DunnBC22
/

codebert-base-Malicious_URLs

Text Classification

Generated from Trainer

Inference Endpoints

Model card Files Files and versions Metrics Training metrics Community

codebert-base-Malicious_URLs / README.md

DunnBC22's picture

Update README.md

1221284 about 1 year ago

|

raw history blame contribute delete

No virus

2.8 kB

	---
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	- f1
	- recall
	- precision
	model-index:
	- name: codebert-base-Malicious_URLs
	results: []
	language:
	- en
	pipeline_tag: text-classification
	---

	# codebert-base-Malicious_URLs

	This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base).
	It achieves the following results on the evaluation set:
	- Loss: 0.8225
	- Accuracy: 0.7279
	- Weighted f1: 0.6508
	- Micro f1: 0.7279
	- Macro f1: 0.4611
	- Weighted recall: 0.7279
	- Micro recall: 0.7279
	- Macro recall: 0.4422
	- Weighted precision: 0.6256
	- Micro precision: 0.7279
	- Macro precision: 0.5436

	## Model description

	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

	## Intended uses & limitations

	This model is intended to demonstrate my ability to solve a complex problem using technology.

	## Training and evaluation data

	Dataset Source: https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset

	_Input Word Length:_

	![Length of Input Text (in Words)](https://github.com/DunnBC22/NLP_Projects/raw/main/Multiclass%20Classification/Malicious%20URLs/Images/Context%20Word%20Length.png)

	_Input Word Length By Class:_

	![Length of Input Text (in Words) By Class](https://github.com/DunnBC22/NLP_Projects/raw/main/Multiclass%20Classification/Malicious%20URLs/Images/Context%20Word%20Length%20By%20Class.png)

	_Class Distribution:_

	![Length of Input Text (in Words)](https://github.com/DunnBC22/NLP_Projects/raw/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/Images/Class%20Distribution.png)

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 2e-05
	- train_batch_size: 64
	- eval_batch_size: 64
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 1

	### 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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-----------:\|:--------:\|:--------:\|:---------------:\|:------------:\|:------------:\|:------------------:\|:---------------:\|:---------------:\|
	\| 0.8273 \| 1.0 \| 6450 \| 0.8225 \| 0.7279 \| 0.6508 \| 0.7279 \| 0.4611 \| 0.7279 \| 0.7279 \| 0.4422 \| 0.6256 \| 0.7279 \| 0.5436 \|

	### Framework versions

	- Transformers 4.27.4
	- Pytorch 2.0.0
	- Datasets 2.11.0
	- Tokenizers 0.13.3