Update README.md

README.md

@@ -1,88 +1,88 @@
 ### Model Card ###
 
-  # Information:
+    # Information:
+    
+    Base Model: facebook/bart-base
+    
+    Fine-tuned : using PEFT-LoRa 
+    
+    Datasets : squad_v2, drop, mou3az/IT_QA-QG 
+    
+    Task: Generating questions from context and answers 
+    
+    Language: English 
   
-  Base Model: facebook/bart-base
   
-  Fine-tuned : using PEFT-LoRa 
+    # Performance Metrics on Evaluation Set:
   
-  Datasets : squad_v2, drop, mou3az/IT_QA-QG 
-  
-  Task: Generating questions from context and answers 
-  
-  Language: English 
-
-
-  # Performance Metrics on Evaluation Set:
-
-  Training Loss: 1.1.1958
-  
-  Evaluation Loss: 1.109059
+    Training Loss: 1.1.1958
+    
+    Evaluation Loss: 1.109059
 
 
 ### Loading the model ###
 
-  ```python
-  from peft import PeftModel, PeftConfig
-  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-  HUGGING_FACE_USER_NAME = "mou3az"
-  model_name = "ITandGeneral_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)
-  ```
+    ```python
+    from peft import PeftModel, PeftConfig
+    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+    HUGGING_FACE_USER_NAME = "mou3az"
+    model_name = "ITandGeneral_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(L_model.parameters()).device
-      input_text = f"Given the context '{context}' and the answer '{answer}', what question can be asked?"
-      encoding = G_tokenizer.encode_plus(input_text, padding=True, return_tensors="pt").to(device)
-  
-      output_tokens = L_model.generate(**encoding, early_stopping=True, num_beams=5, num_return_sequences=1, no_repeat_ngram_size=2, max_length=100)
-      out = G_tokenizer.decode(output_tokens[0], skip_special_tokens=True).replace("question:", "").strip()
-  
-      return out
-  ```
+    ```python
+    def get_question(context, answer):
+        device = next(L_model.parameters()).device
+        input_text = f"Given the context '{context}' and the answer '{answer}', what question can be asked?"
+        encoding = G_tokenizer.encode_plus(input_text, padding=True, return_tensors="pt").to(device)
+    
+        output_tokens = L_model.generate(**encoding, early_stopping=True, num_beams=5, num_return_sequences=1, no_repeat_ngram_size=2, max_length=100)
+        out = G_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=24
-  
-  per_device_train_batch_size=8
+    The following were used during training:
   
-  per_device_eval_batch_size=8
+    # For Lora:
   
-  max_steps=1000
+    r=18
   
-  warmup_steps=50
-  
-  weight_decay=0.05
-  
-  learning_rate=3e-3
+    alpha=8
+
+
+    # For training arguments:
   
-  lr_scheduler_type="linear"
+    gradient_accumulation_steps=24
+    
+    per_device_train_batch_size=8
+    
+    per_device_eval_batch_size=8
+    
+    max_steps=1000
+    
+    warmup_steps=50
+    
+    weight_decay=0.05
+    
+    learning_rate=3e-3
+    
+    lr_scheduler_type="linear"
 
 ### Training Results ###
 
-  | Epoch | Training Loss | Validation Loss |
-  |-------|---------------|-----------------|
-  | 0.0   | 4.6426        | 4.704238        |
-  | 3.0   | 1.5094        | 1.202135        |
-  | 6.0   | 1.2677        | 1.146177        |
-  | 9.0   | 1.2613        | 1.112074        |
-  | 12.0  | 1.1958        | 1.109059        |
+    | Epoch | Training Loss | Validation Loss |
+    |-------|---------------|-----------------|
+    | 0.0   | 4.6426        | 4.704238        |
+    | 3.0   | 1.5094        | 1.202135        |
+    | 6.0   | 1.2677        | 1.146177        |
+    | 9.0   | 1.2613        | 1.112074        |
+    | 12.0  | 1.1958        | 1.109059        |