be asked?"
        encoding = QG_tokenizer.encode_plus(input_text, padding=True, return_tensors="pt").to(device)
    
        output_tokens = QG_model.generate(**encoding, early_stopping=True, num_beams=5, num_return_sequences=1, no_repeat_ngram_size=2, max_length=100)
        out = QG_tokenizer.decode(output_tokens[0], skip_special_tokens=True).replace("question:", "").strip()
    
        return out
  ```


# Training parameters and hyperparameters


  The following were used during training:

  For Lora:

  r=18

  alpha=8


  For training arguments:

  gradient_accumulation_steps=16
  
  per_device_train_batch_size=8
  
  per_device_eval_batch_size=8
  
  max_steps=3000
  
  warmup_steps=75
  
  weight_decay=0.05
  
  learning_rate=1e-3
  
  lr_scheduler_type="linear"

# Performance Metrics on Evaluation Set:

    for 3000 optimization steps:
    
    Training Loss: 1.292400
    
    Evaluation Loss: 1.244928  
  
    Bertscore: 0.8123
  
    Rouge: 0.532144
  
    Fuzzywizzy similarity: 0.74209