Update README.md

README.md

@@ -35,8 +35,8 @@ In training i use 70000 radiology reports to train the model to summarize radiol
 
 ### Model Sources 
 - **Repository:** [GanjinZero/biobart-v2-base]
-- **Paper [optional]:** [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model]
-- **Demo [optional]:** [hamzamalik11/radiology_summarizer]
+- **Paper:** [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model]
+- **Demo:** [hamzamalik11/radiology_summarizer]
 
 ## Uses
 
@@ -77,9 +77,9 @@ output= summarizer("heart size normal mediastinal hilar contours remain stable s
 ## Training Details
 
 ### Training Data
-Data Source: The training data was a custom dataset of 70,000 radiology reports. 
-Data Cleaning: The data was cleaned to remove any personal or confidential information. The data was also tokenized and normalized.
-Data Split: The training data was split into a training set and a validation set. The training set consisted of 63,000 radiology reports, and the validation set consisted of 7,000 radiology reports.
+-Data Source: The training data was a custom dataset of 70,000 radiology reports. 
+-Data Cleaning: The data was cleaned to remove any personal or confidential information. The data was also tokenized and normalized.
+-Data Split: The training data was split into a training set and a validation set. The training set consisted of 63,000 radiology reports, and the validation set consisted of 7,000 radiology reports.
 
 
 
@@ -91,23 +91,21 @@ The model was trained using the Hugging Face Transformers library: https://huggi
 #### Training Hyperparameters
 
 - **Training regime:**
-- evaluation_strategy="epoch",
-    learning_rate=5.6e-5,
-    per_device_train_batch_size=batch_size //4,
-    per_device_eval_batch_size=batch_size //4,
-    weight_decay=0.01,
-    save_total_limit=3,
-    num_train_epochs=num_train_epochs,
-    predict_with_generate=True,
-    logging_steps=logging_steps,
-    push_to_hub=False,
+-evaluation_strategy="epoch",
+-learning_rate=5.6e-5,
+-per_device_train_batch_size=batch_size //4,
+-per_device_eval_batch_size=batch_size //4,
+-weight_decay=0.01,
+-save_total_limit=3,
+-num_train_epochs=num_train_epochs,
+-predict_with_generate=True,
+-logging_steps=logging_steps,
+-push_to_hub=False,
 
 
 
 ## Evaluation
 
-
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
@@ -115,17 +113,17 @@ The testing data consisted of 10,000 radiology reports.
 
 #### Factors
 The following factors were evaluated:
-ROUGE-1
-ROUGE-2
-ROUGE-L
-ROUGELSUM
+-ROUGE-1
+-ROUGE-2
+-ROUGE-L
+-ROUGELSUM
 
 #### Metrics
 The following metrics were used to evaluate the model:
-ROUGE-1 score: 44.857
-ROUGE-2 score: 29.015
-ROUGE-L score: 42.032
-ROUGELSUM score: 42.038
+-ROUGE-1 score: 44.857
+-ROUGE-2 score: 29.015
+-ROUGE-L score: 42.032
+-ROUGELSUM score: 42.038
 
 
 ### Results
@@ -138,13 +136,13 @@ The model was trained on a custom dataset of 70,000 radiology reports. The model
 
 
 ## Model Card Authors 
-Name: Engr. Hamza Iqbal Malik  
-LinkedIn: [www.linkedin.com/in/hamza-iqbal-malik-42366a239](www.linkedin.com/in/hamza-iqbal-malik-42366a239)  
-GitHub: [https://github.com/hamza4344](https://github.com/hamza4344)
+-Name: Engr. Hamza Iqbal Malik  
+-LinkedIn: [www.linkedin.com/in/hamza-iqbal-malik-42366a239](www.linkedin.com/in/hamza-iqbal-malik-42366a239)  
+-GitHub: [https://github.com/hamza4344](https://github.com/hamza4344)
 
 ## Model Card Contact
-Name: Engr. Hamza Iqbal Malik  
-LinkedIn: [www.linkedin.com/in/hamza-iqbal-malik-42366a239](www.linkedin.com/in/hamza-iqbal-malik-42366a239)  
-GitHub: [https://github.com/hamza4344](https://github.com/hamza4344)
+-Name: Engr. Hamza Iqbal Malik  
+-LinkedIn: [www.linkedin.com/in/hamza-iqbal-malik-42366a239](www.linkedin.com/in/hamza-iqbal-malik-42366a239)  
+-GitHub: [https://github.com/hamza4344](https://github.com/hamza4344)