Update README.md

README.md

@@ -35,17 +35,23 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Binary%20Classification/Fake%20Reviews/Fake%20Reviews%20Classification%20-%20BERT-Large%20With%20PEFT.ipynb
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril.
 
 ## Training and evaluation data
 
 Dataset Source: https://www.kaggle.com/datasets/razamukhtar007/fake-reviews
 
+__Histogram of Word Counts of Reviews__
 
+![Histogram of Word Counts of Reviews](https://raw.githubusercontent.com/DunnBC22/NLP_Projects/main/Binary%20Classification/Fake%20Reviews/Images/Histogram%20of%20Review%20Word%20Counts.png)
+
+__Class Distribution__
+
+![Class Distribution](https://raw.githubusercontent.com/DunnBC22/NLP_Projects/main/Binary%20Classification/Fake%20Reviews/Images/Class%20Distribution.png)
 
 ## Training procedure
 
@@ -64,11 +70,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
-| 0.633         | 1.0   | 10438 | 0.5608          | 0.8261   | 0.7914      | 0.8261   | 0.5745   | 0.8261          | 0.8261       | 0.5643       | 0.7844             | 0.8261          | 0.6542          |
-| 0.6029        | 2.0   | 20876 | 0.6490          | 0.8331   | 0.7724      | 0.8331   | 0.5060   | 0.8331          | 0.8331       | 0.5239       | 0.7892             | 0.8331          | 0.6929          |
-| 0.5478        | 3.0   | 31314 | 0.5508          | 0.8305   | 0.8071      | 0.8305   | 0.6189   | 0.8305          | 0.8305       | 0.6003       | 0.8002             | 0.8305          | 0.6784          |
-| 0.513         | 4.0   | 41752 | 0.5459          | 0.8347   | 0.8101      | 0.8347   | 0.6224   | 0.8347          | 0.8347       | 0.6023       | 0.8049             | 0.8347          | 0.6916          |
-| 0.5288        | 5.0   | 52190 | 0.5336          | 0.8381   | 0.8142      | 0.8381   | 0.6308   | 0.8381          | 0.8381       | 0.6090       | 0.8101             | 0.8381          | 0.7029          |
+| 0.633         | 1.0   | 10438 | 0.5608          | 0.8261   | 0.7914      | 0.8261   | __0.5745__ | 0.8261          | 0.8261       | 0.5643       | 0.7844             | 0.8261          | 0.6542          |
+| 0.6029        | 2.0   | 20876 | 0.6490          | 0.8331   | 0.7724      | 0.8331   | __0.5060__ | 0.8331          | 0.8331       | 0.5239       | 0.7892             | 0.8331          | 0.6929          |
+| 0.5478        | 3.0   | 31314 | 0.5508          | 0.8305   | 0.8071      | 0.8305   | __0.6189__ | 0.8305          | 0.8305       | 0.6003       | 0.8002             | 0.8305          | 0.6784          |
+| 0.513         | 4.0   | 41752 | 0.5459          | 0.8347   | 0.8101      | 0.8347   | __0.6224__ | 0.8347          | 0.8347       | 0.6023       | 0.8049             | 0.8347          | 0.6916          |
+| 0.5288        | 5.0   | 52190 | 0.5336          | 0.8381   | 0.8142      | 0.8381   | __0.6308__ | 0.8381          | 0.8381       | 0.6090       | 0.8101             | 0.8381          | 0.7029          |
 
 
 ### Framework versions