DunnBC22 commited on
Commit
64fbe00
1 Parent(s): 0c21dc8

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +11 -8
README.md CHANGED
@@ -3,17 +3,20 @@ tags:
3
  - generated_from_trainer
4
  metrics:
5
  - accuracy
 
 
 
6
  model-index:
7
  - name: codebert-base-Malicious_URLs
8
  results: []
 
 
 
9
  ---
10
 
11
- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
- should probably proofread and complete it, then remove this comment. -->
13
-
14
  # codebert-base-Malicious_URLs
15
 
16
- This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
  - Loss: 0.8225
19
  - Accuracy: 0.7279
@@ -29,15 +32,15 @@ It achieves the following results on the evaluation set:
29
 
30
  ## Model description
31
 
32
- More information needed
33
 
34
  ## Intended uses & limitations
35
 
36
- More information needed
37
 
38
  ## Training and evaluation data
39
 
40
- More information needed
41
 
42
  ## Training procedure
43
 
@@ -64,4 +67,4 @@ The following hyperparameters were used during training:
64
  - Transformers 4.27.4
65
  - Pytorch 2.0.0
66
  - Datasets 2.11.0
67
- - Tokenizers 0.13.3
 
3
  - generated_from_trainer
4
  metrics:
5
  - accuracy
6
+ - f1
7
+ - recall
8
+ - precision
9
  model-index:
10
  - name: codebert-base-Malicious_URLs
11
  results: []
12
+ language:
13
+ - en
14
+ pipeline_tag: text-classification
15
  ---
16
 
 
 
 
17
  # codebert-base-Malicious_URLs
18
 
19
+ This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base).
20
  It achieves the following results on the evaluation set:
21
  - Loss: 0.8225
22
  - Accuracy: 0.7279
 
32
 
33
  ## Model description
34
 
35
+ For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Multiclass%20Classification/Malicious%20URLs/Malicious%20URLs%20-%20CodeBERT.ipynb
36
 
37
  ## Intended uses & limitations
38
 
39
+ This model is intended to demonstrate my ability to solve a complex problem using technology.
40
 
41
  ## Training and evaluation data
42
 
43
+ Dataset Source: https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset
44
 
45
  ## Training procedure
46
 
 
67
  - Transformers 4.27.4
68
  - Pytorch 2.0.0
69
  - Datasets 2.11.0
70
+ - Tokenizers 0.13.3