Update README.md

README.md

@@ -3,43 +3,50 @@ license: apache-2.0
 base_model: albert-base-v2
 tags:
 - generated_from_trainer
+- URL
+- Security
 metrics:
 - accuracy
+- recall
+- precision
+- f1
 model-index:
 - name: albert-base-v2-Malicious_URLs
   results: []
+pipeline_tag: text-classification
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # albert-base-v2-Malicious_URLs
 
-This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset.
+This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2).
+
 It achieves the following results on the evaluation set:
 - Loss: 0.8368
 - Accuracy: 0.7267
-- Weighted f1: 0.6482
-- Micro f1: 0.7267
-- Macro f1: 0.4521
-- Weighted recall: 0.7267
-- Micro recall: 0.7267
-- Macro recall: 0.4294
-- Weighted precision: 0.6262
-- Micro precision: 0.7267
-- Macro precision: 0.5508
+- F1:
+  - Weighted: 0.6482
+  - Micro: 0.7267
+  - Macro: 0.4521
+- Recall
+  - Weighted: 0.7267
+  - Micro: 0.7267
+  - Macro: 0.4294
+- Precision
+  - Weighted: 0.6262
+  - Micro: 0.7267
+  - Macro: 0.5508
 
 ## Model description
 
-More information needed
+For more information on how it was created, check out the following link: 
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to demonstrate my ability to solve a complex problem using technology.
 
 ## Training and evaluation data
 
-More information needed
+Dataset Source: https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset
 
 ## Training procedure
 
@@ -56,14 +63,13 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| 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 |
+| 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 |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
 | 0.7839        | 1.0   | 51087 | 0.8368          | 0.7267   | 0.6482      | 0.7267   | 0.4521   | 0.7267          | 0.7267       | 0.4294       | 0.6262             | 0.7267          | 0.5508          |
 
-
 ### Framework versions
 
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.4
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3