Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use DunnBC22/codebert-base-mlm-Malicious_URLs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/codebert-base-mlm-Malicious_URLs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/codebert-base-mlm-Malicious_URLs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/codebert-base-mlm-Malicious_URLs") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/codebert-base-mlm-Malicious_URLs") - Notebooks
- Google Colab
- Kaggle
update model card README.md
Browse files
README.md
CHANGED
|
@@ -1,6 +1,8 @@
|
|
| 1 |
---
|
| 2 |
tags:
|
| 3 |
- generated_from_trainer
|
|
|
|
|
|
|
| 4 |
model-index:
|
| 5 |
- name: codebert-base-mlm-Malicious_URLs
|
| 6 |
results: []
|
|
@@ -12,6 +14,18 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 12 |
# codebert-base-mlm-Malicious_URLs
|
| 13 |
|
| 14 |
This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on the None dataset.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 15 |
|
| 16 |
## Model description
|
| 17 |
|
|
|
|
| 1 |
---
|
| 2 |
tags:
|
| 3 |
- generated_from_trainer
|
| 4 |
+
metrics:
|
| 5 |
+
- accuracy
|
| 6 |
model-index:
|
| 7 |
- name: codebert-base-mlm-Malicious_URLs
|
| 8 |
results: []
|
|
|
|
| 14 |
# codebert-base-mlm-Malicious_URLs
|
| 15 |
|
| 16 |
This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on the None dataset.
|
| 17 |
+
It achieves the following results on the evaluation set:
|
| 18 |
+
- Loss: 0.7442
|
| 19 |
+
- Accuracy: 0.7322
|
| 20 |
+
- Weighted f1: 0.6538
|
| 21 |
+
- Micro f1: 0.7322
|
| 22 |
+
- Macro f1: 0.4303
|
| 23 |
+
- Weighted recall: 0.7322
|
| 24 |
+
- Micro recall: 0.7322
|
| 25 |
+
- Macro recall: 0.4233
|
| 26 |
+
- Weighted precision: 0.6314
|
| 27 |
+
- Micro precision: 0.7322
|
| 28 |
+
- Macro precision: 0.6034
|
| 29 |
|
| 30 |
## Model description
|
| 31 |
|